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医用画像処理向け深層学習 (ディープラーニング) の世界市場:2020-2030年

Deep Learning Market: Focus on Medical Image Processing, 2020-2030

出版日: | 発行: Roots Analysis | ページ情報: 英文 344 Pages | 納期: 即日から翌営業日

価格
価格表記: USDを日本円(税抜)に換算
本日の銀行送金レート: 1USD=104.68円
医用画像処理向け深層学習 (ディープラーニング) の世界市場:2020-2030年
出版日: 2020年08月31日
発行: Roots Analysis
ページ情報: 英文 344 Pages
納期: 即日から翌営業日
  • 全表示
  • 概要
  • 図表
  • 目次
概要

深層学習 (ディープラーニング) は、画像ベースの医療診断やデータ処理など、医療部門全体のさまざまな用途で徐々に導入が始まっています。特に医用画像では、X 線、コンピュータ断層撮影、磁気共鳴画像、陽電子放射断層撮影など、様々な診断画像の情報処理や結果解釈の自動化に深層学習を利用できる可能性があります。今後はより多くの機械学習ベースのソリューションが承認される見通しであり、市場は大幅な成長が見込まれています。

当レポートでは、世界の医用画像処理向け深層学習 (ディープラーニング) の市場を調査し、 医療における深層学習の各種用途、医療画像向け深層学習に対する各社の提携・協力動向、資金調達・投資動向、臨床試験の状況、コスト削減の可能性に関する分析、診療区分・画像技術・地域別の市場規模の推移・予測、新型コロナウイルス感染症 (COVID-19) およびその他の市場影響因子の分析、専門家による見解などをまとめています。

第1章 序文

第2章 エグゼクティブサマリー

第3章 イントロダクション

  • 人間・機械・インテリジェンス
  • ラーニングの化学
  • AI
  • ビッグデータ革命
  • 医療における深層学習の応用
    • 個別化医療
    • パーソナルフィットネス・ライフスタイル管理
    • 創薬
    • 臨床試験管理
    • 医用画像処理

第4章 ケーススタディ:IBM WATSON vs GOOGLE DEEPMIND

第5章 市場概要

  • 総市場の市場情勢
  • 主要な特性に関する情報
  • 企業リスト

第6章 企業プロファイル

  • Artelus
  • Arterys
  • Butterfly Network
  • ContextVision
  • Enlitic
  • Echonous
  • GE Healthcare
  • InferVision
  • VUNO
    • 企業概要
    • 製品・技術ポートフォリオ
    • 近年の展開・将来の展望

第7章 提携・協力

  • 提携年別
  • 提携タイプ別
  • 提携年別・提携タイプ別
  • 提携者タイプ別
  • 治療領域別
  • もっともアクティブな事業者:提携数別
  • 地域分析
  • 大陸間・大陸内協定

第8章 資金調達・投資分析

  • 資金調達件数別
  • 投資額別
  • 資金調達タイプ別
  • もっともアクティブな事業者:資金調達件数・投資額別
  • もっともアクティブな投資家:資金調達件数別
  • 地域分析:投資額別

第9章 企業評価

第10章 ケーススタディ:米国で登録された深層学習ベースの臨床試験

  • 登録年別
  • 登録年・採用状況別
  • 登録年・患者登録別
  • 設計別
  • 患者区分別
  • 治療領域別
  • 試験目的別
  • 重点分野別
  • 処理画像タイプ別
  • もっともアクティブな事業者:臨床試験数別
  • 臨床試験数・地域別
  • 登録患者数・地域別

第11章 特許分析

  • 出願年・発行年別
  • 発行機関/特許庁別
  • IPCRシンボル別
  • 新たな焦点領域
  • 主要な譲受人:特許数別
  • ベンチマーキング分析

第12章 コスト削減の分析

  • X線画像
  • MRI画像
  • CT画像
  • 超音波画像
    • コスト削減の可能性:地域別
    • コスト削減の可能性:経済力別

第13章 市場予測

  • 総市場
  • 診療区分別
    • 脳の異常/神経障害
    • 心臓異常/心血管障害
    • 乳癌
    • 骨変形/整形外科疾患
    • 肺感染症/肺障害
    • その他
  • 技術別
    • X線
    • MRI
    • CT
    • 超音波
  • 地域別
    • 北米
    • 欧州
    • アジア太平洋・その他の地域

第14章 専門家による考察

第15章 インタビュースクリプト

  • Advenio Technosys
  • Arterys
  • Arya.ai
  • AlgoSurg
  • ContextVision

第16章 COVID-19の影響

第17章 総論

第18章 付録1:集計データ

第19章 付録2:企業・組織リスト

図表

List Of Tables

  • Table 3.1 Machine Learning: A Brief History
  • Table 4.1 IBM: Artificial Intelligence Focused Acquisitions
  • Table 4.2 Google: Artificial Intelligence Focused Acquisitions
  • Table 4.3 IBM Watson: Partnerships and Collaborations in Healthcare
  • Table 4.4 Google DeepMind: Partnerships and Collaborations in Healthcare
  • Table 5.1 Deep Learning in Medical Image Processing Solutions: Information on Status of Development and Regulatory Approvals
  • Table 5.2 Deep Learning in Medical Image Processing Solutions: Information on Type of Offering and Type of Image Processed
  • Table 5.3 Deep Learning in Medical Image Processing Solutions: Information on Anatomical Region and Application Area
  • Table 5.4 Deep Learning in Medical Image Processing Solutions: Information on Key Characteristics of Solutions and Affiliated Technologies
  • Table 5.5 Deep Learning in Medical Image Processing (List of Companies): Information on Year of Establishment, Company Size, Location of Headquarters, Type of Deployment Model, Number of Solutions
  • Table 6.1 List of Companies Profiled
  • Table 6.2 Artelus: Company Overview
  • Table 6.3 Artelus: Information on Medical Image Processing Solutions
  • Table 6.4 Artelus: Recent Developments and Future Outlook
  • Table 6.5 Arterys: Company Overview
  • Table 6.6 Arterys: Information on Medical Image Processing Solutions
  • Table 6.7 Arterys: Recent Developments and Future Outlook
  • Table 6.8 Butterfly Network: Company Overview
  • Table 6.9 Butterfly Network: Information on Medical Image Processing Solutions
  • Table 6.10 Butterfly Network: Recent Developments and Future Outlook
  • Table 6.11 ContextVision: Company Overview
  • Table 6.12 ContextVision: Information on Medical Image Processing Solutions
  • Table 6.13 ContextVision: Recent Developments and Future Outlook
  • Table 6.14 Enlitic: Company Overview
  • Table 6.15 Enlitic: Information on Medical Image Processing Solutions
  • Table 6.16 Enlitic: Recent Developments and Future Outlook
  • Table 6.17 Echonous: Company Overview
  • Table 6.18 Echonous: Information on Medical Image Processing Solutions
  • Table 6.19 Echonous: Recent Developments and Future Outlook
  • Table 6.20 GE Healthcare: Company Overview
  • Table 6.21 GE Healthcare: Information on Medical Image Processing Solutions
  • Table 6.22 GE Healthcare: Recent Developments and Future Outlook
  • Table 6.23 InferVision: Company Overview
  • Table 6.24 InferVision: Information on Medical Image Processing Solutions
  • Table 6.25 InferVision: Recent Developments and Future Outlook
  • Table 6.26 VUNO: Company Overview
  • Table 6.27 VUNO: Information on Medical Image Processing Solutions
  • Table 6.28 VUNO: Recent Developments and Future Outlook
  • Table 7.1 Deep Learning in Medical Image Processing: List of Partnerships and Collaborations, till June 2020
  • Table 8.1 Deep Learning in Medical Image Processing: List of Funding and Investments, till June 2020
  • Table 9.1 Company Valuation Analysis: Sample Dataset
  • Table 9.2 Company Valuation Analysis: Weighted Average Valuation
  • Table 9.3 Company Valuation Analysis: Estimated Valuation
  • Table 9.4 Company Valuation Analysis: Distribution by Specialization
  • Table 11.1 Deep Learning in Medical Image Processing, Patent Portfolio: IPCR Classification Symbol Definitions
  • Table 11.2 Deep Learning in Medical Image Processing, Patent Portfolio: Most Popular IPCR Classification Symbols
  • Table 11.3 Deep Learning in Medical Image Processing, Patent Portfolio: List of Top IPCR Classification Symbols
  • Table 11.4 Deep Learning in Medical Image Processing, Patent Portfolio: Summary of Benchmarking Analysis
  • Table 11.5 Deep Learning in Medical Image Processing, Patent Portfolio: Categorizations based on Weighted Valuation Scores
  • Table 11.6 Deep Learning in Medical Image Processing, Patent Portfolio: List of Leading Patents (by Highest Relative Valuation)
  • Table 12.1 Cost Saving Analysis: Information on Number of Radiologists in Various Countries
  • Table 12.2 Cost Saving Analysis: Information on Yearly Count of X-Ray Scans across Different Geographical Regions, 2020 (Million Scans)
  • Table 12.3 Cost Saving Analysis: Information on Yearly Count of Ultrasound Scans across Different Geographical Regions, 2020 (Million Scans)
  • Table 12.4 Cost Saving Analysis: Information on Yearly Count of MRI Scans across Different Geographical Regions, 2020 (Million Scans)
  • Table 12.5 Cost Saving Analysis: Information on Yearly Count of CT Scans across Different Geographical Regions, 2020 (Million Scans)
  • Table 13.1 Deep Learning in Medical Image Processing Solutions: Information on Adoption by Radiologists Across Different Geographical Regions
  • Table 15.1 Advenio Technosys: Company Snapshot
  • Table 15.2 Arterys: Company Snapshot
  • Table 15.3 Arya.ai: Company Snapshot
  • Table 15.4 AlgoSurg: Company Snapshot
  • Table 15.5 Context Vision: Company Snapshot
  • Table 18.1 IBM: Annual Revenues, 2016 - Q1 2020 (USD Billion)
  • Table 18.2 Alphabet: Annual Revenues, 2016 - Q1 2020 (USD Billion)
  • Table 18.3 Deep Learning in Medical Image Processing: Distribution by Status of Development
  • Table 18.4 Deep Learning in Medical Image Processing: Distribution by Regulatory Approvals Received
  • Table 18.5 Deep Learning in Medical Image Processing: Distribution by Type of Offering
  • Table 18.6 Deep Learning in Medical Image Processing: Distribution by Type of Image Processed
  • Table 18.7 Deep Learning in Medical Image Processing: Distribution by Anatomical Region
  • Table 18.8 Deep Learning in Medical Image Processing: Distribution by Application Area
  • Table 18.9 Deep Learning in Medical Image Processing Solution Developers: Distribution by Year of Establishment
  • Table 18.10 Deep Learning in Medical Image Processing Solution Developers: Distribution by Company Size
  • Table. 18.11 Deep Learning in Medical Image Processing Solution Developers: Distribution by Location of Headquarters
  • Table 18.12 Deep Learning in Medical Image Processing Solution Developers: Distribution by Type of Deployment Model
  • Figure 18.13 Leading Deep Learning in Medical Image Processing Solution Developers: Distribution by Number of Solutions
  • Table 18.14 Partnerships and Collaborations: Distribution by Year of Partnership
  • Table 18.15 Partnerships and Collaborations: Distribution by Type of Partnership
  • Table 18.16 Partnerships and Collaborations: Distribution by Year and Type of Partnership
  • Table 18.17 Partnerships and Collaborations: Distribution by Type of Partner
  • Table 18.18 Partnerships and Collaborations: Distribution by Therapeutic Area
  • Table 18.19 Most Active Players: Distribution by Number of Partnerships
  • Table 18.20 Partnerships and Collaborations: Intercontinental and Intracontinental Agreements
  • Table 18.21 Funding and Investments: Distribution of Instances by Year of Establishment of Companies and Type of Funding, 2016 - H1 2020
  • Table 18.22 Funding and Investments: Cumulative Year-wise Trend, 2016 - H1 2020
  • Table 18.23 Funding and Investments: Distribution by Number of Funding Instances and Amount Invested, 2016-H1 2020
  • Table 18.24 Funding and Investments: Distribution by Type of Funding
  • Table 18.25 Funding and Investments: Distribution by Type of Funding and Total Amount Invested (USD Million)
  • Table 18.26 Most Active Players: Distribution by Number of Funding Instances and Amount of Funding (USD Million)
  • Table 18.27 Most Active Companies: Summary of Funding Raised by Type of Funding and Amount of Funding (USD Million)
  • Table 18.28 Most Active Investors: Distribution by Number of Funding Instances
  • Table 18.29 Clinical Trial Analysis: Distribution by Trial Recruitment Status
  • Table 18.30 Clinical Trial Analysis: Cumulative Distribution by Trial Registration Year, Pre-2016 - Q1 2020
  • Table 18.31 Clinical Trial Analysis: Distribution by Trial Recruitment Status and Trial Registration Year
  • Table 18.32 Clinical Trial Analysis: Distribution by Trial Registration Year and Patient Enrollment, 2007-Q1 2020
  • Table 18.33 Clinical Trial Analysis: Distribution by Study Design
  • Table 18.34 Clinical Trial Analysis: Distribution by Patient Segment
  • Table 18.35 Clinical Trial Analysis: Distribution by Therapeutic Area
  • Table 18.36 Clinical Trial Analysis: Distribution by Trial Objective
  • Table 18.37 Clinical Trial Analysis: Distribution by Type of Image Processed
  • Table 18.39 Clinical Trial Analysis: Distribution by Type of Sponsors / Collaborators
  • Table 18.40 Leading Sponsors / Collaborators: Analysis by Number of Trials
  • Table 18.41 Deep Learning in Medical Image Processing, Patent Portfolio: Distribution by Type of Patent
  • Table 18.42 Deep Learning in Medical Image Processing, Patent Portfolio: Distribution by Application Year and Publication Year
  • Table 18.43 Deep Learning in Medical Image Processing, Patent Portfolio: Distribution by Issuing Authority / Patent Offices Involved
  • Table 18.44 Deep Learning in Medical Image Processing, Patent Portfolio: North America
  • Table 18.45 Deep Learning in Medical Image Processing, Patent Portfolio: Europe
  • Table 18.46 Deep Learning in Medical Image Processing, Patent Portfolio: Asia Pacific and RoW
  • Table 18.47 Deep Learning in Medical Image Processing, Patent Portfolio: Leading Assignees (Industry Players)
  • Table 18.48 Deep Learning in Medical Image Processing, Patent Portfolio: Leading Assignees (Non-Industry Players)
  • Table 18.49 Deep Learning in Medical Image Processing, Patent Portfolio: Distribution by Patent Age
  • Table 18.50 Deep Learning in Medical Image Processing, Patent Portfolio: Valuation Analysis
  • Table 18.51 Deep Learning in Medical Image Processing: Efficiency Profile of Radiologists
  • Table 18.52 Overall Cost Saving Potential of Deep Learning in Medical Image Processing Solutions: Growth Scenarios
  • Table 18.53 Overall Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (X-Ray Images), 2020-2030 (USD Billion)
  • Table 18.54 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (X-Ray Images) in North America, 2020-2030 (USD Billion)
  • Table 18.55 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (X-Ray Images) in Europe, 2020-2030 (USD Billion)
  • Table 18.56 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (X-Ray Images) in Asia Pacific and RoW, 2020-2030 (USD Billion)
  • Table 18.57 Cost Saving Potential of Deep Learning-based Medical Image Processing Solutions (X-Ray Images) in High Income Countries, 2020-2030 (USD Billion)
  • Table 18.58 Cost Saving Potential of Deep Learning-based Medical Image Processing Solutions (X-Ray Images) in Middle Income Countries, 2020-2030 (USD Billion)
  • Table 18.59 Overall Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (MRI Images), 2020-2030 (USD Billion)
  • Table 18.60 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (MRI Images) in North America, 2020-2030 (USD Billion)
  • Table 18.61 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (MRI Images) in Europe, 2020-2030 (USD Billion)
  • Table 18.62 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (MRI Images) in Asia Pacific and RoW, 2020-2030 (USD Billion)
  • Table 18.63 Cost Saving Potential of Deep Learning-based Medical Image Processing Solutions (MRI Images) in High Income Countries, 2020-2030 (USD Billion)
  • Table 18.64 Cost Saving Potential of Deep Learning-based Medical Image Processing Solutions (MRI Images) in Middle Income Countries, 2020-2030 (USD Billion)
  • Table 18.65 Overall Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (CT Images), 2020-2030 (USD Billion)
  • Table 18.66 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (CT Images) in North America, 2020-2030 (USD Billion)
  • Table 18.67 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (CT Images) in Europe, 2020-2030 (USD Billion)
  • Table 18.68 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (CT Images) in Asia Pacific and RoW, 2020-2030 (USD Billion)
  • Table 18.69 Cost Saving Potential of Deep Learning-based Medical Image Processing Solutions (CT Images) in High Income Countries, 2020-2030 (USD Billion)
  • Table 18.70 Cost Saving Potential of Deep Learning-based Medical Image Processing Solutions (CT Images) in Middle Income Countries, 2020-2030 (USD Billion)
  • Table 18.71 Overall Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (Ultrasound Images), 2020-2030 (USD Billion)
  • Table 18.72 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (Ultrasound Images) in North America, 2020-2030 (USD Billion)
  • Table 18.73 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (Ultrasound Images) in Europe, 2020-2030 (USD Billion)
  • Table 18.74 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (Ultrasound Images) in Asia Pacific and RoW, 2020-2030 (USD Billion)
  • Table 18.75 Cost Saving Potential of Deep Learning-based Medical Image Processing Solutions (Ultrasound Images) in High Income Countries, 2020-2030 (USD Billion)
  • Table 18.76 Cost Saving Potential of Deep Learning-based Medical Image Processing Solutions (Ultrasound Images) in Middle Income Countries, 2020-2030 (USD Billion)
  • Table 18.77 Overall Deep Learning in Medical Image Processing Market, Conservative, Base and Optimistic Scenarios, 2020-2030 (USD Million)
  • Table 18.78 Deep Learning in Medical Image Processing Market: Distribution by Application Area, Conservative, Base and Optimistic Scenarios, 2020-2030 (USD Million)
  • Table 18.79 Deep Learning in Medical Image Processing Market for Brain Abnormalities / Neurological Disorders, Conservative, Base and Optimistic Scenarios, 2020-2030 (USD Million)
  • Table 18.80 Deep Learning in Medical Image Processing Market for Cardiac Abnormalities / Cardiovascular Disorders, Conservative, Base and Optimistic Scenarios, 2020-2030 (USD Million)
  • Table 18.81 Deep Learning in Medical Image Processing Market for Breast Cancer, Conservative, Base and Optimistic Scenarios, 2020-2030 (USD Million)
  • Table 18.82 Deep Learning in Medical Image Processing Market for Bone Deformities / Orthopedic Disorders, Conservative, Base and Optimistic Scenarios, 2020-2030 (USD Million)
  • Table 18.83 Deep Learning in Medical Image Processing Market for Lung Infections / Lung Disorders, Conservative, Base and Optimistic Scenarios, 2020-2030 (USD Million)
  • Table 18.84 Deep Learning in Medical Image Processing Market for Other Disorders, Conservative, Base and Optimistic Scenarios, 2020-2030 (USD Million)
  • Table 18.85 Deep Learning in Medical Image Processing Market: Distribution by Type of Image Processed, Conservative, Base and Optimistic Scenarios, Conservative, Base and Optimistic Scenarios, 2020-2030 (USD Million)
  • Table 18.86 Deep Learning in Medical Image Processing Market for X-Rays, Conservative, Base and Optimistic Scenarios, Conservative, Base and Optimistic Scenarios, 2020-2030 (USD Million)
  • Table 18.87 Deep Learning in Medical Image Processing Market for MRI, Conservative, Base and Optimistic Scenarios, 2020-2030 (USD Million)
  • Table 18.88 Deep Learning in Medical Image Processing Market for CT, Conservative, Base and Optimistic Scenarios, 2020-2030 (USD Million)
  • Table 18.89 Deep Learning in Medical Image Processing Market for Ultrasound, Conservative, Base and Optimistic Scenarios, 2020-2030 (USD Million)
  • Table 18.90 Deep Learning in Medical Image Processing Market: Distribution by Key Geographical Regions, Conservative, Base and Optimistic Scenarios, 2020-2030 (USD Million)
  • Table 18.91 Deep Learning in Medical Image Processing Market in North America, Conservative, Base and Optimistic Scenarios, 2020-2030 (USD Million)
  • Table 18.92 Deep Learning in Medical Image Processing Market in Europe, Conservative, Base and Optimistic Scenarios, 2020-2030 (USD Million)
  • Table 18.93 Deep Learning in Medical Image Processing Market in Asia Pacific / RoW, Conservative, Base and Optimistic Scenarios, 2020-2030 (USD Million)
  • Table 18.94 Opportunity for Deep Learning in Medical Image Processing Market, 2015-2030 (COVID Impact Scenario)

Listed Companies

The following companies and organizations have been mentioned in the report

  • 1. 8VC
  • 2. Accel
  • 3. Acequia Capital
  • 4. Advantech Capital
  • 5. Advenio Technosys
  • 6. Aetion
  • 7. Affidea
  • 8. Agfa HealthCare
  • 9. AiCure
  • 10. Aidence
  • 11. Aidoc
  • 12. Alberta Innovates
  • 13. AlbionVC
  • 14. AlchemyAPI
  • 15. Alder Hey Children's Hospital
  • 16. AlgoMedica
  • 17. AlgoSurg
  • 18. Allen Institute for AI
  • 19. ALMatter
  • 20. Almaworks
  • 21. Amazon Web Services
  • 22. AME Cloud Ventures
  • 23. AME Cloud Ventures
  • 24. American Cancer Society
  • 25. American Diabetes Association
  • 26. American Heart Association
  • 27. American Sleep Apnea Association
  • 28. aMoon
  • 29. Amplify Partners
  • 30. Analytics Ventures
  • 31. Anand Diagnostic Laboratory
  • 32. Anthem
  • 33. Antwerp University Hospital (UZA)
  • 34. Apollo Hospitals
  • 35. Apple
  • 36. Apposite Capital
  • 37. Artelus
  • 38. Arterys
  • 39. Arya.ai
  • 40. Asan Medical Center
  • 41. Asset Management Ventures
  • 42. AT&T Labs
  • 43. Atomico
  • 44. Atrium Health
  • 45. Aurum
  • 46. Avicenna
  • 47. Axilor Ventures
  • 48. Ayce Capital
  • 49. AZ Maria Middelares
  • 50. Baidu.ventures
  • 51. Baillie Gifford
  • 52. Bar-llan University
  • 53. Behold.ai
  • 54. Beijing Dongfang Hongtai Technology
  • 55. Beijing Hao Yun Dao Information & Technology (Paiyipai)
  • 56. BenevolentAI
  • 57. Benslie Investment Group
  • 58. BI INVESTMENTS
  • 59. Bill & Melinda Gates Foundation
  • 60. BinomixRay
  • 61. Bioinfogate
  • 62. Biotechnology Industry Research Assistance Council (BIRAC)
  • 63. Blackford Analysis
  • 64. BlueCross BlueShield Venture
  • 65. Boca Raton Regional Hospital
  • 66. Boehringer Ingelheim
  • 67. Boehringer Ingelheim
  • 68. Bold Brain Ventures
  • 69. Bold Capital Partners
  • 70. Bolton NHS Foundation Trust
  • 71. Boston Children's Hospital
  • 72. Brainomix
  • 73. Bridge Bank
  • 74. Bridge to Health USA
  • 75. Buckinghamshire Healthcare NHS Trust
  • 76. Business Development Bank of Canada (BDC)
  • 77. Butterfly Network
  • 78. Cadens Medical Imaging
  • 79. Campus Bio-Medico University Hospital
  • 80. Canon Medical Systems
  • 81. Capital Health
  • 82. Capitol Health
  • 83. Capricorn Partners
  • 84. Caption Health
  • 85. Carestream Health
  • 86. CDH Investments
  • 87. Cedars-Sinai
  • 88. Cemag Invest
  • 89. Cenkos Securities
  • 90. Centre for Advanced Research in Imaging
  • 91. ChainZ Medical Technology
  • 92. Change Healthcare
  • 93. Chimera Partners
  • 94. Chiratae Ventures
  • 95. Chiratae Ventures (Formerly IDG Ventures)
  • 96. Clalit Research Institute
  • 97. Cleveland Clinic
  • 98. Clever Sense
  • 99. Cloud DX
  • 100. Co-Diagnostics
  • 101. Cognea
  • 102. Connect Ventures
  • 103. Connecticut Innovations
  • 104. ContextVision
  • 105. CorTechs Labs
  • 106. Cota Capital
  • 107. Crouse Health
  • 108. CRV (acquired by Microsoft)
  • 109. Ctrip
  • 110. CuraCloud
  • 111. CureMetrix
  • 112. Danhua Capital (DHVC)
  • 113. Daotong Capital
  • 114. Dark Blue Labs
  • 115. Dartford and Gravesham NHS Trust
  • 116. Dartmouth College
  • 117. Data Collective
  • 118. Data Collective (DCVC)
  • 119. Dataminr
  • 120. Deep Genomics
  • 121. DeepMind
  • 122. DeepTek
  • 123. Deepwise
  • 124. DEFTA Partners
  • 125. Dell
  • 126. DePuy Synthes
  • 127. DiA Imaging Analysis
  • 128. DigitalOcean
  • 129. DNA Capital
  • 130. DNNresearch
  • 131. doc.ai
  • 132. DocPanel
  • 133. Dolby Family Ventures
  • 134. Dong Kook Lifescience
  • 135. Dr. Susan Love Foundation for Breast Cancer Research
  • 136. Dubai Diabetes Center
  • 137. Duke University
  • 138. East Seattle Partners
  • 139. EBSCO
  • 140. EchoNous
  • 141. Edan Instruments
  • 142. Edwards Lifesciences
  • 143. eInfochips
  • 144. Elekta
  • 145. Emergent Connect
  • 146. Emergent Medical Partners
  • 147. Emu Technology
  • 148. Endiya Partners
  • 149. Enlitic
  • 150. Erlanger Health System
  • 151. European Commission
  • 152. Exigent Capital Group
  • 153. Exilant Technologies
  • 154. Exor
  • 155. Explorys, an IBM Company
  • 156. Fang Danhua Capital
  • 157. fast.ai
  • 158. FbStart
  • 159. FemtoDx
  • 160. Fertility Road
  • 161. ff Venture Capital
  • 162. Fidelis Care
  • 163. Fidelity Investments
  • 164. FIDI (Imaging Diagnostic Research Institute Foundation)
  • 165. Forestay Capital
  • 166. Forge
  • 167. Formation 8
  • 168. Fosun RZ Capital
  • 169. Founder Friendly Labs (FFL)
  • 170. Fractal Analytics
  • 171. Frazier Healthcare Partners
  • 172. Froedtert & the Medical College of Wisconsin Cancer Network
  • 173. Frost Data Capital
  • 174. Fujifilm Medical Systems USA
  • 175. FUJIFILM Sonosite
  • 176. Fujita Health University
  • 177. Future Play Green Cross Holdings
  • 178. Fysicon
  • 179. Gachon University Gil Medical Center
  • 180. GE Healthcare
  • 181. GE Ventures
  • 182. Genentech
  • 183. gener8tor
  • 184. General City Hospital, Aalst
  • 185. Genesis Capital Advisors
  • 186. Georges Harik
  • 187. GF Securities
  • 188. Google
  • 189. Google Ventures
  • 190. Government of Canada
  • 191. Granata Decision Systems (acquired by Google)
  • 192. Green House Ventures (GHV) Accelerator
  • 193. Greenbox Venture Partners
  • 194. Greenoaks Capital
  • 195. Greycroft
  • 196. Guerbet
  • 197. Haitong Leading Capital Management
  • 198. Halli Labs
  • 199. HALO Diagnostics
  • 200. Hanfor Capital Management
  • 201. Hangzhou CognitiveCare
  • 202. Harrow Council
  • 203. HB Investment
  • 204. Health Innovations
  • 205. HealthKonnect India
  • 206. HealthNet Global
  • 207. HeartFlow
  • 208. HelpAround
  • 209. henQ
  • 210. Hera Investment Funds
  • 211. Herman Verrelst
  • 212. Highmark Health
  • 213. Holland Capital
  • 214. Hongdao Capital
  • 215. Hoxton Ventures
  • 216. HTC
  • 217. Huntington Hospital
  • 218. Hyundai Investment Partners
  • 219. IBM
  • 220. iCAD
  • 221. icometrix
  • 222. iLabs Capital
  • 223. Illumina
  • 224. IMADIS Téléradiologie
  • 225. Imagia Cybernetics
  • 226. Imaging Biometrics
  • 227. Imbio
  • 228. ImFusion
  • 229. IMM Investment
  • 230. Imperial College London
  • 231. Incepto
  • 232. Indira IVF
  • 233. Infervision
  • 234. InHealth
  • 235. INKEF Capital
  • 236. In-Med Prognostics
  • 237. Innova Salud
  • 238. Innovacom
  • 239. Innovate UK
  • 240. Innovation Endeavors
  • 241. InnovationQuarter
  • 242. Institut Curie
  • 243. Institute for Data Valorization (IVADO)
  • 244. Intel
  • 245. Intelerad Medical Systems
  • 246. Intelligent Ultrasound
  • 247. Intermountain Healthcare
  • 248. Intervest
  • 249. Intrasense
  • 250. Invenshure
  • 251. IQ Capital
  • 252. iSchemaView (RapidAI)
  • 253. iSono Health
  • 254. Israel Innovation Authority
  • 255. Jetpac (Justice Education Technology Political Advocacy Center)
  • 256. Johns Hopkins University
  • 257. Johnson & Johnson
  • 258. joule
  • 259. Kaggle
  • 260. Kakao Ventures
  • 261. Karos Health
  • 262. KB Investment
  • 263. Kentuckiana Health Collaborative (KHC)
  • 264. Keshif Ventures
  • 265. Kheiron Medical Technologies
  • 266. Khosla Ventures
  • 267. Kinzon Capital
  • 268. Kinzon Capital
  • 269. Kleiner Perkins
  • 270. Koinvesticinis Fondas
  • 271. Koios Medical
  • 272. Konica Minolta
  • 273. Korea Development Bank
  • 274. Korea Telecom
  • 275. Kt Investments
  • 276. Kumamoto University
  • 277. L2 Ventures
  • 278. La Costa Investment Group
  • 279. Legend Capital
  • 280. Lenovo
  • 281. Lenovo
  • 282. LG CNS
  • 283. Linköping University
  • 284. LPIXEL
  • 285. LucidHealth
  • 286. Lumenis
  • 287. Luminous Ventures
  • 288. Lunit
  • 289. M3
  • 290. Maccabi Healthcare Services
  • 291. Mach7 Technologies
  • 292. Manipal Hospitals
  • 293. Marubeni
  • 294. MassMutual Ventures (MMV)
  • 295. MaxQ AI
  • 296. Mayo Clinic
  • 297. MBM Company
  • 298. McGill University
  • 299. MD Anderson Cancer Center
  • 300. MedAxiom
  • 301. MedGlobal
  • 302. Medica Superspecialty Hospital
  • 303. Mediscan Systems
  • 304. MEDNAX
  • 305. MedNetwork
  • 306. MEDO.ai
  • 307. Medsynaptic
  • 308. MEDTEQ
  • 309. Medtronic
  • 310. Merge Healthcare
  • 311. Methinks
  • 312. Microsoft
  • 313. Mindshare Medical
  • 314. Minneapolis Heart Institute Ventures
  • 315. Mirada Medical
  • 316. Mirae Asset Venture Investment
  • 317. MLP Care
  • 318. Monash IVF
  • 319. Montefiore Nyack Hospital
  • 320. Montreal Institute for Learning Algorithms (MILA)
  • 321. Moodstocks
  • 322. Moorfields Eye Hospital
  • 323. Morado Venture Partners
  • 324. Mount Sinai Hospital
  • 325. Myongji Hospital
  • 326. Nanox
  • 327. National Health Service (NHS) Trust
  • 328. National Imaging Academy Wales
  • 329. National Institute of General Medical Sciences
  • 330. National Institutes of Health
  • 331. National Science Foundation
  • 332. Nauto
  • 333. NeuralSeg
  • 334. New York Genome Center (NYGC)
  • 335. New York University (NYU)
  • 336. NewMargin Ventures
  • 337. NewYork-Presbyterian Hospital
  • 338. Nico.lab
  • 339. Nightingale Hospital
  • 340. Nines
  • 341. NMC Healthcare
  • 342. Nobori
  • 343. Nordic Medtech
  • 344. Northwell Health
  • 345. Northzone
  • 346. Norwich Ventures
  • 347. Novo Nordisk
  • 348. NTT DATA
  • 349. Nuance Communications
  • 350. NVIDIA
  • 351. NXC Imaging
  • 352. Nyansa (now a part of VMware)
  • 353. ODH Solutions
  • 354. Olea Medical
  • 355. Optellum
  • 356. Optina Diagnostics
  • 357. Optum Ventures
  • 358. ORI Capital
  • 359. OurCrowd
  • 360. Ovation Fertility
  • 361. Oxipit
  • 362. Panorama Point Partners
  • 363. Parkwalk Advisors
  • 364. Partners HealthCare
  • 365. Pathway Genomics
  • 366. Pentathlon Ventures
  • 367. Philips
  • 368. Phytel, An IBM Company
  • 369. pi Ventures
  • 370. platform.ai
  • 371. PointGrab
  • 372. PowerCloud Venture Capital
  • 373. Practica Capital
  • 374. Prairie Cardiovascular
  • 375. Precision Vascular
  • 376. Presence Capital
  • 377. Qiming Venture Partners
  • 378. Qingsong Fund
  • 379. Qualcomm Design
  • 380. Quantib
  • 381. Quest Diagnostics
  • 382. QuEST Global
  • 383. QUIBIM
  • 384. Qure.ai
  • 385. Rabo Ventures
  • 386. RADLogics
  • 387. RaySearch Laboratories
  • 388. Realize
  • 389. Red Hat
  • 390. Regal Funds Management
  • 391. Revelation Partners
  • 392. Rhön-Klinikum
  • 393. Riverain Technologies
  • 394. Roche
  • 395. Royal Berkshire NHS Foundation Trust
  • 396. Royal United Hospitals
  • 397. R-Pharm
  • 398. Samsung
  • 399. San Raffaele Hospital
  • 400. Sana Kliniken
  • 401. Satis Operations
  • 402. SB Investment
  • 403. SBRI Healthcare
  • 404. ScreenPoint Medical
  • 405. SeeAI
  • 406. Segunda Lectura Diagnóstica
  • 407. Sejong Hospital
  • 408. SELECT Healthcare Solutions
  • 409. SEMA Translink Investment
  • 410. SemanticMD
  • 411. Semmelweis University
  • 412. Sentient Technologies
  • 413. Seoul National University Hospital
  • 414. Sequoia Capital
  • 415. ShengJing360
  • 416. Shinhan Investment
  • 417. Siemens Healthineers
  • 418. SigTuple
  • 419. Skope Magnetic Resonance Technologies
  • 420. Smilegate Investment
  • 421. SoftBank Ventures Asia
  • 422. SpaceX
  • 423. Square Peg Capital
  • 424. SRI Ventures
  • 425. St. John's College
  • 426. Stanford University
  • 427. StartX
  • 428. Subtle Medical
  • 429. Sunland Fund
  • 430. Sunshine Insurance Group
  • 431. Taihe Capital
  • 432. Tech Transfer UPV
  • 433. Tekes - the Finnish Funding Agency for Technology and Innovation
  • 434. Telemedicine Clinic
  • 435. Telerad Tech
  • 436. Temasek
  • 437. Temecula Valley Hospital
  • 438. Tencent
  • 439. TeraRecon
  • 440. Terason
  • 441. Teva Pharmaceuticals
  • 442. Texas Medical Center
  • 443. The Alan Turing Institute
  • 444. The American College of Radiology (ACR) Data Science Institute(DSI)
  • 445. The Inventor's Guild
  • 446. The Israel Innovation Authority
  • 447. The Jagen Group
  • 448. The Oncopole
  • 449. The Scottish Government
  • 450. The Venture Reality Fund
  • 451. Thorney Investment Group
  • 452. Threshold Ventures
  • 453. Tiatros
  • 454. Timeful (acquired by Google)
  • 455. TLV Partners
  • 456. Tongdu Capital
  • 457. Tracxn Technologies
  • 458. Trakterm
  • 459. Trillium Health Partners
  • 460. Trusted Insight
  • 461. Truven Health Analytics
  • 462. Tsingyuan Ventures
  • 463. Twitter Cortex
  • 464. University of Antwerp
  • 465. University of Bordeaux
  • 466. University of California
  • 467. University of Cambridge
  • 468. University of Dundee
  • 469. University of Edinburgh
  • 470. University of Florida
  • 471. University of Hertfordshire
  • 472. University of Montreal
  • 473. University of Oxford
  • 474. University of Oxford
  • 475. University of San Francisco
  • 476. University of Sheffield
  • 477. UW Medicine
  • 478. Varian Medical Systems
  • 479. VH Capital
  • 480. Vision Factory
  • 481. Vivo
  • 482. Viz.ai
  • 483. Vizyon
  • 484. Volpara Solutions
  • 485. VoxelCloud
  • 486. VUNO
  • 487. Wavemaker Partners
  • 488. WeDoctor
  • 489. Wellbeing Software
  • 490. Wellington Management
  • 491. Wisemont Capital
  • 492. Wish
  • 493. Women's Imaging Associates
  • 494. XB Ventures
  • 495. Xiang He Capital
  • 496. Y Combinator
  • 497. Yongin Severance Hospital
  • 498. Zebra Medical Vision
  • 499. ZhenFund
目次

Overview:

Deep learning is a machine learning approach that involves the use of intuitive algorithms and artificial neural networks to facilitate unsupervised pattern recognition / insight generation from large volumes of unstructured data. This technology is gradually being incorporated in a variety of applications across the healthcare sector, including imaging-based medical diagnosis and data processing. Specifically concerning medical imaging, deep learning has the potential to be used to automate information processing and result interpretation for a variety of diagnostic images, such as X-rays, computed tomography scans, magnetic resonance imaging, and positron emission tomography. In this context, it is worth mentioning that the manual examination of medical images is limited, both in terms of accuracy (resulting in misdiagnosis) and throughput (leading to delays in communication of results). As a result, in situations characterized by low physician / pathologist to patient ratios, the conventional mode of operation is rendered inadequate. Experts have predicted a shortage of 10,000 to 40,000 physicians, by 2030, in the US alone. Further, it is estimated that 90% of medical data generated in hospitals is in the form of images; this puts an immense burden on radiologists and other consulting physicians related to processing such large volumes of data. In fact, according to a study published in the American Journal of Medicine, ~15% of reported medical cases in developed countries, are misdiagnosed. In addition, close to 1.5 million individuals are estimated to die each year, across the world, due to misdiagnosis. On the other hand, accurate diagnosis at an early stage has been demonstrated to allow significant cost savings for both patients and healthcare providers. In this scenario, deep learning and other artificial intelligence-based technologies are currently being developed / investigated to automate such processes.

Over time, various industry stakeholders have designed proprietary deep learning algorithms for processing of medical images. Presently, many innovators claim to have developed the means to train computers to read and triage medical images, and recognize patterns related to both temporal and spatial changes (which are not even visible to the naked eye). Experts in this field also believe that the use of deep learning can actually speed up the processing and interpretation of radiology data by 20%, reducing the rate of false positives by approximately 10%. It is also worth mentioning that in the past few years, the FDA has provided the necessary clearances and approved the use for a variety of deep learning software. Moreover, several technology-focused innovators, such as (in alphabetical order) IBM, GE Healthcare and Google, have entered into strategic alliances with big pharma players, in order to bring proprietary deep learning-based medical solutions to the market. This upcoming segment of the pharmaceutical industry that exists at the interface between medicine and information technology, has garnered the attention of prominent venture capital firms and strategic investors. In the long term, the market is anticipated witness significant growth as more machine learning based solutions are approved for use.

Scope of the Report:

The 'Deep Learning Market: Focus on Medical Image Processing, 2020-2030' report features an extensive study on the current market landscape offering an informed opinion on the likely adoption of such solutions over the next decade. The study presents an in-depth analysis, highlighting the capabilities of various stakeholders engaged in this domain. In addition to other elements, the report provides:

  • A detailed review of the current market landscape of deep learning solutions for medical image processing, along with information on their status of development (launched / under development), regulatory approvals (FDA, CE mark, others), type of offering (diagnostic software / tool, diagnostic software / tool + device), type of image processed (X-ray, MRI, CT, ultrasound), application area (lung infections / respiratory disorders, brain injuries / disorders, lung cancer, cardiac conditions / cardiovascular disorders, bone deformities / orthopedic disorders, breast cancer and others). In addition, it presents details of companies developing such solutions, such as their year of establishment, company size, location of headquarters and focus area (in terms of type of deployment model). Further, it highlights key features of each solution and affiliated technologies.
  • An in-depth analysis of the contemporary market trends, presented using three schematic representations, including [A] a grid representation illustrating the distribution of solutions based on application area, type of image processed and type of offering and [B] an insightful map representation highlighting the geographical activity of the players.
  • Elaborate profiles of key players that are engaged in the development of deep learning-based solutions intended for processing of medical images. Each company profile features a brief overview of the company (including information on year of establishment, number of employees, location of headquarters and key members of the executive team), details of their respective portfolio of solutions, recent developments and an informed future outlook.
  • An analysis of the partnerships that have been inked by stakeholders in the domain, during the time period 2016-2020 (till June), covering research / development agreements, solution utilization agreements, solution integration agreements, marketing / distribution agreements, other relevant types of deals.
  • An analysis of the investments made, including seed financing, venture capital financing, debt financing, grants and others, in companies that are focused on developing deep learning-based solutions intended for processing of medical images.
  • An elaborate valuation analysis of companies that are involved in applying deep learning in solutions intended for processing of medical images. Further, we have built a multi-variable dependent valuation model to estimate the current valuation of a number of companies engaged in this domain.
  • A clinical trial analysis of completed, ongoing and planned studies (available on ct.gov), focused on the assessment deep learning-based software solutions, based on various parameters, such as trial registration year, trial recruitment status, trial design, target therapeutic area, leading industry and non-industry players, and geographical locations of trials.
  • An in-depth analysis of over 3,000 patents related to deep learning and medical images that have been filed / granted till June 2020, highlighting key trends associated with these patents, across type of patent, publication year and application year, regional applicability, CPC symbols, emerging focus areas, leading patent assignees (in terms of number of patents filed / granted), patent benchmarking and valuation.
  • An insightful analysis highlighting cost saving potential associated with the use of deep learning solutions intended for processing of medical images, based on information gathered from close to 30 countries, taking into consideration various parameters, such as total number of radiologists, annual salary of radiologists, number of scans performed (across each type of image) and increase in efficiency by adoption of deep learning solutions.
  • An insightful discussion on the views presented by various industry and non-industry experts present across the globe, on various portals, such as YouTube and other media platforms. The summary of insights provided by each expert is discussed across focus area, current industry status / challenges and future outlook.

One of the key objectives of this report was to estimate the existing market size and the future growth potential within the deep learning market (medical image processing segment), such as global radiology spending across countries, number of radiologists employed across different regions of globe, annual salary of radiologists, rate of adoption of deep learning-based solutions, we have developed informed estimates on the financial evolution of the market, over the period 2020-2030. The report also provides details on the likely distribution of the current and forecasted opportunity across [A] application area (lung infections / respiratory disorders, brain injuries / disorders, lung cancer, cardiac conditions / cardiovascular disorders, bone deformities / orthopedic disorders, breast cancer and others), [B] type of image processed (X-ray, MRI, CT, ultrasound) and [C] region (North America, Europe and Asia Pacific / Rest of the World). In order to account for future uncertainties and to add robustness to our forecast model, we have provided three scenarios, namely conservative, base and optimistic scenarios, representing different tracks of the industry's growth.

The opinions and insights presented in the report were also influenced by discussions held with multiple stakeholders in this domain. The report features detailed transcripts of interviews held with the following individuals (in alphabetical order):

  • Walter de Back (Research Scientist, Context Vision, Q2 2020)
  • Dr. Vikas Karade (CEO, AlgoSurg, Q2 2020)
  • Babak Rasolzadeh (Senior Director of Product, Arterys, Q2 2020)
  • Carla Leibowitz, (Head of Strategy and Marketing, Arterys, Q2 2017)
  • Mausumi Acharya, (CEO, Advenio Technosys, Q2 2017)
  • Deekshith Marla, (CTO, Arya.ai) and Sanjay Bhadra, (COO, Arya.ai, Q2 2017)

All actual figures have been sourced and analyzed from publicly available information forums. Financial figures mentioned in this report are in USD, unless otherwise specified.

Key Questions Answered:

  • Who are the leading developers of deep learning-based solutions for medical image processing?
  • What are the key application areas for deep learning solutions designed for processing of medical images, such as X-Ray, ultrasound, CT, MRI and others?
  • How many solutions based on deep learning technology for processing of medical images have been cleared by FDA or have received CE marking?
  • What is the impact of COVID-19 on the demand for deep learning solutions designed for processing of medical images?
  • What is the likely valuation / net worth of companies involved in this segment?
  • What is the likely cost saving potential associated with the use of deep learning-based solutions for processing of medical images?
  • How is the current and future opportunity likely to be distributed across key market segments?
  • What is the potential usability of deep learning-based medical image processing solutions for lung scanning in COVID-19 patients?
  • Which partnership models are commonly adopted by stakeholders in this industry?
  • What is the overall trend of funding and investments in this domain?
  • What are the opinions of key opinion leaders involved in the deep learning space?

Chapter Outlines:

Chapter 2 is an executive summary of the key insights captured in our research. It offers a high-level view on the current state of deep learning in medical image processing market and its likely evolution in the short-mid term and long term.

Chapter 3 is an introductory chapter that presents details on the digital revolution in the medical industry. It elaborates on the growth of artificial intelligence and machine learning tools, such as deep learning algorithms, along with a discussion on their potential applications in solving some of the key challenges faced by the healthcare industry. The chapter also gives an overview on the rise of big data and its role in providing personalized and evidence-based care to patients. It emphasizes on the applications of deep learning in healthcare sector with detailed information on areas including personalized medicine and drug discovery, personal fitness and lifestyle management, clinical trial management and medical image processing. Additionally, it includes an analysis of contemporary trends, as observed on the Google Trends (till August 2020) and insights from the recent news articles related to deep learning and medical image processing, indicating the increasing popularity of this domain.

Chapter 4 presents a case study on two technology giants in this field, namely IBM and Google. It provides a detailed description of the initiatives being undertaken by these companies to explore the applications of deep learning in the medical field. In addition, the chapter provides a comparison of the two companies based on their respective deep learning expertise, and partnerships and acquisitions.

Chapter 5 includes a detailed analysis of the current market landscape of over 200 deep learning-based medical image processing solutions, based on status of development (launched / under development), regulatory approvals (FDA, CE marked, others), type of offering (diagnostic software / tool, diagnostic software / tool + device), type of image processed (X-ray, MRI, CT, ultrasound) and application area (lung infections / respiratory disorders, brain injuries / disorders, lung cancer, cardiac conditions / cardiovascular disorders, bone deformities / orthopedic disorders, breast cancer and others).

In addition, it presents details of companies developing such solutions, such as their year of establishment, company size, location of headquarters and focus area (in terms of type of deployment model). Further, it highlights key features of each solution and affiliated technologies. It also includes an in-depth analysis of the contemporary market trends, presented using three schematic representations, including [A] a grid representation illustrating the distribution of solutions based on application area, type of image processed and type of offering and [B] an insightful map representation highlighting the geographical activity of the players.

Chapter 6 features elaborate profiles of key players that are engaged in the development of deep learning-based solutions intended for processing of medical images. Each company profile features a brief overview of the company (including information on year of establishment, number of employees, location of headquarters and key members of the executive team), details of their respective portfolio of solutions, recent developments and an informed future outlook.

Chapter 7 features an in-depth analysis and discussion on the various partnerships that have been inked by stakeholders in the domain, during the time period between 2016 and 2020 (till June), covering research / development agreements, solution utilization agreements, solution integration agreements, marketing / distribution agreements, other relevant types of deals.

Chapter 8 includes a detailed analysis of the investments made, including seed financing, venture capital financing, debt financing, grants, and others, in companies that are focused on developing deep learning-based solutions intended for processing of medical images.

Chapter 9 is a detailed valuation analysis of companies that are involved in applying deep learning in solutions intended for processing of medical images. Further, we have built a multi-variable dependent valuation model to estimate the current valuation of a number of companies engaged in this domain.

Chapter 10 represents an elaborate clinical trial analysis of completed, ongoing and planned studies (available on ct.gov), focused on the assessment deep learning-based software solutions, based on various parameters, such as trial registration year, trial recruitment status, trial design, target therapeutic area, leading industry and non-industry players, and geographical locations of trials.

Chapter 11 includes an in-depth analysis of over 3,000 patents related to deep learning and medical images that have been filed / granted till June 2020, highlighting key trends associated with these patents, across type of patent, publication year and application year, regional applicability, CPC symbols, emerging focus areas, leading patent assignees (in terms of number of patents filed / granted), patent benchmarking and valuation.

Chapter 12 presents an insightful analysis highlighting cost saving potential associated with the use of deep learning solutions intended for processing of medical images, based on information gathered from close to 30 countries, taking into consideration various parameters, such as total number of radiologists, annual salary of radiologists, number of scans performed (across each type of image) and increase in efficiency by adoption of deep learning solutions.

Chapter 13 features an informed estimate of the existing market size and the future growth potential within the deep learning market (medical image processing segment), such as global radiology spending across countries, number of radiologists employed across different regions of globe, annual salary of radiologists, rate of adoption of deep learning-based solutions, we have developed informed estimates on the financial evolution of the market, over the period 2020-2030. The report also provides details on the likely distribution of the current and forecasted opportunity across [A] application area (lung infections / respiratory disorders, brain injuries / disorders, lung cancer, cardiac conditions / cardiovascular disorders, bone deformities / orthopedic disorders, breast cancer and others), [B] type of image processed (X-ray, MRI, CT, ultrasound) and [C] region (North America, Europe and Asia Pacific / Rest of the World). In order to account for future uncertainties and to add robustness to our forecast model, we have provided three scenarios, namely conservative, base and optimistic scenarios, representing different tracks of the industry's growth.

Chapter 14 presents an insightful discussion on the views presented by various industry and non-industry experts present across the globe, on various portals, such as YouTube and other media platforms. The summary of insights provided by each expert is discussed across focus area, current industry status / challenges and future outlook.

Chapter 15 is a collection of interview transcripts of discussions held with various key stakeholders in this market. The chapter provides a brief overview of the companies and details of interviews held with Walter de Back (Research Scientist, ContextVision), Dr. Vikas Karade (CEO, AlgoSurg, Q2 2020), Babak Rasolzadeh (Senior Director of Product, Arterys), Carla Leibowitz (Head of Strategy and Marketing, Arterys), Mausumi Acharya (CEO, Advenio Technosys), Deekshith Marla (CTO, Arya.ai) and Sanjay Bhadra (COO, Arya.ai).

Chapter 16 highlights the impact of COVID-19 on the overall deep learning in medical image processing market. It includes a brief discussion on the short-term and long-term impact of COVID-19 upsurge on the market opportunity for software developers. In addition, it includes a brief section on strategies and action plans that companies involved in this space have adopted in order to fight against the infection.

Chapter 17 is a summary of the overall report. It includes key takeaways related to research and analysis from the report in an infographic format.

Chapter 18 is an appendix, which provides tabulated data and numbers for all the figures provided in the report.

TABLE OF CONTENTS

1. PREFACE

  • 1.1. Scope of the Report
  • 1.2. Research Methodology
  • 1.3. Chapter Outlines

2. EXECUTIVE SUMMARY

3. INTRODUCTION

  • 3.1. Humans, Machines and Intelligence
  • 3.2. The Science of Learning
    • 3.2.1. Teaching Machines
      • 3.2.1.1. Machines for Computing
      • 3.2.1.2. Artificial Intelligence for Understanding the Human Brain
  • 3.3. Artificial Intelligence
  • 3.4. The Big Data Revolution
    • 3.4.1. Overview of Big Data
    • 3.4.2. Role of Internet of Things (IoT)
    • 3.4.3. Growing Adoption of Big Data
    • 3.4.4. Key Application Areas
      • 3.4.4.1. Big Data Analytics in Healthcare
      • 3.4.4.2. Machine Learning
      • 3.4.4.3. Deep Learning: The Amalgamation of Machine Learning and Big Data
  • 3.5. Applications of Deep Learning in Healthcare
    • 3.5.1. Personalized Medicine
    • 3.5.2. Personal Fitness and Lifestyle Management
    • 3.5.3. Drug Discovery
    • 3.5.4. Clinical Trial Management
    • 3.5.5. Medical Image Processing

4. CASE STUDY: IBM WATSON VERSUS GOOGLE DEEPMIND

  • 4.1. Chapter Overview
  • 4.2. International Business Machines (IBM)
    • 4.2.1. Company Overview
    • 4.2.2. Financial Information
    • 4.2.3. IBM Watson
  • 4.3. Google
    • 4.3.1. Company Overview
    • 4.3.2. Financial Information
    • 4.3.3. Google DeepMind
  • 4.4. IBM versus Google: Artificial Intelligence-related Acquisitions
  • 4.5. IBM versus Google: Healthcare Focused Partnerships and Collaborations
  • 4.6. IBM versus Google: Primary Concerns and Future Outlook

5. MARKET OVERVIEW

  • 5.1. Chapter Overview
  • 5.2. Deep Learning in Medical Image Processing: Overall Market Landscape
    • 5.2.1. Analysis by Status of Development
      • 5.2.1.1 Analysis by Regulatory Approvals Received
    • 5.2.2. Analysis by Type of Offering
    • 5.3.3. Analysis by Type of Image Processed
    • 5.2.4. Analysis by Anatomical Region
    • 5.2.5. Analysis by Application Area
    • 5.2.6. Grid Representation: Analysis by Type of Offering, Type of Image Processed and Application Area
  • 5.3. Deep Learning in Medical Image Processing: Information on Key Characteristics
  • 5.4. Deep Learning in Medical Image Processing: List of Companies
    • 5.4.1. Analysis by Year of Establishment
    • 5.4.2. Analysis by Company Size
    • 5.4.3. Analysis by Location of Headquarters
      • 5.4.3.1. World Map Representation: Regional Activity
    • 5.4.4. Analysis by Type of Deployment Model
    • 5.4.5. Leading Companies: Analysis by Number of Solutions

6. COMPANY PROFILES

  • 6.1. Chapter Overview
  • 6.2. Artelus
    • 6.2.1. Company Overview
    • 6.2.2. Product / Technology Portfolio
    • 6.2.3. Recent Developments and Future Outlook
  • 6.3. Arterys
    • 6.3.1. Company Overview
    • 6.3.2. Product / Technology Portfolio
    • 6.3.3. Recent Developments and Future Outlook
  • 6.4. Butterfly Network
    • 6.4.1. Company Overview
    • 6.4.2. Product / Technology Portfolio
    • 6.4.3. Recent Developments and Future Outlook
  • 6.5. ContextVision
    • 6.5.1. Company Overview
    • 6.5.2. Product / Technology Portfolio
    • 6.5.3. Recent Developments and Future Outlook
  • 6.6. Enlitic
    • 6.6.1. Company Overview
    • 6.6.2. Product / Technology Portfolio
    • 6.6.3. Recent Developments and Future Outlook
  • 6.7. Echonous
    • 6.7.1. Company Overview
    • 6.7.2. Product / Technology Portfolio
    • 6.7.3. Recent Developments and Future Outlook
  • 6.8. GE Healthcare
    • 6.8.1. Company Overview
    • 6.8.2. Product / Technology Portfolio
    • 6.8.3. Recent Developments and Future Outlook
  • 6.9. InferVision
    • 6.9.1. Company Overview
    • 6.9.2. Product / Technology Portfolio
    • 6.9.3. Recent Developments and Future Outlook
  • 6.10. VUNO
    • 6.10.1. Company Overview
    • 6.10.2. Product / Technology Portfolio
    • 6.10.3. Recent Developments and Future Outlook

7. PARTNERSHIPS AND COLLABORATIONS

  • 7.1. Chapter Overview
  • 7.2. Partnership Models
  • 7.3. Deep Learning in Medical Image Processing: List of Partnerships and Collaborations
    • 7.3.1. Analysis by Year of Partnership
    • 7.3.2. Analysis by Type of Partnership
    • 7.3.3. Analysis by Year and Type of Partnership
    • 7.3.4. Analysis by Type of Partner
    • 7.3.5. Analysis by Therapeutic Area
    • 7.3.6. Most Active Players: Analysis by Number of Partnerships
    • 7.3.7. Regional Analysis
    • 7.3.8. Intercontinental and Intracontinental Agreements
  • 7.4. Concluding Remarks

8. FUNDING AND INVESTMENT ANALYSIS

  • 8.1. Chapter Overview
  • 8.2. Types of Funding
  • 8.3. Deep Learning in Medical Image Processing: Recent Funding Instances
    • 8.3.1. Analysis by Number of Funding Instances
    • 8.3.2. Analysis by Amount Invested
    • 8.3.3. Analysis by Type of Funding
    • 8.3.4. Most Active Players: Analysis by Number of Funding Instances and Amount Invested
    • 8.3.5. Most Active Investors: Analysis by Number of Funding Instances
    • 8.3.6. Geographical Analysis by Amount Invested

9. COMPANY VALUATION ANALYSIS

  • 9.1. Chapter Overview
  • 9.2. Methodology
  • 9.3. Categorization by Parameters
    • 9.3.1. Twitter Followers Score
    • 9.3.2. Google Hits Score
    • 9.3.3. Partnerships Score
    • 9.3.3. Weighted Average Score
    • 9.3.4. Company Valuation: Roots Analysis Proprietary Scores

10. CASE STUDY: ANALYSIS OF DEEP LEARNING-BASED CLINICAL TRIALS REGISTERED IN THE US

  • 10.1. Chapter Overview
  • 10.2. Scope and Methodology
  • 10.3 Clinical Trial Analysis
    • 10.3.1. Analysis by Trial Registration Year
    • 10.3.2. Analysis by Trial Registration Year and Recruitment Status
    • 10.3.3. Analysis by Trial Registration Year and Patient Enrollment
    • 10.3.4. Analysis by Trial Design
    • 10.3.5. Analysis by Patient Segment
    • 10.3.6. Analysis by Therapeutic Area
    • 10.3.7. Analysis by Trial Objective
    • 10.3.8. Analysis by Focus Areas
    • 10.3.9. Analysis by Type of Image Processed
    • 10.3.8. Most Active Players: Analysis by Number of Clinical Trials
    • 10.3.9. Analysis by Number of Clinical Trials and Geography
    • 10.3.10. Analysis by Enrolled Patient Population and Geography

11. PATENT ANALYSIS

  • 11.1. Chapter Overview
  • 11.2. Scope and Methodology
  • 11.3. Deep Learning and Medical Image Processing: Patent Analysis
    • 11.3.1. Analysis by Application Year and Publication Year
    • 11.3.2. Analysis by Issuing Authority / Patent Offices Involved
    • 11.3.3. Analysis by IPCR Symbols
    • 11.3.4. Emerging Focus Areas
    • 11.3.5. Leading Assignees: Analysis by Number of Patents
    • 11.3.6. Patent Benchmarking Analysis
      • 11.3.6.1. Analysis by Patent Characteristics
  • 11.4. Patent Valuation Analysis

12. COST SAVING ANALYSIS

  • 12.1. Chapter Overview
  • 12.2. Key Assumptions and Methodology
  • 12.3. Overall Cost Saving Potential of Deep Learning in Medical Image Processing Solutions, 2020-2030
  • 12.4. X-Ray Images
    • 12.4.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (X-Ray Images): Analysis by Geography
      • 12.4.1.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (X-Ray Images) in North America, 2020-2030
      • 12.4.1.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (X-Ray Images) in Europe, 2020-2030
      • 12.4.1.3. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (X-Ray Images) in Asia-Pacific and RoW, 2020-2030
    • 12.4.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions: Analysis by Economic Strength
      • 12.4.2.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (X-Ray Images) in High Income Countries, 2020-2030
      • 12.4.2.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (X-Ray Images) in Middle Income Countries, 2020-2030
  • 12.5. MRI Images
    • 12.5.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (MRI Images): Analysis by Geography
      • 12.5.1.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (MRI Images) in North America, 2020-2030
      • 12.5.1.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (MRI Images) in Europe, 2020-2030
      • 12.5.1.3. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (MRI Images) in Asia-Pacific and RoW, 2020-2030
    • 12.5.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (MRI Images): Analysis by Economic Strength
      • 12.5.2.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (MRI Images) in High Income Countries, 2020-2030
      • 12.5.2.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (MRI Images) in Middle Income Countries, 2020-2030
  • 12.6. CT Images
    • 12.6.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (CT Images): Analysis by Geography
      • 12.6.1.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (CT Images) in North America, 2020-2030
      • 12.6.1.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (CT Images) in Europe, 2020-2030
      • 12.6.1.3. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (CT Images) in Asia-Pacific and RoW, 2020-2030
    • 12.6.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (CT Images): Analysis by Economic Strength
      • 12.6.2.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (CT Images) in High Income Countries, 2020-2030
      • 12.6.2.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (CT Images) in Middle Income Countries, 2020-2030
  • 12.7. Ultrasound Images
    • 12.7.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (Ultrasound Images): Analysis by Geography
      • 12.7.1.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (Ultrasound Images) in North America, 2020-2030
      • 12.7.1.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (Ultrasound Images) in Europe, 2020-2030
      • 12.7.1.3. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (Ultrasound Images) in Asia-Pacific and RoW, 2020-2030
    • 12.7.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (Ultrasound Images): Analysis by Economic Strength
      • 12.7.2.1. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (Ultrasound Images) in High Income Countries, 2020-2030
      • 12.7.2.2. Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (Ultrasound Images) in Middle Income Countries, 2020-2030
  • 12.8. Concluding Remarks: Cost Saving Scenarios

13. MARKET FORECAST

  • 13.1. Chapter Overview
  • 13.2 Forecast Methodology and Key Assumptions
  • 13.3 Overall Deep Learning in Medical Image Processing Market
  • 13.3 Deep Learning in Medical Image Processing Market: Distribution by Application Area
    • 13.3.1 Deep Learning in Medical Image Processing Market for Brain Abnormalities / Neurological Disorders
    • 13.3.2 Deep Learning in Medical Image Processing Market for Cardiac Abnormalities / Cardiovascular Disorders
    • 13.3.3 Deep Learning in Medical Image Processing Market for Breast Cancer
    • 13.3.4 Deep Learning in Medical Image Processing Market for Bone Deformities / Orthopedic Disorders
    • 13.3.5 Deep Learning in Medical Image Processing Market for Lung Infections / Lung Disorders
    • 13.3.6 Deep Learning in Medical Image Processing Market for Other Disorders
  • 13.4 Deep Learning in Medical Image Processing Market: Distribution by Type of Image Processed
    • 13.4.1 Deep Learning in Medical Image Processing Market for X-Rays
    • 13.4.2 Deep Learning in Medical Image Processing Market for MRI
    • 13.4.3 Deep Learning in Medical Image Processing Market for CT
    • 13.4.3 Deep Learning in Medical Image Processing Market for Ultrasound
  • 13.5 Deep Learning in Medical Image Processing Market: Distribution by Key Geographical Regions
    • 13.5.1 Deep Learning in Medical Image Processing Market in North America
    • 13.5.2 Deep Learning in Medical Image Processing Market in Europe
    • 13.5.3 Deep Learning in Medical Image Processing Market in Asia Pacific / RoW
  • 13.6 Concluding Remarks

14. DEEP LEARNING IN HEALTHCARE: EXPERT INSIGHTS

  • 14.1. Chapter Overview
  • 14.2. Industry Experts
    • 14.2.1. David Reich, President / Chief Operating Officer (The Mount Sinai Hospital) and Robbie Freeman, Vice President of Clinical Innovation (The Mount Sinai Hospital)
    • 14.2.2. Elad Benjamin, Vice President of Radiology Informatics (Philips) and Jonathan Laserson, Lead AI Strategist (Zebra Medical Vision)
    • 14.2.3. Kevin Lyman, Chief Executive Officer (Enlitic)
    • 14.2.4. Alejandro Jaimes, Chief Scientist and Senior Vice President (Dataminr)
    • 14.2.5. Jeremy Howard, Founder and Researcher (Fast.ai)
    • 14.2.6. Riley Doyle, Serial Entrepreneur and Data Engineer
  • 14.3. University and Hospital Experts
    • 14.3.1. Dr Steven Alberts, Chairman of Medical Oncology (Mayo Clinic)
    • 14.3.2. Neil Lawrence, Professor (University of Cambridge and University of Sheffield) and Senior AI Fellowship (Alan Turing Institute)
    • 14.3.3. Yoshua Bengio, Professor (Université de Montréal) and Scientific Director (IVADO)
  • 14.4. Other Expert Opinions

15. INTERVIEW TRANSCRIPTS

  • 15.1 Chapter Overview
  • 15.2. Advenio Technosys
    • 15.2.1. Company Snapshot
    • 15.2.2. Interview Transcript: Mausumi Acharya (CEO, Advenio Technosys, Q2 2017)
  • 15.3. Arterys
    • 15.3.1. Company Snapshot
    • 15.3.2. Interview Transcript: Carla Leibowitz (Head of Strategy and Marketing, Arterys, Q2 2017)
    • 15.3.3. Interview Transcript: Babak Rasolzadeh (Senior Director of Product, Arterys, Q2 2020)
  • 15.4. Arya.ai
    • 15.4.1. Company Snapshot
    • 15.4.2. Interview Transcript: Deekshith Marla (CTO, Arya.ai) and Sanjay Bhadra (COO, Arya.ai, Q2 2017)
  • 15.5. AlgoSurg
    • 15.5.1. Company Snapshot
    • 15.5.2. Interview Transcript: Dr. Vikas Karade (Founder / CEO, Q2 2020)
  • 15.6. ContextVision
    • 15.6.1. Company Snapshot
    • 15.6.2. Interview Transcript: Walter de Back (Research Scientist, Context Vision, Q2 2020)

16. IMPACT OF COVID-19 OUTBREAK ON DEEP LEARNING MARKET DYNAMICS

  • 16.1. Chapter Overview
  • 16.2. Evaluation of Impact of COVID-19 Pandemic
    • 16.2.1. Current Initiatives and Recuperative Strategies of Key Players
    • 16.2.2. Impact on Opportunity for Deep Learning in Medical Image Processing Market
  • 16.3. Response Strategies: A Roots Analysis Perspective
    • 16.3.1. Propositions for Immediate Implementation
    • 16.3.2. Propositions for Short / Long Term Implementation

17. CONCLUSION

18. APPENDIX 1: TABULATED DATA

19. APPENDIX 2: LIST OF COMPANIES AND ORGANIZATIONS

List Of Figures

  • Figure 3.1 Key Stages of Observational Learning
  • Figure 3.2 Understanding Neurons and the Human Brain: Key Scientific Contributions
  • Figure 3.3 Big Data: The Three V's
  • Figure 3.4 Internet of Things: Framework
  • Figure 3.5 Internet of Things: Applications in Healthcare
  • Figure 3.6 Big Data: Google Trends
  • Figure 3.7 Big Data: Application Areas
  • Figure 3.8 Big Data: Opportunities in Healthcare
  • Figure 3.9 Machine Learning Algorithm: Workflow
  • Figure 3.10 Machine Learning Algorithms: Timeline
  • Figure 3.11 Neural Networks: Architecture
  • Figure 3.12 Deep Learning: Image Recognition
  • Figure 3.13 Google Trends: Artificial Intelligence versus Machine Learning versus Deep Learning versus Cognitive Computing
  • Figure 3.14 Google Trends: Popular Keywords (Deep Learning)
  • Figure 3.15 Deep Learning Frameworks: Relative Performance
  • Figure 3.16 Personalized Medicine: Applications in Healthcare
  • Figure 4.17 IBM: Annual Revenues, 2016 - Q1 2020 (USD Billion)
  • Figure 4.18 Alphabet: Annual Revenues, 2016 - Q1 2020 (USD Billion)
  • Figure 5.1 Deep Learning in Medical Image Processing: Distribution by Status of Development
  • Figure 5.2 Deep Learning in Medical Image Processing: Distribution by Regulatory Approvals Received
  • Figure 5.3 Deep Learning in Medical Image Processing: Distribution by Type of Offering
  • Figure 5.4 Deep Learning in Medical Image Processing: Distribution by Type of Image Processed
  • Figure 5.5 Deep Learning in Medical Image Processing: Distribution by Anatomical Region
  • Figure 5.6 Deep Learning in Medical Image Processing: Distribution by Application Area
  • Figure 5.6 Grid Representation: Distribution by Type of Offering, Type of Image Processed and Application Area
  • Figure 5.7 Deep Learning in Medical Image Processing Solution Developers: Distribution by Year of Establishment
  • Figure 5.8 Deep Learning in Medical Image Processing Solution Developers: Distribution by Company Size
  • Figure 5.9 Deep Learning in Medical Image Processing Solution Developers: Distribution by Location of Headquarters
  • Figure 5.10 World Map Representation: Regional Activity of Deep Learning in Medical Image Processing Solution Developers
  • Figure 5.11 Deep Learning in Medical Image Processing Solution Developers: Distribution by Type of Deployment Model
  • Figure 5.12 Leading Deep Learning in Medical Image Processing Solution Developers: Distribution by Number of Solutions
  • Figure 7.1 Partnerships and Collaborations: Distribution by Year of Partnership
  • Figure 7.2 Partnerships and Collaborations: Distribution by Type of Partnership
  • Figure 7.3 Partnerships and Collaborations: Distribution by Year and Type of Partnership
  • Figure 7.4 Partnerships and Collaborations: Distribution by Type of Partner
  • Figure 7.5 Partnerships and Collaborations: Distribution by Therapeutic Area
  • Figure 7.6 Most Active Players: Distribution by Number of Partnerships
  • Figure 7.7 Partnerships and Collaborations: Regional Distribution
  • Figure 7.8 Partnerships and Collaborations: Intercontinental and Intracontinental Agreements
  • Figure 7.9 Partnerships and Collaborations: Summary of Partnership Activity
  • Figure 8.1 Funding and Investments: Distribution of Instances by Year of Establishment of Companies and Type of Funding, 2016 - H1 2020
  • Figure 8.2 Funding and Investments: Cumulative Year-wise Trend, 2016 - H1 2020
  • Figure 8.3 Funding and Investments: Distribution by Number of Funding Instances and Amount Invested, 2016-H1 2020
  • Figure 8.4 Funding and Investments: Distribution by Type of Funding
  • Figure 8.5 Funding and Investments: Distribution by Type of Funding and Total Amount Invested (USD Million)
  • Figure 8.6 Most Active Players: Distribution by Number of Funding Instances and Amount of Funding (USD Million)
  • Figure 8.7 Most Active Companies: Summary of Funding Raised by Type of Funding and Amount of Funding (USD Million)
  • Figure 8.8 Most Active Investors: Distribution by Number of Funding Instances
  • Figure 8.9 Funding and Investments: Geographical Distribution by Amount Invested (USD Million)
  • Figure 9.1 Company Valuation Analysis: A/F Ratio, Input Dataset
  • Figure 9.2 Company Valuation Analysis: A/Y Ratio, Input Dataset
  • Figure 9.3 Company Valuation Analysis: A/E Ratio, Input Dataset
  • Figure 9.4 Company Valuation Analysis: Categorization by Twitter Followers Score
  • Figure 9.5 Company Valuation Analysis: Categorization by Google Hits Score
  • Figure 9.6 Company Valuation Analysis: Categorization by Partnerships Score
  • Figure 9.7 Company Valuation Analysis: Categorization by Weighted Average Score
  • Figure 9.8 Company Valuation Analysis: Unicorns in Deep Learning in Medical Image Processing Sector
  • Figure 10.1 Clinical Trial Analysis: Distribution by Trial Recruitment Status
  • Figure 10.2 Clinical Trial Analysis: Cumulative Distribution by Trial Registration Year, Pre-2016 - Q1 2020
  • Figure 10.3 Clinical Trial Analysis: Distribution by Trial Recruitment Status and Trial Registration Year
  • Figure 10.4 Clinical Trial Analysis: Distribution by Trial Registration Year and Patient Enrollment, 2007-Q1 2020
  • Figure 10.5 Clinical Trial Analysis: Distribution by Study Design
  • Figure 10.6 Clinical Trial Analysis: Distribution by Patient Segment
  • Figure 10.7 Clinical Trial Analysis: Distribution by Therapeutic Area
  • Figure 10.8 Clinical Trial Analysis: Distribution by Trial Objective
  • Figure 10.9 Clinical Trial Analysis: Focus Areas
  • Figure 10.10 Clinical Trial Analysis: Distribution by Type of Image Processed
  • Figure 10.11 Clinical Trial Analysis: Distribution by Type of Sponsors / Collaborators
  • Figure 10.12 Leading Sponsors / Collaborators: Analysis by Number of Trials
  • Figure 10.13 Clinical Trial Analysis: Geographical Distribution of Trials
  • Figure 10.14 Clinical Trial Analysis: Geographical Distribution of Trials and Patient Population
  • Figure 11.1 Deep Learning in Medical Image Processing, Patent Portfolio: Distribution by Type of Patent
  • Figure 11.2 Deep Learning in Medical Image Processing, Patent Portfolio: Distribution by Application Year and Publication Year
  • Figure 11.3 Deep Learning in Medical Image Processing, Patent Portfolio: Distribution by Issuing Authority / Patent Offices Involved
  • Figure 11.4 Deep Learning in Medical Image Processing, Patent Portfolio: North America
  • Figure 11.5 Deep Learning in Medical Image Processing, Patent Portfolio: Europe
  • Figure 11.6 Deep Learning in Medical Image Processing, Patent Portfolio: Asia Pacific and RoW
  • Figure 11.7 Deep Learning in Medical Image Processing, Patent Portfolio: Distribution by IPCR Symbols
  • Figure 11.8 Deep Learning in Medical Image Processing, Patent Portfolio: Focus Areas
  • Figure 11.9 Deep Learning in Medical Image Processing, Patent Portfolio: Leading Assignees (Industry Players)
  • Figure 11.10 Deep Learning in Medical Image Processing, Patent Portfolio: Leading Assignees (Non-Industry Players)
  • Figure 11.11 Deep Learning in Medical Image Processing, Patent Portfolio: Leading Industry Players (Benchmarking by Patent Characteristics)
  • Figure 11.12 Deep Learning in Medical Image Processing, Patent Portfolio: Distribution by Patent Age
  • Figure 11.13 Deep Learning in Medical Image Processing, Patent Portfolio: Valuation Analysis
  • Figure 12.1 Deep Learning in Medical Image Processing: Efficiency Profile of Radiologists
  • Figure 12.2 Overall Cost Saving Potential of Deep Learning in Medical Image Processing Solutions: Growth Scenarios
  • Figure 12.3 Overall Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (X-Ray Images), 2020-2030 (USD Billion)
  • Figure 12.4 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (X-Ray Images) in North America, 2020-2030 (USD Billion)
  • Figure 12.5 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (X-Ray Images) in Europe, 2020-2030 (USD Billion)
  • Figure 12.6 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (X-Ray Images) in Asia Pacific and RoW, 2020-2030 (USD Billion)
  • Figure 12.7 Cost Saving Potential of Deep Learning-based Medical Image Processing Solutions (X-Ray Images) in High Income Countries, 2020-2030 (USD Billion)
  • Figure 12.8 Cost Saving Potential of Deep Learning-based Medical Image Processing Solutions (X-Ray Images) in Middle Income Countries, 2020-2030 (USD Billion)
  • Figure 12.9 Overall Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (MRI Images), 2020-2030 (USD Billion)
  • Figure 12.10 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (MRI Images) in North America, 2020-2030 (USD Billion)
  • Figure 12.11 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (MRI Images) in Europe, 2020-2030 (USD Billion)
  • Figure 12.12 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (MRI Images) in Asia Pacific and RoW, 2020-2030 (USD Billion)
  • Figure 12.13 Cost Saving Potential of Deep Learning-based Medical Image Processing Solutions (MRI Images) in High Income Countries, 2020-2030 (USD Billion)
  • Figure 12.14 Cost Saving Potential of Deep Learning-based Medical Image Processing Solutions (MRI Images) in Middle Income Countries, 2020-2030 (USD Billion)
  • Figure 12.15 Overall Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (CT Images), 2020-2030 (USD Billion)
  • Figure 12.16 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (CT Images) in North America, 2020-2030 (USD Billion)
  • Figure 12.17 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (CT Images) in Europe, 2020-2030 (USD Billion)
  • Figure 12.18 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (CT Images) in Asia Pacific and RoW, 2020-2030 (USD Billion)
  • Figure 12.19 Cost Saving Potential of Deep Learning-based Medical Image Processing Solutions (CT Images) in High Income Countries, 2020-2030 (USD Billion)
  • Figure 12.20 Cost Saving Potential of Deep Learning-based Medical Image Processing Solutions (CT Images) in Middle Income Countries, 2020-2030 (USD Billion)
  • Figure 12.21 Overall Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (Ultrasound Images), 2020-2030 (USD Billion)
  • Figure 12.22 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (Ultrasound Images) in North America, 2020-2030 (USD Billion)
  • Figure 12.23 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (Ultrasound Images) in Europe, 2020-2030 (USD Billion)
  • Figure 12.24 Cost Saving Potential of Deep Learning in Medical Image Processing Solutions (Ultrasound Images) in Asia Pacific and RoW, 2020-2030 (USD Billion)
  • Figure 12.25 Cost Saving Potential of Deep Learning-based Medical Image Processing Solutions (Ultrasound Images) in High Income Countries, 2020-2030 (USD Billion)
  • Figure 12.26 Cost Saving Potential of Deep Learning-based Medical Image Processing Solutions (Ultrasound Images) in Middle Income Countries, 2020-2030 (USD Billion)
  • Figure 13.1 Overall Deep Learning in Medical Image Processing Market, 2020-2030 (USD Million)
  • Figure 13.2 Deep Learning in Medical Image Processing Market: Distribution by Application Area, 2020-2030 (USD Million)
  • Figure 13.3 Deep Learning in Medical Image Processing Market for Brain Abnormalities / Neurological Disorders, 2020-2030 (USD Million)
  • Figure 13.4 Deep Learning in Medical Image Processing Market for Cardiac Abnormalities / Cardiovascular Disorders, 2020-2030 (USD Million)
  • Figure 13.5 Deep Learning in Medical Image Processing Market for Breast Cancer, 2020-2030 (USD Million)
  • Figure 13.6 Deep Learning in Medical Image Processing Market for Bone Deformities / Orthopedic Disorders, 2020-2030 (USD Million)
  • Figure 13.7 Deep Learning in Medical Image Processing Market for Lung Infections / Lung Disorders, 2020-2030 (USD Million)
  • Figure 13.8 Deep Learning in Medical Image Processing Market for Other Disorders, 2020-2030 (USD Million)
  • Figure 13.9 Deep Learning in Medical Image Processing Market: Distribution by Type of Image Processed, 2020-2030 (USD Million)
  • Figure 13.10 Deep Learning in Medical Image Processing Market for X-Rays, 2020-2030 (USD Million)
  • Figure 13.11 Deep Learning in Medical Image Processing Market for MRI, 2020-2030 (USD Million)
  • Figure 13.12 Deep Learning in Medical Image Processing Market for CT, 2020-2030 (USD Million)
  • Figure 13.13 Deep Learning in Medical Image Processing Market for Ultrasound, 2020-2030 (USD Million)
  • Figure 13.14 Deep Learning in Medical Image Processing Market: Distribution by Key Geographical Regions, 2020-2030 (USD Million)
  • Figure 13.15 Deep Learning in Medical Image Processing Market in North America, 2020-2030 (USD Million)
  • Figure 13.16 Deep Learning in Medical Image Processing Market in Europe, 2020-2030 (USD Million)
  • Figure 13.17 Deep Learning in Medical Image Processing Market in Asia Pacific / RoW, 2020-2030 (USD Million)
  • Figure 13.18 Concluding Remarks
  • Figure 14.1 Deep Learning in Healthcare: Other Expert Insights
  • Figure 16.1 Opportunity for Deep Learning in Medical Image Processing Market, 2015-2030 (COVID Impact Scenario)
  • Figure 17.1 Concluding Remarks: Current Market Landscape
  • Figure 17.2 Concluding Remarks: Partnerships and Collaborations
  • Figure 17.3 Concluding Remarks: Funding and Investments
  • Figure 17.4 Concluding Remarks: Company Valuation
  • Figure 17.5 Concluding Remarks: Clinical Trials
  • Figure 17.6 Concluding Remarks: Patents
  • Figure 17.7 Concluding Remarks: Cost Saving Potential
  • Figure 17.8 Concluding Remarks: Market Forecast and Opportunity
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