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

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    August 2020

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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?

Contents

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.

Chapter 19 is an appendix, which contains the list of companies and organizations mentioned 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

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

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