1. | EXECUTIVE SUMMARY |
1.1. | Report scope |
1.2. | Image recognition AI in medical imaging |
1.3. | Drivers & constraints of AI in medical imaging |
1.4. | Benefits of using AI in medical diagnostics |
1.5. | Clinical applications of image recognition AI covered in this report |
1.6. | Investments into image recognition AI companies by disease application |
1.7. | Image recognition AI: Performance comparison by application |
1.8. | Cancer detection AI: State of development and market readiness |
1.9. | Cancer detection AI companies: State of product development |
1.10. | Cancer detection AI: Conclusions and outlook |
1.11. | Cancer detection AI: Conclusions and outlook (2) |
1.12. | CVD detection AI: State of development and market readiness |
1.13. | CVD detection AI companies: State of product development |
1.14. | CVD detection AI: Conclusions and outlook |
1.15. | Respiratory diseases detection AI: State of development and market readiness |
1.16. | Respiratory diseases detection AI companies: State of product development |
1.17. | Respiratory diseases detection AI: Conclusions and outlook |
1.18. | Retinal diseases detection AI: State of development and market readiness |
1.19. | Retinal diseases detection AI companies: State of product development |
1.20. | Retinal diseases detection AI: Conclusions and outlook |
1.21. | Retinal diseases detection AI: Conclusions and outlook (2) |
1.22. | NDD detection AI: State of development and market readiness |
1.23. | NDD detection AI companies: State of product development |
1.24. | NDD detection AI: Conclusions and outlook |
1.25. | Image recognition AI: Technological roadmap |
1.26. | Image recognition AI: Roadmap of factors limiting penetration |
1.27. | Image recognition AI: Market penetration 2020-2040 |
1.28. | Market forecast 2020-2031 by disease application |
1.29. | AI provides real value and the market is rapidly growing |
1.30. | Remaining challenges |
1.31. | Opportunities for technological improvements |
1.32. | Why do image recognition AI companies struggle to achieve profitability? |
2. | INTRODUCTION |
2.1. | Report scope |
2.2. | Medical imaging advances diagnostics |
2.3. | Types of medical imaging |
2.4. | Uses, pros and cons of each type of imaging |
2.5. | X-radiation (X-ray) |
2.6. | Computed tomography (CT) |
2.7. | Positron emission tomography (PET) |
2.8. | Magnetic resonance imaging (MRI) |
2.9. | Ultrasound |
2.10. | Imaging devices: Regulations & path to approval |
2.11. | Radiation from imaging devices: Safety regulations |
2.12. | Image recognition AI in medical imaging |
2.13. | Drivers & constraints of AI in medical imaging |
2.14. | AI in healthcare: Existing regulations |
2.15. | AI in healthcare: Regulations & path to approval |
2.16. | Clinical applications of image recognition AI covered in this report |
2.17. | Interest in AI and deep learning has soared in the last five years... |
2.18. | ... And so have investments into image recognition AI companies |
2.19. | CVD and cancer have generated the most funding |
3. | ARTIFICIAL INTELLIGENCE, DEEP LEARNING AND CONVOLUTIONAL NEURAL NETWORKS |
3.1. | What is artificial intelligence (AI)? Terminologies explained |
3.2. | The two main types of AI in healthcare |
3.3. | Requirements for AI in medical imaging |
3.4. | Main deep learning (DL) approaches |
3.5. | DL makes automated image recognition possible |
3.6. | Image recognition AI is based on convolutional neural networks (CNNs) |
3.7. | Workings of CNNs: How are images processed? |
3.8. | Workings of CNNs: Another example |
3.9. | Common CNN architectures for image recognition |
3.10. | Milestones in DL: Image recognition surpasses human level |
3.11. | How do image recognition AI algorithms learn to detect disease? |
3.12. | The depth and variation of training data dictate the robustness of image recognition AI algorithms |
3.13. | Assessing algorithm performance: The importance of true/false positives/negatives |
3.14. | Measures in deep learning: Sensitivity and Specificity |
3.15. | DL algorithms assess the rate of true/false positives/negatives to determine sensitivity and specificity |
3.16. | Measures in deep learning: Area Under Curve (AUC) or area under curve of receiver operating characteristics (AUCROC) |
3.17. | When AUC is not a good measure of the algorithm success? |
3.18. | Measures in deep learning: Reproducibility |
3.19. | F1 Score |
3.20. | Benefits of using AI in medical diagnostics |
3.21. | Drivers of image recognition AI usage |
3.22. | Limiting factors of image recognition AI using CNNs |
4. | CANCER |
4.1. | Image recognition enhances cancer diagnostic solutions |
4.2. | Investments into cancer detection AI companies |
4.3. | Image recognition AI for cancer detection: Key players |
4.4. | Breast cancer |
4.5. | Breast cancer: Detection and quantification of breast densities via mammography (2018) |
4.6. | Breast cancer screening via mammograms and pathology slides |
4.7. | Lunit: Breast cancer screening via mammography |
4.8. | Reproducible breast cancer screening: Densitas, Kheiron Medical, and Therapixel |
4.9. | Therapixel: Early breast cancer detection |
4.10. | CureMetrix: AI estimates the risk of disease |
4.11. | Google: Surpassing human performance regardless of patient population type |
4.12. | Google: Surpassing human performance regardless of patient population type (2) |
4.13. | On the market: Intrasense, ScreenPoint Medical, Qlarity Imaging and Koios Medical |
4.14. | Currently on the market or upcoming: Qview Medical, PathAI and Zebra Medical Vision |
4.15. | AI performance comparison: Methodology |
4.16. | Breast cancer detection AI: Performance comparison |
4.17. | Breast cancer detection AI: Performance comparison (2) |
4.18. | Lung cancer |
4.19. | Lung cancer: NYU uses DL on lung cancer histopathological images to identify cancer cells, determine their type, and predict what somatic mutations are present in the tumour |
4.20. | Lung cancer: Detection made easier |
4.21. | Infervision: Detecting nodules three times faster than radiologists |
4.22. | Enlitic: Identifying malignant lung nodules 18 months sooner |
4.23. | Arterys: Accelerating reading time by 45% |
4.24. | Additional players: VUNO, Lunit, Intrasense & VoxelCloud |
4.25. | Additional players: Behold.ai, Aidence, Mindshare Medical & Riverain Technologies |
4.26. | Lung cancer detection AI: Performance comparison |
4.27. | Lung cancer detection AI: Performance comparison (2) |
4.28. | Skin cancer |
4.29. | Skin cancer: Key players |
4.30. | Skin cancer: Machine learning algorithms |
4.31. | Skin cancer: The ABCDE criteria |
4.32. | Skin cancer: Dermoscopic melanoma recognition (2018) |
4.33. | Skin cancer: Dermoscopic melanoma recognition and its challenges |
4.34. | Miiskin: Tracking skin changes over time |
4.35. | SkinVision: Risk assessment and unparalleled accuracy at a low cost |
4.36. | MetaOptima: Medical grade image quality for the consumer |
4.37. | Stanford University: Automated classification of skin lesions |
4.38. | Mole mapping apps track skin changes over time: SkinIO, Skin Analytics & University of Michigan |
4.39. | Skin cancer detection AI: Performance comparison |
4.40. | Skin cancer detection AI: Performance comparison (2) |
4.41. | Thyroid cancer: AmCad BioMed automatically identifies nodules |
4.42. | Prostate cancer: Cortechs Labs improves a key visualisation and quantification method |
4.43. | Prostate cancer: Intrasense and YITU Technology |
4.44. | Microsoft: Using AI for cancer detection, radiotherapy planning and outcome monitoring |
4.45. | AI-driven histological analysis of tissue slides for cancer detection: Paige & Primaa |
4.46. | Cancer detection AI: Performance comparison |
4.47. | Cancer detection AI: State of development and market readiness |
4.48. | Cancer detection AI applications: State of development |
4.49. | Cancer detection AI companies: State of product development |
4.50. | Cancer detection AI companies: Software complexity |
4.51. | Conclusions and outlook |
4.52. | Conclusions and outlook (2) |
5. | CARDIOVASCULAR DISEASE |
5.1. | What is cardiovascular disease (CVD) and where does image recognition AI apply? |
5.2. | AI can provide solutions to improve CVD management |
5.3. | Investments into CVD detection AI companies |
5.4. | Using imaging & AI to detect clots and blockages |
5.5. | Key players |
5.6. | Stroke |
5.7. | Stroke detection AI: Key players |
5.8. | MIT: A DL solution for stroke detection from CT scans |
5.9. | iSchemaView: Categorising the extent and location of ischemic injury up to 30 hours post-symptoms onset |
5.10. | Infervision: Dynamic and risk assessment of active bleeding |
5.11. | MaxQ AI: Near real-time detection, triage and annotation of stroke injury |
5.12. | Qure.ai: Identifying 5 types of intracranial haemorrhages |
5.13. | Other stroke detection companies: Aidoc, Zebra Medical Vision and Quantib |
5.14. | Stroke detection AI: Performance comparison |
5.15. | Stroke detection AI: Performance comparison (2) |
5.16. | Coronary heart disease (CHD) & myocardial infarction |
5.17. | CHD detection AI: Key players |
5.18. | CHD: Cornell & NYU's DL approach to diagnosis |
5.19. | HeartFlow: Assessing the impact of coronary blockages on cardiac blood supply |
5.20. | Circle Cardiovascular Imaging: Automated plaque assessment |
5.21. | Other CHD detection AI companies: Intrasense, CASIS & VoxelCloud |
5.22. | Assessing blood flow |
5.23. | Assessing blood flow: Key players |
5.24. | Arterys: Quantifying blood flow in minutes |
5.25. | Pie Medical Imaging: Calculating blood flow from 3D phase-contrast MR images |
5.26. | On the market: NeoSoft, HeartFlow, iSchemaView & Circle Cardiovascular Imaging |
5.27. | Blood flow detection AI: Performance comparison |
5.28. | Cardiac function |
5.29. | Ejection fraction is key for evaluating cardiac function, and AI allows for more accurate measurements |
5.30. | Cardiac function detection AI: Key players |
5.31. | Philips: Assessing cardiac performance, strength and structure |
5.32. | NeoSoft: automated segmentation for cardiac function and myocardial characterisation |
5.33. | Other cardiac function players: TomTec, DiA Imaging Analysis, GE Healthcare & BioMedical Image Analysis Group |
5.34. | Cardiac function detection AI: Performance comparison |
5.35. | CVD detection AI: Performance comparison |
5.36. | CVD detection AI: State of development and market readiness |
5.37. | CVD detection AI applications: State of development |
5.38. | CVD detection AI companies: State of product development |
5.39. | CVD detection AI companies: Software complexity |
5.40. | Conclusions and outlook |
6. | RESPIRATORY DISEASES |
6.1. | How can AI improve respiratory disease diagnosis? |
6.2. | Investments into respiratory diseases detection AI companies |
6.3. | Key players |
6.4. | VIDA: Identifying asthma and COPD |
6.5. | Infervision: Level of pneumonia infection as a percentage |
6.6. | SemanticMD: Probability score for tuberculosis |
6.7. | Lunit: Algorithm detects 9 different respiratory disorders |
6.8. | VUNO: Cutting image reading time by half |
6.9. | Arterys: Displaying negative findings for rule out support |
6.10. | Qure.ai: Detecting multiple chest abnormalities |
6.11. | AI embedded into imaging device: GE Healthcare |
6.12. | On the market: Aidoc, Zebra Medical Vision, Intrasense & Behold.ai |
6.13. | In development: Artelus, Enlitic & SigTuple |
6.14. | Respiratory diseases detection AI: Performance comparison |
6.15. | Respiratory diseases detection AI: Algorithm comparison (2) |
6.16. | COVID-19 |
6.17. | COVID-19: Key players |
6.18. | COVID-19: Infervision |
6.19. | COVID-19: Other companies |
6.20. | COVID-19 detection AI: Performance comparison |
6.21. | COVID-19 detection AI: Algorithm comparison (2) |
6.22. | Respiratory diseases detection AI: Performance comparison |
6.23. | Respiratory diseases detection AI: State of development and market readiness |
6.24. | Respiratory diseases detection AI applications: State of development |
6.25. | Respiratory diseases detection AI companies: State of product development |
6.26. | Respiratory diseases detection AI companies: Software complexity |
6.27. | Conclusions and outlook |
7. | RETINAL DISEASES |
7.1. | What are retinal diseases and how are they detected? |
7.2. | AI can reach expert level of disease detection in 10 days, compared to 20 years for humans |
7.3. | Investments into retinal diseases detection AI companies |
7.4. | Key players |
7.5. | Artelus: Detecting DR by ensuring image quality |
7.6. | VUNO: Identifying 12 types of eye disorders |
7.7. | SemanticMD: AI solution for use offline |
7.8. | SigTuple: Applying AI to multiple imaging modalities |
7.9. | Pr3vent: Detects 50+ pathologies in newborns |
7.10. | Currently in clinical trials: Novai, Verily & Capital University of Medical Sciences |
7.11. | On the market or upcoming: VoxelCloud, Singapore National Eye Centre & CERA |
7.12. | Retinal diseases detection AI: Performance comparison |
7.13. | Retinal diseases detection AI: Performance comparison (2) |
7.14. | Retinal diseases detection AI: Performance comparison (3) |
7.15. | Retinal diseases detection AI: State of development and market readiness |
7.16. | Retinal diseases detection AI companies: State of product development |
7.17. | Retinal diseases detection AI companies: Software complexity |
7.18. | Conclusions and outlook |
7.19. | Conclusions and outlook (2) |
8. | NEURODEGENERATIVE DISEASES |
8.1. | AI can identify signs of dementia years before its onset |
8.2. | Investments into neurodegenerative diseases detection AI companies |
8.3. | Key players |
8.4. | Quantib: Measuring brain size and atrophy |
8.5. | Icometrix: Diagnosing various NDDs |
8.6. | Cortechs Labs: Automated quantification of brain structure volume |
8.7. | Avalon AI: Interpreting multiple MRI modalities |
8.8. | VUNO: Immediate segmentation and parcellation |
8.9. | University of Bari: Predicting Alzheimer's disease up to a decade before onset |
8.10. | On the market: Qure.ai & Siemens Healthineers |
8.11. | In development: IDx, Icahn School of Medicine, UCSF |
8.12. | Research only: BioMedical Image Group, Imperial College London & University of Edinburgh |
8.13. | Research only: McGill & University College London |
8.14. | NDD detection AI: Performance comparison |
8.15. | NDD detection AI: Performance comparison (2) |
8.16. | NDD detection AI: Performance comparison (3) |
8.17. | NDD detection AI: State of development and market readiness |
8.18. | NDD detection AI companies: State of product development |
8.19. | NDD detection AI companies: Software complexity |
8.20. | Conclusions and outlook |
9. | MARKET ANALYSIS |
9.1. | Geographic segmentation: Almost 50% of medical diagnostics AI companies are based in the USA |
9.2. | Modality segmentation: Over half of medical diagnostics AI companies focus on CT and X-ray imaging |
9.3. | Image recognition AI: Technological roadmap |
9.4. | Image recognition AI: Technological roadmap (2) |
9.5. | Image recognition AI: Technological roadmap (3) |
9.6. | Image recognition AI: Roadmap of factors limiting penetration |
9.7. | Image recognition AI: Roadmap of factors limiting penetration (2) |
9.8. | Image recognition AI: Roadmap of factors limiting penetration (3) |
9.9. | Market analysis methodology |
9.10. | Addressable markets are growing, with some exceptions, and AI use is expected to mirror this trend |
9.11. | Scan volume per year: AI use will rise as its adoption increases |
9.12. | Image recognition AI: Market penetration 2020-2040 |
9.13. | Image recognition AI: Market penetration 2020-2040 (2) |
9.14. | Image recognition AI: Market penetration 2020-2040 (3) |
9.15. | Image recognition AI: Market penetration 2020-2040 (4) |
9.16. | Business models: Subscription vs Pay Per Use |
9.17. | Market share in 2019: CVD detection |
9.18. | Market forecast 2020-2031 by disease application |
9.19. | Market forecast 2020-2031: CVD detection |
9.20. | Market forecast 2020-2031: Cancer detection |
10. | CONCLUSIONS & OUTLOOK |
10.1. | AI provides real value and the market is rapidly growing |
10.2. | Remaining challenges: Improving data curation and algorithm training procedures |
10.3. | Remaining challenges: Need for clearer images |
10.4. | Remaining challenges: Regulations and data privacy |
10.5. | Opportunities for technological improvements |
10.6. | Cloud-based vs offline software |
10.7. | Moving towards equipment-integrated AI software? |
10.8. | Why do image recognition AI companies struggle to achieve profitability? |
11. | LIST OF COMPANIES |
11.1. | Cancer detection AI |
11.2. | CVD detection AI |
11.3. | Respiratory diseases detection AI |
11.4. | Retinal diseases detection AI |
11.5. | Neurodegenerative diseases detection AI |