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AI Technology for Prevention and Control of Juvenile Myopia

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Presented at 2019眼科发展新引擎高峰论坛于 in Shanghai China

Veröffentlicht in: Gesundheit & Medizin
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AI Technology for Prevention and Control of Juvenile Myopia

  1. 1. AI Technology Helps To Prevent And Control Juvenile Myopia
  2. 2. Healthcare made easy with AI 我们的使命是人工智能让医疗更高效! More Intelligent,Better Care
  3. 3. CONTENT New data measuring the alarming rise in juvenile myopia 01. Myopia Crisis How Airdoc uses AI for Myopia prevention Using AI for reporting and suggesting countermeasures 03. Reporting and Countermeasures Emerging devices and software solutions04. Other Tech Advances 02.Airdoc Myopia Prediction
  4. 4. Optometry results relative to age Median optometry results shift steadily toward myopia, becoming an important social problem.
  5. 5. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 NumberOfPeople Age High myopia Moderate myopia Mild myopia Hyperopia Myopia Distribution (based on hospital visits)
  6. 6. Myopia development in adolescents (ages 3-18) Based on real diopter data of adolescents 2006 vs 2016 2006 2016
  7. 7. CONTENT New data measuring the alarming rise in juvenile myopia 01. Myopia Crisis How Airdoc uses AI for Myopia prevention Using AI for reporting and suggesting countermeasures 03. Reporting and Countermeasures Emerging devices and software solutions04. Other Tech Advances 02.Airdoc Myopia Prediction
  8. 8. AI and Vision Prediction Using one million continuous optometry records on 140K people, Airdoc developed a vision prediction model suitable for Chinese adolescents. Using subject age plus three time-delayed optometry inputs, the model can predict vision changes by year to age 18. Age:10 1st: OD,-0.10 OS,-0.15 2nd : OD,-0.10 OS,-0.15 3rd : OD,-0.11 OS,-0.16 Optometry three times Prediction Results Recurrent Neural Networks Age 10 11 12 13 14 15 16 17 18 OD -0.1 -0.139 -0.179 -0.217 -0.256 -0.294 -0.331 -0.367 -0.402 OS -0.15 -0.189 -0.229 -0.268 -0.306 -0.344 -0.381 -0.417 -0.452
  9. 9. Data 2,467,949 records 519,499 teenagers 14 years range Data distribution: • 70% train, • 20% validation, • 10% test Model Design RNN-based model Training Results Input: Se1 Age1 Con1 Se3 Age3 Con4 Se2 Age2 Con3 SeN AgeN ConN Output: SeN+1 AgeN+1 ConN+1 SeN+2 AgeN+2 ConN+2 Se18 Age18 Con18 … Hyperparameters tuned based on performance on validation set. Learning rate = 0.001 Optimizer = Adam A B C D Following years • Sex • Age • Con AI and Vision Prediction
  10. 10. Workflow: Comprehensive eye health examination for adolescents 儿童青少年屈光度发育预测( 3-18岁, per month) 儿童青少年视力变化数据2006 vs 2016 By scanning QR codes, parents can easily view students' eye health record and continuously track their children's eye health status, eye disease risk, and vision change trends.
  11. 11. CONTENT New data measuring the alarming rise in juvenile myopia 01. Myopia Crisis How Airdoc uses AI for Myopia prevention Using AI for reporting and suggesting countermeasures 03. Reporting and Countermeasures Emerging devices and software solutions04. Other Tech Advances 02.Airdoc Myopia Prediction
  12. 12. Administrative Report for school officials
  13. 13. 0 100 200 300 400 500 600 700 800 900 1000 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 MyopiaIndex Age No control Intervention 900 525 MCT Vision Training Vision Monitor Countermeasures Special Lenses Monitoring Efficacy of Countermeasures
  14. 14. CONTENT New data measuring the alarming rise in juvenile myopia 01. Myopia Crisis How Airdoc uses AI for Myopia prevention Using AI for reporting and suggesting countermeasures 03. Reporting and Countermeasures Emerging devices and software solutions04. Other Tech Advances 02.Airdoc Myopia Prediction
  15. 15. Diopter detection on mobile devices
  16. 16. Diopter detection on mobile devices -Group tracking.
  17. 17. Corneal Karataconis Prediction Corneal Topography Results
  18. 18. LV Prasad+MIT http://www.lvpei.org/services/srujana-centre-innovation/ http://lvpmitra.com/previous-editions/
  19. 19. Seeing AI https://www.microsoft.com/en-us/seeing-ai/
  20. 20. De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., … Ronneberger, O. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine, 24(9), 1342–1350. https://doi.org/10.1038/s41591-018-0107-6
  21. 21. De Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine. 2018;24(9):1342-1350. doi:10.1038/s41591-018-0107-6 Device independent representation of the scan
  22. 22. Lee, Cecilia S., Doug M. Baughman, and Aaron Y. Lee. 2017. “Deep Learning Is Effective for Classifying Normal versus Age- Related Macular Degeneration OCT Images.” Ophthalmology Retina 1 (4): 322–27. https://doi.org/10.1016/j.oret.2016.12.009.
  23. 23. De Fauw, Jeffrey, Joseph R. Ledsam, Bernardino Romera- Paredes, Stanislav Nikolov, Nenad Tomasev, Sam Blackwell, Harry Askham, et al. 2018. “Clinically Applicable Deep Learning for Diagnosis and Referral in Retinal Disease.” Nature Medicine 24 (9): 1342–50. https://doi.org/10.1038/s41591-018-0107-6.
  24. 24. ray@airdoc.com

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