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Universal artificial intelligence platform for collaborative management of cataracts
  1. Xiaohang Wu1,
  2. Yelin Huang2,
  3. Zhenzhen Liu1,
  4. Weiyi Lai1,
  5. Erping Long1,
  6. Kai Zhang3,
  7. Jiewei Jiang3,
  8. Duoru Lin1,
  9. Kexin Chen4,
  10. Tongyong Yu4,
  11. Dongxuan Wu4,
  12. Cong Li4,
  13. Yanyi Chen4,
  14. Minjie Zou4,
  15. Chuan Chen1,5,
  16. Yi Zhu1,5,
  17. Chong Guo1,
  18. Xiayin Zhang1,
  19. Ruixin Wang1,
  20. Yahan Yang1,
  21. Yifan Xiang1,
  22. Lijian Chen2,
  23. Congxin Liu2,
  24. Jianhao Xiong2,
  25. Zongyuan Ge6,
  26. Dingding Wang7,
  27. Guihua Xu7,
  28. Shaolin Du8,
  29. Chi Xiao9,
  30. Jianghao Wu9,
  31. Ke Zhu10,
  32. Danyao Nie11,
  33. Fan Xu12,
  34. Jian Lv12,
  35. Weirong Chen1,
  36. Yizhi Liu1,
  37. Haotian Lin
  1. 1State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
  2. 2Beijing Tulip Partners Technology Co., Ltd, Beijing, China
  3. 3School of Computer Science and Technology, Xidian University, Xi'an, China
  4. 4Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
  5. 5Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, Florida, USA
  6. 6Department of Electrical and Computer Systems Engineering, Faculty of Engineering, Monash University, Melbourne, Victoria, Australia
  7. 7Huizhou Municipal Central Hospital, Huizhou, China
  8. 8Tung Wah Hospital, Sun Yat-sen University, Dongguan, China
  9. 9Dongguan Guangming Ophthalmic Hospital, Dongguan, China
  10. 10Kaifeng Eye Hospital, Kaifeng, China
  11. 11Shenzhen Eye Hospital, Shenzhen Key Laboratory of Ophthalmology, Shenzhen University School of Medicine, Shenzhen, China
  12. 12Department of Ophthalmology, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
  1. Correspondence to Prof. Haotian Lin, State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China; haot.lin{at}hotmail.com

Abstract

Purpose To establish and validate a universal artificial intelligence (AI) platform for collaborative management of cataracts involving multilevel clinical scenarios and explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage.

Methods The training and validation datasets were derived from the Chinese Medical Alliance for Artificial Intelligence, covering multilevel healthcare facilities and capture modes. The datasets were labelled using a three-step strategy: (1) capture mode recognition; (2) cataract diagnosis as a normal lens, cataract or a postoperative eye and (3) detection of referable cataracts with respect to aetiology and severity. Moreover, we integrated the cataract AI agent with a real-world multilevel referral pattern involving self-monitoring at home, primary healthcare and specialised hospital services.

Results The universal AI platform and multilevel collaborative pattern showed robust diagnostic performance in three-step tasks: (1) capture mode recognition (area under the curve (AUC) 99.28%–99.71%), (2) cataract diagnosis (normal lens, cataract or postoperative eye with AUCs of 99.82%, 99.96% and 99.93% for mydriatic-slit lamp mode and AUCs >99% for other capture modes) and (3) detection of referable cataracts (AUCs >91% in all tests). In the real-world tertiary referral pattern, the agent suggested 30.3% of people be ‘referred’, substantially increasing the ophthalmologist-to-population service ratio by 10.2-fold compared with the traditional pattern.

Conclusions The universal AI platform and multilevel collaborative pattern showed robust diagnostic performance and effective service for cataracts. The context of our AI-based medical referral pattern will be extended to other common disease conditions and resource-intensive situations.

  • Diagnostic tests/Investigation
  • Lens and zonules
  • Public health
  • Imaging

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Footnotes

  • YL and HL are joint senior authors.

  • XW and YH contributed equally.

  • Correction notice An author name has been corrected since this paper was published Online First. Zhongyuan Ge has been corrected to Zongyuan Ge.

  • Contributors XW and HL designed the research. XW, YH, ZL, WL, EL, DL, DW, GX, SD, CX, JW, KZ, DN, FX and JL collected the data. XW, KC, TY, DW, CL, YC, MZ, JX, ZG and CL conducted the study. CG, XZ, RW, YY, YX, KZ, JJ, YZ and CC analysed the data. XW and HL co-wrote the manuscript. HL, YL and WC critically revised the manuscript. All authors discussed the results and commented on the manuscript.

  • Funding This study was supported by the National Key Research and Development Programme (2018YFC0116500), the Key Research Plan for the National Natural Science Foundation of China in Cultivation Project (91846109), the Science Foundation of China for Excellent Young Scientists (81822010), the National Natural Science Foundation of China (81770967, 81873675, 81800810), the Science and Technology Planning Projects of Guangdong Province (2019B030316012, 2018B010109008, 2017B030314025), Guangdong Science and Technology Innovation Leading Talents (2017TX04R031) and the Natural Science Foundation of Guangdong Province (2018A030310104).

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Ethics approval Ethical review of the study was performed by the Zhongshan Ophthalmic Center Ethics Review Committee.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data are available on request.

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