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
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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.