Universal artificial intelligence platform for collaborative management of cataracts

Xiaohang Wu, Yelin Huang, Zhenzhen Liu, Weiyi Lai, Erping Long, Kai Zhang, Jiewei Jiang, Duoru Lin, Kexin Chen, Tongyong Yu, Dongxuan Wu, Cong Li, Yanyi Chen, Minjie Zou, Chuan Chen, Yi Zhu, Chong Guo, Xiayin Zhang, Ruixin Wang, Yahan YangYifan Xiang, Lijian Chen, Congxin Liu, Jianhao Xiong, Zongyuan Ge, Dingding Wang, Guihua Xu, Shaolin Du, Chi Xiao, Jianghao Wu, Ke Zhu, Danyao Nie, Fan Xu, Jian Lv, Weirong Chen, Yizhi Liu, Haotian Lin

Research output: Contribution to journalArticleResearchpeer-review

15 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1553-1560
Number of pages8
JournalBritish Journal of Ophthalmology
Volume103
Issue number11
DOIs
Publication statusPublished - Nov 2019

Keywords

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

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