RT Journal Article
SR Electronic
T1 Universal artificial intelligence platform for collaborative management of cataracts
JF British Journal of Ophthalmology
JO Br J Ophthalmol
FD BMJ Publishing Group Ltd.
SP bjophthalmol-2019-314729
DO 10.1136/bjophthalmol-2019-314729
A1 Xiaohang Wu
A1 Yelin Huang
A1 Zhenzhen Liu
A1 Weiyi Lai
A1 Erping Long
A1 Kai Zhang
A1 Jiewei Jiang
A1 Duoru Lin
A1 Kexin Chen
A1 Tongyong Yu
A1 Dongxuan Wu
A1 Cong Li
A1 Yanyi Chen
A1 Minjie Zou
A1 Chuan Chen
A1 Yi Zhu
A1 Chong Guo
A1 Xiayin Zhang
A1 Ruixin Wang
A1 Yahan Yang
A1 Yifan Xiang
A1 Lijian Chen
A1 Congxin Liu
A1 Jianhao Xiong
A1 Zongyuan Ge
A1 Dingding Wang
A1 Guihua Xu
A1 Shaolin Du
A1 Chi Xiao
A1 Jianghao Wu
A1 Ke Zhu
A1 Danyao Nie
A1 Fan Xu
A1 Jian Lv
A1 Weirong Chen
A1 Yizhi Liu
A1 Haotian Lin
YR 2019
UL http://bjo.bmj.com/content/early/2019/09/25/bjophthalmol-2019-314729.abstract
AB 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.