Dataset Information

Universal artificial intelligence platform for collaborative management of cataracts.

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.

SUBMITTER: Wu X

PROVIDER: S-EPMC6855787 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Universal artificial intelligence platform for collaborative management of cataracts.

Wu Xiaohang X Huang Yelin Y Liu Zhenzhen Z Lai Weiyi W Long Erping E Zhang Kai K Jiang Jiewei J Lin Duoru D Chen Kexin K Yu Tongyong T Wu Dongxuan D Li Cong C Chen Yanyi Y Zou Minjie M Chen Chuan C Zhu Yi Y Guo Chong C Zhang Xiayin X Wang Ruixin R Yang Yahan Y Xiang Yifan Y Chen Lijian L Liu Congxin C Xiong Jianhao J Ge Zongyuan Z Wang Dingding D Xu Guihua G Du Shaolin S Xiao Chi C Wu Jianghao J Zhu Ke K Nie Danyao D Xu Fan F Lv Jian J Chen Weirong W Liu Yizhi Y Lin Haotian H

The British journal of ophthalmology 20190902 11

<h4>Purpose</h4>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.<h4>Methods</h4>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 u ...[more]

PMID: 31481392

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Universal artificial intelligence platform for collaborative management of cataracts.

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Universal artificial intelligence platform for collaborative management of cataracts.

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