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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Wu, Xiaohang / Huang, Yelin / Liu, Zhenzhen / Lai, Weiyi / Long, Erping / Zhang, Kai / Jiang, Jiewei / Lin, Duoru / Chen, Kexin / Yu, Tongyong / ... [more]

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.

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Title :

Universal artificial intelligence platform for collaborative management of cataracts

Contributors:

Wu, Xiaohang (author) / Huang, Yelin (author) / Liu, Zhenzhen (author) / Lai, Weiyi (author) / Long, Erping (author) / Zhang, Kai (author) / Jiang, Jiewei (author) / Lin, Duoru (author) / Chen, Kexin (author) / Yu, Tongyong (author)

Publication date :

2019-11-01

Remarks:

Wu , X , Huang , Y , Liu , Z , Lai , W , Long , E , Zhang , K , Jiang , J , Lin , D , Chen , K , Yu , T , Wu , D , Li , C , Chen , Y , Zou , M , Chen , C , Zhu , Y , Guo , C , Zhang , X , Wang , R , Yang , Y , Xiang , Y , Chen , L , Liu , C , Xiong , J , Ge , Z , Wang , D , Xu , G , Du , S , Xiao , C , Wu , J , Zhu , K , Nie , D , Xu , F , Lv , J , Chen , W , Liu , Y & Lin , H 2019 , ' Universal artificial intelligence platform for collaborative management of cataracts ' , British Journal of Ophthalmology , vol. 103 , no. 11 , pp. 1553-1560 . https://doi.org/10.1136/bjophthalmol-2019-314729

DOI :

https://doi.org/10.1136/bjophthalmol-2019-314729

Type of media :

Article (Journal)

Type of material :

Electronic Resource

Language :

English

Keywords :

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

Classification :

DDC: