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人工智能白内障协同管理的通用平台

WU Xiaohang YU Tongyong WU Dongxuan LI Cong CHEN Yanyi ZOU Minjie CHEN Chuan ZHU Yi GUO Chong ZHANG Xiayin WANG Ruixin HUANG Yelin YANG Yahan XIANG Yifan CHEN Lijian LIU Congxin XIONG Jianhao GE Zongyuan WANG Dingding XU Guihua DU Shaolin XIAO Chi LIU Zhenzhen WU Jianghao ZHU Ke NIE Danyao XU Fan LV Jian CHEN Weirong LIU Yizhi LIN Haotian 《眼科学报》出版团队王厚硕 LAI Weiyi 罗明杰林浩添 LONG Erping ZHANG Kai JIANG Jiewei LIN Duoru CHEN Kexin

眼科学报2023，Vol.38Issue(10)：665-675,11.

眼科学报2023，Vol.38Issue(10)：665-675,11.DOI:10.12419/2308150001

人工智能白内障协同管理的通用平台

Universal artificial intelligence platform for collaborative management of cataracts(authorized Chinese translation)

WU Xiaohang ¹YU Tongyong ²WU Dongxuan ²LI Cong ²CHEN Yanyi ²ZOU Minjie ²CHEN Chuan ³ZHU Yi ³GUO Chong ¹ZHANG Xiayin ¹WANG Ruixin ¹HUANG Yelin ⁴YANG Yahan ¹XIANG Yifan ¹CHEN Lijian ⁴LIU Congxin ⁴XIONG Jianhao ⁴GE Zongyuan ⁵WANG Dingding ⁶XU Guihua ⁶DU Shaolin ⁷XIAO Chi ⁸LIU Zhenzhen ¹WU Jianghao ⁸ZHU Ke ⁹NIE Danyao ¹⁰XU Fan ¹¹LV Jian ¹¹CHEN Weirong ¹LIU Yizhi ¹LIN Haotian ¹《眼科学报》出版团队 ¹²王厚硕 ¹³LAI Weiyi ¹罗明杰 ¹⁴林浩添 ¹²LONG Erping ¹ZHANG Kai ¹⁵JIANG Jiewei ¹⁵LIN Duoru ¹CHEN Kexin²

作者信息

1. State Key Laboratory of Ophthalmology,Zhongshan Ophthalmic Center,Sun Yat-sen University,Guangzhou,China
2. Zhongshan School of Medicine,Sun Yat-sen University,Guangzhou,China
3. State Key Laboratory of Ophthalmology,Zhongshan Ophthalmic Center,Sun Yat-sen University,Guangzhou,China||Department of Molecular and Cellular Pharmacology,University of Miami Miller School of Medicine,Miami,Florida,USA
4. Beijing Tulip Partners Technology Co.,Ltd,Beijing,China
5. Department of Electrical and Computer Systems Engineering,Faculty of Engineering,Monash University,Melbourne,Victoria,Australia
6. Huizhou Municipal Central Hospital,Huizhou,China
7. Tung Wah Hospital,Sun Yat-sen University,Dongguan,China
8. Dongguan Guangming Ophthalmic Hospital,Dongguan,China
9. Kaifeng Eye Hospital,Kaifeng,China
10. Shenzhen Eye Hospital,Shenzhen Key Laboratory of Ophthalmology,Shenzhen University School of Medicine,Shenzhen,China
11. Department of Ophthalmology,People's Hospital of Guangxi Zhuang Autonomous Region,Nanning,China
13. 西安交通大学第一附属医院
14. 中山大学中山眼科中心,眼病防治全国重点实验室,广东省眼科视觉科学重点实验室
15. School of Computer Science and Technology,Xidian University,Xi'an,China
折叠

摘要

Abstract

Objective: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 pattem 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.

引用本文复制引用

WU Xiaohang,YU Tongyong,WU Dongxuan,LI Cong,CHEN Yanyi,ZOU Minjie,CHEN Chuan,ZHU Yi,GUO Chong,ZHANG Xiayin,WANG Ruixin,HUANG Yelin,YANG Yahan,XIANG Yifan,CHEN Lijian,LIU Congxin,XIONG Jianhao,GE Zongyuan,WANG Dingding,XU Guihua,DU Shaolin,XIAO Chi,LIU Zhenzhen,WU Jianghao,ZHU Ke,NIE Danyao,XU Fan,LV Jian,CHEN Weirong,LIU Yizhi,LIN Haotian,《眼科学报》出版团队,王厚硕,LAI Weiyi,罗明杰,林浩添,LONG Erping,ZHANG Kai,JIANG Jiewei,LIN Duoru,CHEN Kexin..人工智能白内障协同管理的通用平台[J].眼科学报,2023,38(10):665-675,11.

基金项目

国家重点研发计划(2018YFC0116500),国家自然科学基金重点研究计划(91846109),国家自然科学优秀青年基金(81822010),国家自然科学基金(81770967,81873675,81800810),广东省科技计划项目(2019B030316012,2018B010109008,2017B030314025),广东科技创新领军人才计划(2017TX04R031),广东省自然科学基金(2018A030310104). （2018YFC0116500）

眼科学报

ISSN：1000-4432

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