Universal artificial intelligence platform for collaborative management of cataracts
| العنوان: | Universal artificial intelligence platform for collaborative management of cataracts |
|---|---|
| المؤلفون: | Ke Zhu, Jiewei Jiang, Duoru Lin, Erping Long, Yizhi Liu, Xiaohang Wu, Yahan Yang, Dingding Wang, Tongyong Yu, Weiyi Lai, Chen Lijian, Kexin Chen, Cong Li, Ruixin Wang, Shaolin Du, Weirong Chen, Yelin Huang, Danyao Nie, Yanyi Chen, Chi Xiao, Zhongyuan Ge, Xiayin Zhang, Guihua Xu, Minjie Zou, Jianghao Wu, Congxin Liu, Yifan Xiang, Kai Zhang, Dongxuan Wu, Haotian Lin, Fan Xu, Jianhao Xiong, Yi Zhu, Zhenzhen Liu, Jian Lv, Chuan Chen, Chong Guo |
| المصدر: | The British Journal of Ophthalmology |
| بيانات النشر: | BMJ, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Adult, Male, genetic structures, Adolescent, Referral, Common disease, Vision Disorders, Primary health care, Context (language use), Cataract Extraction, Lens and zonules, Slit Lamp Microscopy, Cataract, Imaging, Cellular and Molecular Neuroscience, Cataracts, Artificial Intelligence, Health care, medicine, Humans, Mass Screening, Intersectoral Collaboration, Aged, Aged, 80 and over, Public health, business.industry, Clinical Science, Middle Aged, medicine.disease, eye diseases, Sensory Systems, Ophthalmology, ROC Curve, Area Under Curve, Collaborative management, Female, Diagnostic tests/Investigation, Artificial intelligence, business |
| الوصف: | PurposeTo 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.MethodsThe 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.ResultsThe 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.ConclusionsThe 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. |
| تدمد: | 1468-2079 0007-1161 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::091f4667d4f2a8a4324aed62b83b862a https://doi.org/10.1136/bjophthalmol-2019-314729 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....091f4667d4f2a8a4324aed62b83b862a |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14682079 00071161 |
|---|