31.01.2020 |
Xiaohang Wu, Yelin Huang, Zhenzhen Liu, Weiyi Lai, Erping Long, Kai Zhang, Jiewei Jian3, Duoru Lin, Kexin Chen, Tongyong Yu, Dongxuan Wu, Cong Li, Yanyi Chen, Minjie Zou, Chuan Chen, Yi Zhu, Chong Guo, Xiayin Zhang, Ruixin Wang, Yahan Yang, Yifan Xiang, Lijian Chen, Congxin Liu, Jianhao Xiong, Zongyuan Ge, Dingding Wang, Guihua Xu, Shaolin Du, Chi Xiao, Jianghao Wu, Ke Zhu, Danyao Nie, Fan Xu, Jian Lv, Weirong Chen, Yizhi Liu, Haotian Lin
We 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.
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: capture mode recognition; cataract diagnosis as a normal lens, cataract or a postoperative eye and 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.
British Journal of Ophthalmology, Volume 103, Issue 11