Artificial intelligence manages congenital cataract with individualized prediction and telehealth computing

Artificial intelligence manages congenital cataract with individualized prediction and telehealth computing

Abstract A challenge of chronic diseases that remains to be solved is how to liberate patients and medical resources from the burdens of long-term monitoring and periodic visits. Precise management based on artificial intelligence (AI) holds great promise; however, a clinical application that fully...

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Autores principales: Erping Long, Jingjing Chen, Xiaohang Wu, Zhenzhen Liu, Liming Wang, Jiewei Jiang, Wangting Li, Yi Zhu, Chuan Chen, Zhuoling Lin, Jing Li, Xiaoyan Li, Hui Chen, Chong Guo, Lanqin Zhao, Daoyao Nie, Xinhua Liu, Xin Liu, Zhe Dong, Bo Yun, Wenbin Wei, Fan Xu, Jian Lv, Min Li, Shiqi Ling, Lei Zhong, Junhong Chen, Qishan Zheng, Li Zhang, Yi Xiang, Gang Tan, Kai Huang, Yifan Xiang, Duoru Lin, Xulin Zhang, Meimei Dongye, Dongni Wang, Weirong Chen, Xiyang Liu, Haotian Lin, Yizhi Liu
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2020
Materias:
Computer applications to medicine. Medical informatics
R858-859.7
Acceso en línea:https://doaj.org/article/737f5b0004e146a7bec22cf2d79b21a2
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Sumario:Abstract A challenge of chronic diseases that remains to be solved is how to liberate patients and medical resources from the burdens of long-term monitoring and periodic visits. Precise management based on artificial intelligence (AI) holds great promise; however, a clinical application that fully integrates prediction and telehealth computing has not been achieved, and further efforts are required to validate its real-world benefits. Taking congenital cataract as a representative, we used Bayesian and deep-learning algorithms to create CC-Guardian, an AI agent that incorporates individualized prediction and scheduling, and intelligent telehealth follow-up computing. Our agent exhibits high sensitivity and specificity in both internal and multi-resource validation. We integrate our agent with a web-based smartphone app and prototype a prediction-telehealth cloud platform to support our intelligent follow-up system. We then conduct a retrospective self-controlled test validating that our system not only accurately detects and addresses complications at earlier stages, but also reduces the socioeconomic burdens compared to conventional methods. This study represents a pioneering step in applying AI to achieve real medical benefits and demonstrates a novel strategy for the effective management of chronic diseases.

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