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 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.

Improving the Generalizability of Infantile Cataracts Detection via Deep Learning-Based Lens Partition Strategy and Multicenter Datasets

“…Each slit-lamp image was comprehensively evaluated and labeled by three senior ophthalmologists from three-degree grading: opacity area (extensive vs. limited), density (dense vs. transparent), and location (central vs. peripheral). The definition of the threedegree grading was consistent with previous studies (1,3,28,29).…”

Application of artificial intelligence in cataract management: current and future directions

“…This places a burden on traveling with incremental costs incurred by the patients. Long et al [55] used Bayesian and DL algorithms to create "CC-guardian", an AI platform that comprises a prediction module to first identify patients at high risk of postoperative complications then schedules a follow-up visit at a primary care center based on the risk stratification, and finally, utilizes a telehealth computing module to make a clinical decision regarding treatment options (referral to a specialized care center versus continual primary care follow-up). The model achieved a high level of specificity and sensitivity and marks a breakthrough in the way ophthalmic care can be delivered.…”

Telehealth in Ophthalmology

“…Pre-op screening for corneal refractive disease using AI has been adopted, and AI has even been utilized in planning aspects of cataract surgery (7). For the post-op period, Long et al created CC-guardian, an AI-based smartphone application that provides patients with specific follow-up plans (19).…”