Abstract
Abstract
Background
Screening for hyperthyroidism using gold-standard diagnostic criteria in the general population is not cost-effective, leading to a relatively high rate of undiagnosed and untreated patients. This study aimed to establish a deep learning-based system to detect hyperthyroidism based on retinal photographs.
Methods
The multicenter, observational study included retinal photographs taken from participants in two hospitals and 24 health care centers throughout China. We first trained two models to identify hyperthyroidism: in model #1, the non-hyperthyroidism individuals were randomly selected, while in model #2, the non-hyperthyroidism group was matched for age and gender with the hyperthyroidism group. After internal validation, we selected the better model for further evaluation using external validation datasets.
Results
The study included 22,940 retinal photographs of 11,409 participants for the model development, and 3862 retinal photographs (1870 participants) which were obtained from two hospitals and four medical centers as the external validation datasets. Model #1 achieved a higher area under the receiver operator curve (AUC) than model #2 (0.907, 95% CI: 0.894–0.918 versus 0.850, 95% CI: 0.832–0.866) in the internal validation so that model #1 was used for further evaluation. In external datasets, model #1 reached AUCs ranging from 0.816 (95% CI 0.789–0.846) to 0.849 (95% CI 0.824–0.874) and achieved accuracies between 0.735 (95% CI 0.700–0.773) and 0.796 (95% CI 0.765–0.824). Heatmaps showed a focus of the DL-algorism on large fundus vessels and the optic nerve head.
Conclusions
Retinal fundus photographs may serve for DL systems for a cost-effective and non-invasive method to detect hyperthyroidism.
Funder
Research Foundation of Beijing Friendship Hospital, Capital Medical University
Capital Health Research and Development of Special Fund
Science & Technology Project of Beijing Municipal Science & Technology Commission
Beijing Municipal Administration of Hospitals’ Ascent Plan
National Natural Science Foundation of China
Special Fund of the Pediatric Medical Coordinated Development Center of Beijing Hospitals Authority
Publisher
Springer Science and Business Media LLC
Subject
Information Systems and Management,Computer Networks and Communications,Hardware and Architecture,Information Systems