Retinal photograph-based deep learning system for detection of hyperthyroidism: a multicenter, diagnostic study

Journal of Big Data

Research

Yuzhong Chen¹  Lie Ju¹  Xin Zhao¹  Chao He¹  Xin Wang¹  Heyan Li²  Zihan Nie²  Li Dong²  Shiqi Hui²  Wenda Zhou²  Xue Jiang²  Wenbin Wei²  Ruiheng Zhang²  Dongmei Li²  Lihua Luo³  Jost B. Jonas⁴  Zongyuan Ge⁵  Jianxiong Gao⁶  Zhaohui Wang⁶ 

[1] Beijing Airdoc Technology Co., Ltd, Beijing, China;Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, 1 Dong Jiao Min Lane, 100730, Beijing, China;Department of Ophthalmology, Beijing Friendship Hospital, Capital Medical University, Beijing, China;Department of Ophthalmology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany;Institute of Molecular and Clinical Ophthalmology Basel, IOB, Basel, Switzerland;Privatpraxis Prof Jonas Und Dr Panda-Jonas, Heidelberg, Germany;Faculty of Engineering, Monash University, Melbourne, VIC, Australia;Faculty of Engineering, ECSE, Monash University, Melbourne, VIC, Australia;iKang Guobin Healthcare Group Co., Ltd, Beijing, China;

关键词: Artificial intelligence;    Deep learning;    Hyperthyroidism;    Thyrotoxicosis;    Retinal photographs;    Retina;   

DOI  :  10.1186/s40537-023-00777-6

 received in 2022-01-20, accepted in 2023-05-17,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundScreening 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.MethodsThe 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.ResultsThe 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.ConclusionsRetinal fundus photographs may serve for DL systems for a cost-effective and non-invasive method to detect hyperthyroidism.

【 授权许可】

CC BY   
© Springer Nature Switzerland AG 2023

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