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

Li Dong, Lie Ju, Shiqi Hui, Lihua Luo, Xue Jiang, Zihan Nie, Ruiheng Zhang, Wenda Zhou, Heyan Li, Jost B. Jonas, Xin Wang, Xin Zhao, Chao He, Yuzhong Chen, Zhaohui Wang, Jianxiong Gao, Zongyuan Ge, Wenbin Wei, Dongmei Li

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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.

Original languageEnglish
Article number134
Number of pages12
JournalJournal of Big Data
Issue number1
Publication statusPublished - 29 Aug 2023


  • Artificial intelligence
  • Deep learning
  • Hyperthyroidism
  • Retina
  • Retinal photographs
  • Thyrotoxicosis

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