Deep learning system for the auxiliary diagnosis of thyroid eye disease: evaluation of ocular inflammation, eyelid retraction, and eye movement disorder

Yu Han; Jun Xie; Xiaoyu Li; Xinying Xu; Bin Sun; Han Liu; Chunfang Yan

doi:10.3389/fcell.2025.1609231

Deep learning system for the auxiliary diagnosis of thyroid eye disease: evaluation of ocular inflammation, eyelid retraction, and eye movement disorder

Front Cell Dev Biol. 2025 Jun 16:13:1609231. doi: 10.3389/fcell.2025.1609231. eCollection 2025.

Authors

Yu Han¹, Jun Xie¹, Xiaoyu Li¹, Xinying Xu², Bin Sun³, Han Liu³, Chunfang Yan³

Affiliations

¹ College of Electronic Information Engineering, Taiyuan University of Technology, Jinzhong, Shanxi, China.
² College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, Shanxi, China.
³ Department of Orbital Diseases, Shanxi Eye Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China.

Abstract

Objective: This study aims to construct a semantic segmentation-based auxiliary diagnostic model for thyroid eye disease (TED) focusing on eyelid retraction, eye movement disorders, ocular inflammation related to Clinical Activity Score (CAS), facilitating rapid and non-invasive diagnosis for suspected TED patients and enhancing the efficiency of treatment and diagnosis.

Methods: Data were collected from 153 subjects exhibiting symptoms of eyelid retraction, eye movement disorders, and ocular inflammation related to CAS. After quality screening, datasets for the primary position (303 eyes), gaze positions (1,199 eyes), and a multi-label inflammatory classification dataset (272 eyes) were constructed. The constructed TBRM-Net adopts a dual-branch feature extraction and fusion strategy to extract inflammation features for multi-label classification and recognition; the constructed DSR-Net performs segmentation of ocular structures and has designed a quantitative diagnostic algorithm.

Results: The semantic segmentation-based auxiliary diagnostic model for TED demonstrated a mean pixel accuracy (MPA) of 94.1% in the primary position dataset and 95.0% in the gaze positions dataset. The accuracy for diagnosing eye movement disorders, upper eyelid retraction, and lower eyelid retraction reached 85.4%, 95.1%, and 87.0%, respectively. The accuracy for Redness of Eyelids, Swelling of Eyelids, Redness of Conjunctiva, Swelling of Conjunctiva, and Swelling of Caruncle or Plica reaches 81.8%, 78.8%, 90.6%, 73.5%, and 83.9%, respectively, with an average accuracy of 81.7%. Segmenting and classifying images of structures affected by ocular inflammation can effectively exclude interfering features. The designed quantitative algorithm provides greater interpretability than existing studies, thereby validating the effectiveness of the diagnostic system.

Conclusion: The deep learning-based auxiliary diagnostic model for TED established in this study exhibits high accuracy and interpretability in the diagnosis of ocular inflammation related to CAS, eyelid retraction, and eye movement disorders. It holds significant medical value in assisting doctors in formulating treatment plans and evaluating therapeutic effects.

Keywords: automatic quantization; eye digital image dataset; feature extraction; multi-label image classification; semantic segmentation; thyroid eye disease (TED).