Smartphone video-based early diagnosis of blepharospasm using dual cross-attention modeling enhanced by facial pose estimation.

Journal: NPJ digital medicine
Published Date: Aug 5, 2025

Abstract

Blepharospasm is a focal dystonia characterized by involuntary eyelid contractions that impair vision and social function. The subtle clinical signs of blepharospasm make early and accurate diagnosis difficult, delaying timely intervention. In this study, we propose a dual cross-attention deep learning framework that integrates temporal video features and facial landmark dynamics to assess blepharospasm severity, frequency, and diagnosis from smartphone-recorded facial videos. A retrospective dataset of 847 patient videos collected from two hospitals (2016-2023) was used for model development. The model achieved high accuracy for severity (0.828) and frequency (0.82), and moderate performance for diagnosis (0.674).SHAP analysis identified case-specific video fragments contributing to predictions, enhancing interpretability. In a prospective evaluation on an independent dataset (N = 179), AI assistance improved junior ophthalmologist's diagnostic accuracy by up to 18.5%. These findings demonstrate the potential of an explainable, smartphone-compatible video model to support early detection and assessment of blepharospasm.

Authors

  • Shenyu Huang
    Zhejiang University, Eye Center of Second Affiliated Hospital, School of Medicine. Zhejiang Provincial Key Laboratory of Ophthalmology. Zhejiang Provincial Clinical Research Center for Eye Diseases. Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China.
  • Boyuan Yang
    Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
  • Xiaoling Huang
    Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University, College of Medicine, Hangzhou, 310009, China.
  • Huina Zhang
    Zhejiang University, Eye Center of Second Affiliated Hospital, School of Medicine. Zhejiang Provincial Key Laboratory of Ophthalmology. Zhejiang Provincial Clinical Research Center for Eye Diseases. Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China.
  • Dong Luo
    CAS Key Laboratory for Plant Diversity and Biogeography of East Asia, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China.
  • Guanchao Tong
    College of Science, Mathematics and Technology, Wenzhou-Kean University, Wenzhou, Zhejiang, China.
  • Yijie Wang
    College of Computer, National University of Defense Technology, Changsha 410073, China.
  • Yongqing Shao
    Ningbo Key Laboratory of Medical Research on Blinding Eye Diseases, Ningbo Eye Institute, Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, China.
  • Menglu Chen
    Department of Ophthalmology, College of Medicine, The Second Affiliated Hospital of Zhejiang University, Hangzhou, China.
  • Qi Gao
    Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, People's Republic of China.
  • Juan Ye
    Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.

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