Scholarlink

<p>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 (<i>N</i> = 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月5日

查看全文
  • Shenyu Huang,
  • Boyuan Yang,
  • Xiaoling Huang,
  • Huina Zhang,
  • Dong Luo,
  • Guanchao Tong,
  • Yijie Wang,
  • Yongqing Shao,
  • Menglu Chen,
  • Qi Gao,
  • Juan Ye

摘要

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.