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<p>Early detection of congenital ptosis is critical to prevent visual and psychosocial impairment in children, yet clinical assessment is challenged by limited patient cooperation and specialist availability. In this prospective, multicenter study, we developed and validated a smartphone-based system comprising three modules: morphological assessment, functional analysis, and a domain-adapted dialogue model, using 3164 blink clips and 1,229 facial images. The morphological module showed high measurement accuracy with intraclass correlation coefficients over 0.90 versus manual assessments. The functional module identified levator dysfunction with an area under the curve of 0.993, achieving robust functional stratification accuracy in both internal (0.91) and real-world (0.89) cohorts. The dialogue model demonstrated improved correctness and applicability over its baseline in addressing ptosis-related queries, achieving overall performance comparable to GPT-4o in expert evaluation and a patient satisfaction score of 4.93/5 in real-world deployment. This smartphone platform enables precise ptosis evaluation with patient-centered interaction, facilitating informed decision-making and personalized care in oculoplastic practice. ClinicalTrials.gov NCT07078552.</p>

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From blink to care: smartphone video–based functional analysis and personalized management in pediatric blepharoptosis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月18日

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  • Huimin Li,
  • Jing Cao,
  • Shuangshuang Duan,
  • Saiyu Hu,
  • Lixia Lou,
  • Ming Lin,
  • Tianming Jian,
  • Ji Shao,
  • Xuan Zhang,
  • Pengjie Chen,
  • Yingcheng He,
  • Jiawei Wang,
  • Shoujun Huang,
  • Juan Ye

摘要

Early detection of congenital ptosis is critical to prevent visual and psychosocial impairment in children, yet clinical assessment is challenged by limited patient cooperation and specialist availability. In this prospective, multicenter study, we developed and validated a smartphone-based system comprising three modules: morphological assessment, functional analysis, and a domain-adapted dialogue model, using 3164 blink clips and 1,229 facial images. The morphological module showed high measurement accuracy with intraclass correlation coefficients over 0.90 versus manual assessments. The functional module identified levator dysfunction with an area under the curve of 0.993, achieving robust functional stratification accuracy in both internal (0.91) and real-world (0.89) cohorts. The dialogue model demonstrated improved correctness and applicability over its baseline in addressing ptosis-related queries, achieving overall performance comparable to GPT-4o in expert evaluation and a patient satisfaction score of 4.93/5 in real-world deployment. This smartphone platform enables precise ptosis evaluation with patient-centered interaction, facilitating informed decision-making and personalized care in oculoplastic practice. ClinicalTrials.gov NCT07078552.