用手机应用精准检测眨眼，临床验证准确率达98.3%
Clinically-Validated Innovative Mobile Application for Assessing Blinking and Eyelid Movements
- 基于Flutter和ML Kit开发移动端实时分析工具
- 在45个患者视频上实现98.4%精确率与96.9%召回率
- 适合眼科监测与术后评估，便携且无需专业设备
眨眼是保护和维持眼表健康的重要生理过程。现有工具因复杂、昂贵且临床适用性差，难以实现睑裂运动的客观评估。本研究提出Bapp（Blink Application），基于Flutter框架并集成Google ML Kit，实现移动端实时睑裂运动分析，并进行临床验证。验证使用45名患者的视频，由眼科专家手动标注为基准真值。评估结果显示，Bapp的精确率为98.4%，召回率为96.9%，整体准确率达98.3%。结果证实Bapp是一种便携、易用且客观的眼睑运动监测工具，可替代传统人工计数，支持连续眼健康监测及术后评估。
原文摘要 · Abstract (English)
Blinking is a vital physiological process that protects and maintains the health of the ocular surface. Objective assessment of eyelid movements remains challenging due to the complexity, cost, and limited clinical applicability of existing tools. This study presents the Bapp (Blink Application), a mobile application developed using the Flutter framework and integrated with Google ML Kit for on-device, real-time analysis of eyelid movements, and its clinical validation. The validation was performed using 45 videos from patients, whose blinks were manually annotated by an ophthalmology specialist as the ground truth. The Bapp's performance was evaluated using standard metrics, with results demonstrating 98.4% precision, 96.9% recall, and an overall accuracy of 98.3%. These outcomes confirm the reliability of the Bapp as a portable, accessible, and objective tool for monitoring eyelid movements. The application offers a promising alternative to traditional manual blink counting, supporting continuous ocular health monitoring and postoperative evaluation in clinical environments.
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