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 clinical validation of 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. The validation occurred using 45 videos from real patients, whose blinks were manually annotated by ophthalmology specialists from the Paulista School of Medicine of the Federal University of Sao Paulo (EPM-UNIFESP) to serve as the ground truth. Bapp's performance was evaluated using standard metrics, including Precision, Recall, and F1-Score, with results demonstrating 98.4% precision, 96.9% recall, and an overall accuracy of 98.3%. These outcomes confirm the reliability of Bapp as a portable, accessible, and objective tool for monitoring both normal and abnormal eyelid movements. The application offers a promising alternative to traditional manual blink counting, supporting continuous ocular health monitoring and postoperative evaluation in clinical environments.

翻译：眨眼是保护和维持眼表健康的关键生理过程。由于现有工具的复杂性、成本高昂及临床适用性有限，眼睑运动的客观评估仍具挑战性。本研究介绍了Bapp（Blink Application）的临床验证，该移动应用程序基于Flutter框架开发，并集成Google ML Kit以实现设备端实时眼睑运动分析。验证过程使用了45段真实患者视频，其眨眼动作由圣保罗联邦大学保利斯塔医学院（EPM-UNIFESP）的眼科专家手动标注作为基准真值。通过精确率、召回率和F1分数等标准指标评估Bapp性能，结果显示其精确率达98.4%、召回率为96.9%、整体准确度为98.3%。这些结果证实了Bapp作为便携、可及且客观的工具，在监测正常与异常眼睑运动方面的可靠性。该应用为传统人工眨眼计数提供了有前景的替代方案，支持临床环境中持续的眼部健康监测及术后评估。