智能PLR:一个数字解决方案,用于人工智能智能手机学生计
Kyu Lim Kim1, Dong Kyu Kim2,3, Jeong Hoon Lee1
1iDynamics Research Institute, Seoul, Republic of Korea.
BMC ophthalmology
|November 12, 2025
概括
一个新的智能手机应用程序使用深度学习来准确地测量瞳孔光反射 (PLR),而无需额外的设备. 这种创新方法为现有的瞳孔测量方法提供了可商业化的替代方案.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 基于智能手机的瞳孔测量旨在提供一种便携式和可访问的方法来评估瞳孔光反射 (PLR).
- 现有的智能手机应用程序通常需要额外的硬件,从而限制了它们的商业可行性.
研究的目的:
- 为学生计量开发基于深度学习的智能手机应用程序.
- 为了评估这个应用程序的准确性与商业学生计 (NPi-300).
主要方法:
- 深度学习模型 (Mask R-CNN与ConvNeXt V2骨干) 在336次PLR考试中接受了培训和验证.
- 根据眼的开放和模糊度,应用了图像质量过.
- 学生尺寸差异,收缩速度 (CV) 和百分比变化 (CP) 与NPI-300进行了比较,从而产生了SmartPLR评分系统.
主要成果:
- 面具R-CNN模型实现了高细分和检测精度 (mAP 0.8670).
- 在智能手机应用程序和NPi-300之间发现了强烈的皮尔森相关性,用于瞳孔大小差异 (0.77),CV (0.77),CP (0.74).
- 定义了SmartPLR系统来分类学生的反应性.
结论:
- 一个新的智能手机应用程序,利用深度学习进行瞳孔测量,已成功开发.
- 该应用程序显示了与商业设备相比较的高精度,不需要红外光或附加功能.
- 这项技术为远程和可访问的PLR评估提供了一个完全商业化的解决方案.
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