智能手机瞳孔测量和机器学习用于检测急性轻度创伤性脑损伤:队列研究
Anthony J Maxin1,2, Do H Lim1,3, Sophie Kush4
1Department of Neurological Surgery University of Washington Seattle, WA United States.
JMIR neurotechnology
|December 4, 2025
概括
智能手机瞳孔测量准确地区分轻度创伤性脑损伤 (mTBI) 和健康个体. 这项技术对未来的mTBI诊断具有前景,提供了一个便携式和有效的评估工具.
科学领域:
- 神经学 神经学
- 医疗技术 医疗技术 医学技术
- 数据科学数据科学数据科学
背景情况:
- 定量瞳孔测量因其在评估轻度创伤性脑损伤 (mTBI) 的实用性而得到认可.
- 在各种mTBI环境中观察到瞳孔反应性的变化,包括爆炸伤害,慢性疾病和脑震荡.
研究的目的:
- 为了评估基于智能手机的数字学生计的诊断能力.
- 为了区分急诊室患有急性mTBI的患者与健康对照.
主要方法:
- 患有急性mTBI (正常神经成像) 的成年人和健康的对照人群使用PupilScreen智能手机学生计进行了评估.
- 对瞳孔光反射 (PLR) 参数进行了定量分析,并在各组之间进行了比较.
- 机器学习算法 (随机森林,k-NN,SVM,后勤回归) 与PLR参数用于分类,使用10倍交叉验证.
主要成果:
- 在mTBI患者和对照组 (P<.001) 之间发现了PLR参数 (百分比变化,最小直径,最大直径,平均收缩速度) 的显著差异.
- 一个随机森林模型,利用特定的PLR参数,实现了高诊断性能.
- 最好的模型显示了93.5%的准确性,96.2%的灵敏度,90.9%的特异性和0.936的mTBI检测AUC.
结论:
- 基于智能手机的定量瞳孔测量显示出作为急性mTBI的诊断工具的潜力.
- 这项试点研究强调了使用移动技术进行客观的mTBI评估的可行性.
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