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使用红外摄影图像进行非侵入性干眼病检测:一项概念验证研究

Laily Azyan Ramlan¹, Wan Mimi Diyana Wan Zaki¹, Marizuana Mat Daud²

¹Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Malaysia.

Diagnostics (Basel, Switzerland)

|August 28, 2025

概括

智能手机的红外热学显示有望检测干眼病 (DED). 这种非侵入性查方法通过分析眼睛表面温度变化来准确识别DED,有助于早期诊断.

科学领域:

眼科眼科
医学成像
生物医学工程

背景情况:

干眼病 (DED) 严重影响生活质量,原因是眼膜不稳定,眼产量减少.
目前对DED的诊断方法往往是侵入性的,不可携带的,耗时的,导致诊断和治疗延迟.
眼科专业人员的有限访问加剧了DED管理的挑战.

研究的目的:

研究基于智能手机的红外热学 (IRT) 作为DED的非侵入性和便携式查工具的可行性.
通过IRT测量眼睛表面温度变化与已确定的DED指标,如膜破裂时间 (TBUT) 和眼睛表面疾病指数 (OSDI) 之间的相关性.
通过IRT数据评估机器学习模型对正常眼睛和DED眼睛的分类效果.

主要方法:

在40名受试者身上采集了红外热图 (IRT) 图像 (22名正常患者,58名DED患者).
分析了鼻角膜,中角膜和角膜的眼表面温度变化.
进行了统计分析,包括相关性和独立的t测试. 使用机器学习模型 (SVM,k-NN) 来进行分类.

主要成果:

与正常眼睛相比,DED眼睛表现出明显更快的眼表面冷却率 (p < 0. 001).
在所有角膜区域中,TBUT与OSDI (负) 和冷却率 (阳性) 有显著的相关性.
机器学习模型表现出高分类精度,k-NN达到91.89%,SVM达到90.54%.

结论:

基于智能手机的IRT显示出作为干眼病的非侵入性查工具的巨大潜力.
机器学习模型在使用IRT数据进行DED分类时的高精度支持其临床实用性.
需要使用更大,更多样化的数据集进行进一步的验证,以促进该技术的临床应用.

关键词:

干眼疾病红外摄像机机器学习统计分析

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