基于智能手机的红外热摄像头的机器学习校准:改进了偏差和持续的随机错误
Jayroop Ramesh1, Tom Loney2, Stefan Du Plessis2
1Department of Computer Science and Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
与标准温度计相比,像FLIR One Pro这样的智能手机热摄像头显示出明显的偏差和皮肤绝对温度测量的差异. 算法校正不能完全克服它们在临床使用方面的固有局限性.
科学领域:
- 生物医学工程 生物医学工程
- 医疗器械 医疗器械 医疗器械
- 热成像是一种热成像技术.
背景情况:
- 智能手机的热摄像头提供了可访问的生理监测.
- 它们在临床环境中测量绝对皮肤温度的准确性受到争议.
研究的目的:
- 为了比较FLIR One Pro智能手机热摄像头与iHealth PT3非接触式红外温度计的一致性和可重复性.
- 为了评估机器学习对校准FLIR One Pro读数的有效性.
主要方法:
- 一项方法比较研究,涉及40名健康成年人和2400次温度测量.
- 使用FLIR One Pro和iHealth PT3.3同时测量手背部的皮肤温度.
- 配对t测试和布兰德-阿尔特曼分析用于一致性和可重复性评估.
主要成果:
- 与FLIR One Pro (SD ≈ 0.30-0.34 °C) 相比,iHealth PT3表现出更高的精度 (SD ≈ 0.03-0.09 °C) 与FLIR One Pro (SD ≈ 0.30-0.34 °C) 相比.
- FLIR One Pro表现出相当大的平均偏差 (-1.15至-1.42°C) 和广泛的协议极限 (≈6°C).
- 机器学习校准减少了偏差,但并没有完全弥补FLIR One Pro的高可变性 (R2 = 0.152).
结论:
- 由于偏差和可变性,FLIR One Pro对绝对皮肤温度测量有重大限制.
- 算法校正不足以克服设备的基本测量约束.
- 该设备在需要精确温度值的临床监测或诊断决策方面具有有限的实用性.
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