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热图的可行性研究,用于检测不同皮肤色调的压力损伤

Miriam Asare-Baiden¹, Sharon Eve Sonenblum¹, Kathleen Jordan¹

¹Emory University, Atlanta, GA.

medRxiv : the preprint server for health sciences

|November 1, 2024

概括

热力学显示有望检测压力损伤 (PI) 在个人与较深的皮肤色调. 使用热成像的深度学习模型在各种数据收集方法中都很强大,提高了PI检测的准确性.

科学领域:

生物医学工程生物医学工程
医疗成像医学成像
人工智能的人工智能

背景情况:

检测压力损伤 (PI) 的视觉检查是不可靠的,特别是在肤色较深的人群中.
热力学通过识别表明组织损伤的温度变化提供了一个潜在的替代方案.
目前用于PI检测的深度学习模型缺乏对不同肤色和各种数据收集协议的评估.

研究的目的:

开发和评估深度学习模型,用于使用热学检测压力损伤,重点关注较深色皮肤色调.
评估基于温度学模型的稳定性与数据收集的变化,包括摄像头,照明和患者定位.
为了比较在热与光学图像上训练的卷积神经网络 (CNN) 模型的性能.

主要方法:

一个新的数据集由35名参与者创建,强调更深的皮肤色调,使用冷却和杯协议来诱导温度差异.
三个CNN模型在数据集中的热和光学图像上进行了训练和评估.
对不同相机,照明条件,患者姿势和相机距离的性能进行了评估.

主要成果:

基于温度计的CNN模型在各种数据收集协议中表现出强度.
模型显示有效识别感兴趣的区域,而不需要明确的界限框注释.
跨不同肤色的性能评估是重点关注的重点,解决了现有研究中的差距.

结论:

热图学与深度学习相结合,为压力损伤检测提供了一种可靠的方法,特别是在较暗的皮肤色调中.
开发的方法对数据采集的变化具有弹性,提高了其实际适用性.
模型中的视觉解释有助于准确识别潜在的压力损伤部位.

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