用热成像检测深肤色患者压力性损伤，效果稳定可靠。
Is thermography a viable solution for detecting pressure injuries in dark skin patients?
- 构建了针对深肤色人群的热成像与光学影像数据集
- 热成像模型在不同采集条件下对所有肤色均表现稳健
- 适合临床用于深肤色患者早期压力损伤筛查
压力性损伤（PI）检测在深肤色患者中尤为困难，因视觉检查不可靠。热成像被认为是一种可行替代方案，因皮肤温度差异可预示组织损伤。尽管深度学习模型在可靠检测方面展现出潜力，现有研究未评估其在深肤色及不同数据采集协议下的表现。本文引入一个包含35名受试者的新型热成像与光学影像数据集，聚焦深肤色人群，并通过冷却和按压协议诱导温度差异。我们系统改变图像采集条件，包括不同相机、光照、体位和拍摄距离。比较了一个小型卷积神经网络（CNN）在仅热成像或仅光学图像上的性能表现。初步结果表明，基于热成像的CNN对所有肤色在多种采集条件下均具鲁棒性。
原文摘要 · Abstract (English)
Pressure injury (PI) detection is challenging, especially in dark skin tones, due to the unreliability of visual inspection. Thermography has been suggested as a viable alternative as temperature differences in the skin can indicate impending tissue damage. Although deep learning models have demonstrated considerable promise toward reliably detecting PI, the existing work fails to evaluate the performance on darker skin tones and varying data collection protocols. In this paper, we introduce a new thermal and optical imaging dataset of 35 participants focused on darker skin tones where temperature differences are induced through cooling and cupping protocols. We vary the image collection process to include different cameras, lighting, patient pose, and camera distance. We compare the performance of a small convolutional neural network (CNN) trained on either the thermal or the optical images on all skin tones. Our preliminary results suggest that thermography-based CNN is robust to data collection protocols for all skin tones.
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