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.

本研究解决了在深色皮肤上检测压疮（PI）面临的挑战，尤其是由于视觉检查不可靠的问题。论文提出利用热成像作为替代方案，并引入了一个针对深色皮肤的新型热光成像数据集，研究表明，基于热成像的卷积神经网络（CNN）在不同的数据收集协议下能够稳健地检测各种肤色的压疮。

热成像在检测深色皮肤患者压疮中的可行性研究