Is thermography a viable solution for detecting pressure injuries in dark skin patients?

TL;DR

This study evaluates thermography's potential for detecting pressure injuries in dark skin patients, introducing a new diverse dataset and analyzing CNN performance across various imaging conditions.

Contribution

It provides the first comprehensive dataset focused on dark skin tones and assesses the robustness of thermography-based CNNs under different data collection protocols.

Findings

Thermography-based CNN performs consistently across skin tones.

The dataset includes varied imaging conditions and protocols.

Preliminary results support thermography as a viable detection method.

Abstract

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.