Noncontact Size Estimation of Pressure Ulcers Using IR Thermal Imaging

• Published in: IEEE sensors letters Vol. 8; no. 12; pp. 1 - 4
• Main Authors: Pandey, Bhaskar, Arora, Ajat Shatru, Joshi, Deepak
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Biomedical measurement; Deep learning; Error analysis; Image segmentation; infrared (IR) imaging; Infrared imaging; Instance segmentation; Mathematical models; measurement; Pressure measurement; Remote monitoring; Root-mean-square errors; sensor applications; Sensor signal processing; Sensor systems; Signal processing; Thermal imaging; Training; Ulcers; wound care; Wounds
• DOI: 10.1109/LSENS.2024.3494843
• ISSN: 2475-1472

Pressure injuries cause discomfort and potential fatality, underscoring the importance of wound assessment. In the post-COVID era, remote monitoring of wounds, particularly through noncontact methods like infrared (IR) thermal imaging and deep learning, is imperative. This letter proposes a deep learning approach for dimension detection from thermal images, trained on data from 18 subjects. Instance segmentation achieved a maximum accuracy of 0.9542, with classification accuracy reaching 0.9922. The model exhibited a root mean square error (RMSE) of 0.1609 cm for measured dimensions, with superior accuracy in detecting wound length (RMSE: 0.1114 cm) compared to width (RMSE: 0.1506 cm).