Noncontact Size Estimation of Pressure Ulcers Using IR Thermal Imaging

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 lea...

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Bibliographic Details
Published inIEEE sensors letters Vol. 8; no. 12; pp. 1 - 4
Main Authors Pandey, Bhaskar, Arora, Ajat Shatru, Joshi, Deepak
Format Journal Article
LanguageEnglish
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
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Summary: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).
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ISSN:2475-1472
2475-1472
DOI:10.1109/LSENS.2024.3494843