Automated Prediction of Photographic Wound Assessment Tool in Chronic Wound Images.
Clinical decision support system
Computer vision
Image analysis
PWAT
Wound healing
Journal
Journal of medical systems
ISSN: 1573-689X
Titre abrégé: J Med Syst
Pays: United States
ID NLM: 7806056
Informations de publication
Date de publication:
16 Jan 2024
16 Jan 2024
Historique:
received:
12
08
2023
accepted:
22
12
2023
medline:
16
1
2024
pubmed:
16
1
2024
entrez:
16
1
2024
Statut:
epublish
Résumé
Many automated approaches have been proposed in literature to quantify clinically relevant wound features based on image processing analysis, aiming at removing human subjectivity and accelerate clinical practice. In this work we present a fully automated image processing pipeline leveraging deep learning and a large wound segmentation dataset to perform wound detection and following prediction of the Photographic Wound Assessment Tool (PWAT), automatizing the clinical judgement of the adequate wound healing. Starting from images acquired by smartphone cameras, a series of textural and morphological features are extracted from the wound areas, aiming to mimic the typical clinical considerations for wound assessment. The resulting extracted features can be easily interpreted by the clinician and allow a quantitative estimation of the PWAT scores. The features extracted from the region-of-interests detected by our pre-trained neural network model correctly predict the PWAT scale values with a Spearman's correlation coefficient of 0.85 on a set of unseen images. The obtained results agree with the current state-of-the-art and provide a benchmark for future artificial intelligence applications in this research field.
Identifiants
pubmed: 38227131
doi: 10.1007/s10916-023-02029-9
pii: 10.1007/s10916-023-02029-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
14Informations de copyright
© 2024. The Author(s).
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