Automated Prediction of Photographic Wound Assessment Tool in Chronic Wound Images

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 le...

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Bibliographic Details
Published in:Journal of medical systems 2024-01, Vol.48 (1), p.14, Article 14
Main Authors: Curti, Nico, Merli, Yuri, Zengarini, Corrado, Starace, Michela, Rapparini, Luca, Marcelli, Emanuela, Carlini, Gianluca, Buschi, Daniele, Castellani, Gastone C., Piraccini, Bianca Maria, Bianchi, Tommaso, Giampieri, Enrico
Format: Article
Language:English
Subjects:
Artificial intelligence
Automation
Clinical medicine
Correlation coefficient
Correlation coefficients
Datasets
Deep learning
Dermatology
Health Informatics
Health Sciences
Image acquisition
Image processing
Machine learning
Medicine
Medicine & Public Health
Morphology
Neural networks
Original Paper
Smartphones
Statistics for Life Sciences
Subjectivity
Ulcers
Wound healing
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Summary: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.
ISSN:1573-689X
0148-5598
1573-689X
DOI:10.1007/s10916-023-02029-9