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Automated Prediction of Photographic Wound Assessment Tool in Chronic Wound Images.
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.
Afiliación
  • Curti N; Department of Physics and Astronomy, University of Bologna, 40127, Bologna, Italy.
  • Merli Y; Data Science and Bioinformatics Laboratory, IRCCS Institute of Neurological Sciences of Bologna, 40139, Bologna, Italy.
  • Zengarini C; Dermatology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138, Bologna, Italy.
  • Starace M; Department of Medical and Surgical Sciences, University of Bologna, 40138, Bologna, Italy.
  • Rapparini L; Department of Medical and Surgical Sciences, University of Bologna, 40138, Bologna, Italy. corrado.zengarini@studio.unibo.it.
  • Marcelli E; Dermatology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138, Bologna, Italy.
  • Carlini G; Department of Medical and Surgical Sciences, University of Bologna, 40138, Bologna, Italy.
  • Buschi D; Department of Medical and Surgical Sciences, University of Bologna, 40138, Bologna, Italy.
  • Castellani GC; Department of Medical and Surgical Sciences, University of Bologna, 40138, Bologna, Italy.
  • Piraccini BM; eDIMESLab, Department of Medical and Surgical Sciences, University of Bologna, 40138, Bologna, Italy.
  • Bianchi T; Data Science and Bioinformatics Laboratory, IRCCS Institute of Neurological Sciences of Bologna, 40139, Bologna, Italy.
  • Giampieri E; Department of Medical and Surgical Sciences, University of Bologna, 40138, Bologna, Italy.
J Med Syst ; 48(1): 14, 2024 Jan 16.
Article en En | MEDLINE | ID: mdl-38227131
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Benchmarking Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: J Med Syst Año: 2024 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Benchmarking Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: J Med Syst Año: 2024 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Estados Unidos