Pressure injury image analysis with machine learning techniques: a systematic review on previous and possible future methods

Vista sencilla de ítem
dc.contributor.authorZahia, Sofia
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorSevillano, Xavier
dc.contributor.authorGonzález, Alejandro
dc.contributor.authorKim, Paul J.
dc.contributor.authorElmaghraby, Adel Said
dc.date.accessioned2024-11-14T11:37:38Z
dc.date.available2024-11-14T11:37:38Z
dc.date.issued2020-01
dc.date.updated2024-11-14T11:37:38Z
dc.description.abstractPressure injuries represent a tremendous healthcare challenge in many nations. Elderly and disabled people are the most affected by this fast growing disease. Hence, an accurate diagnosis of pressure injuries is paramount for efficient treatment. The characteristics of these wounds are crucial indicators for the progress of the healing. While invasive methods to retrieve information are not only painful to the patients but may also increase the risk of infections, non-invasive techniques by means of imaging systems provide a better monitoring of the wound healing processes without causing any harm to the patients. These systems should include an accurate segmentation of the wound, the classification of its tissue types, the metrics including the diameter, area and volume, as well as the healing evaluation. Therefore, the aim of this survey is to provide the reader with an overview of imaging techniques for the analysis and monitoring of pressure injuries as an aid to their diagnosis, and proof of the efficiency of Deep Learning to overcome this problem and even outperform the previous methods. In this paper, 114 out of 199 papers retrieved from 8 databases have been analyzed, including also contributions on chronic wounds and skin lesions.en
dc.description.sponsorshipThis project has been partially funded by eVida Group IT905-16, the Basque Government, ACM 2017-09 and ACM 2018-21en
dc.identifier.citationZahia, S., Garcia Zapirain, M. B., Sevillano, X., González, A., Kim, P. J., & Elmaghraby, A. (2020). Pressure injury image analysis with machine learning techniques: A systematic review on previous and possible future methods. Artificial Intelligence in Medicine, 102. Elsevier B.V. https://doi.org/10.1016/J.ARTMED.2019.101742
dc.identifier.doi10.1016/J.ARTMED.2019.101742
dc.identifier.eissn1873-2860
dc.identifier.issn0933-3657
dc.identifier.urihttp://hdl.handle.net/20.500.14454/1863
dc.language.isoeng
dc.publisherElsevier B.V.
dc.rights© 2019 Elsevier B.V.
dc.subject.otherDeep learning
dc.subject.otherMachine learning algorithms
dc.subject.otherPressure injury
dc.subject.otherWound image analysis
dc.titlePressure injury image analysis with machine learning techniques: a systematic review on previous and possible future methodsen
dc.typereview article
dcterms.accessRightsmetadata only access
oaire.citation.titleArtificial Intelligence in Medicine
oaire.citation.volume102
Ficheros en el ítem
Colecciones
Artículos