PREDICTION AND CLASSIFICATION OF PRESSURE INJURIES BY DEEP LEARNING

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dc.contributor.authorYilmaz, Atinc
dc.contributor.authorKizil, Hamiyet
dc.contributor.authorKaya, Umut
dc.contributor.authorcakir, Ridvan
dc.contributor.authorDemiral, Melek
dc.date.accessioned2024-03-13T10:33:23Z
dc.date.available2024-03-13T10:33:23Z
dc.date.issued2021
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractPressure injuries are a serious medical problem that both negatively affects the patient's quality of life and results in significant healthcare costs. In cases where a patient doesn't receive appropriate treatment and care, death may result. Nurses play critical roles in the prevention, care, and treatment of pressure injuries as members of the healthcare team who closely monitor the health status of the patient. Today, the use of artificial intelligence is becoming more prevalent in healthcare, as in many other areas. Artificial intelligence is a method that aims to solve complex problems by using computers to mathematically simulate the way the brain works. In this article, we compile and share information about a deep learning model developed for the detection and classification of pressure injuries. Deep learning can operate on many types of data. Convolutional Neural Networks (CNN) prefer images because they can handle 2D arrays. In this case, the images, annotated according to the National Pressure Injury Advisory Panel pressure injury classification system, have been fed into a deep learning model using CNN. The developed CNN model has a 97% success in detecting and classifying pressure injuries, and as more images are collected and fed into the CNN, the prediction accuracy will increase. This deep learning model allows for the automatic detection and classification of pressure injuries, an indicator of health outcomes, at an early stage and for quick and accurate intervention. In this context, it is expected that the quality of nursing care will increase, the prevalence of pressure injury will decrease, and the economic burden of this health problem will decrease.en_US
dc.description.sponsorshipBeykent University Scientific Research Projects (BAP) Coordination Unit [21-BAP-09]en_US
dc.description.sponsorshipThe authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. This work was supported by the Beykent University Scientific Research Projects (BAP) Coordination Unit. Project No: 21-BAP-09, 2020.en_US
dc.identifier.doi10.5114/hpc.2021.110457
dc.identifier.endpage335en_US
dc.identifier.issn2353-6942
dc.identifier.issn2354-0265
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85146032414
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage328en_US
dc.identifier.urihttps://doi.org/10.5114/hpc.2021.110457
dc.identifier.urihttps://hdl.handle.net/20.500.12662/3929
dc.identifier.volume15en_US
dc.identifier.wosWOS:000749718200001
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Science
dc.language.isoenen_US
dc.publisherTermedia Publishing House Ltden_US
dc.relation.ispartofHealth Problems Of Civilizationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectdeep learningen_US
dc.subjectpressure ulcersen_US
dc.subjectartificial intelligenceen_US
dc.subjectnursing careen_US
dc.titlePREDICTION AND CLASSIFICATION OF PRESSURE INJURIES BY DEEP LEARNINGen_US
dc.typeReview Articleen_US

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