Artificial Intelligence Methods for Diagnostic and Decision-Making Assistance in Chronic Wounds: A Systematic Review.
Chronic wounds, which take over four weeks to heal, are a major global health issue linked to conditions such as diabetes, venous insufficiency, arterial diseases, and pressure ulcers. These wounds cause pain, reduce quality of life, and impose significant economic burdens. This systematic review ex...
| Published in: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 40 |
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| Main Authors: | , , , |
| Format: | research systematic review tables/charts Journal Article |
| Published: |
Springer Nature
2/19/2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183131988&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183131988 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 2/19/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 183131988 183131988 183131988 10.1007/s10916-025-02153-8 183131988 ppf: 1 ppct: 39 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial Intelligence Methods for Diagnostic and Decision-Making Assistance in Chronic Wounds: A Systematic Review. aug: au: Reifs Jiménez, David Casanova-Lozano, Lorena Grau-Carrión, Sergi Reig-Bolaño, Ramon affil: https://ror.org/006zjws59 Digital Care Research Group, University of Vic, C/ Sagrada Familia, 7, 08500, Vic, Barcelona, Spain sug: subj: Artificial Intelligence Methods Wounds, Chronic Diagnosis Decision Making, Clinical Image Processing, Computer Assisted Human Systematic Review PubMed Data Analysis Tissue Analysis Wound Measurement Wounds, Chronic Etiology Wound Healing Wounds, Chronic Therapy Quality Improvement Health Care Costs Diabetic Foot Diagnosis Pressure Ulcer Diagnosis Venous Ulcer Diagnosis Surgical Wound Diagnosis Deep Learning Algorithms Machine Learning Sensitivity and Specificity False Positive Results Convolutional Neural Networks Support Vector Machine Random Forest ab: Chronic wounds, which take over four weeks to heal, are a major global health issue linked to conditions such as diabetes, venous insufficiency, arterial diseases, and pressure ulcers. These wounds cause pain, reduce quality of life, and impose significant economic burdens. This systematic review explores the impact of technological advancements on the diagnosis of chronic wounds, focusing on how computational methods in wound image and data analysis improve diagnostic precision and patient outcomes. A literature search was conducted in databases including ACM, IEEE, PubMed, Scopus, and Web of Science, covering studies from 2013 to 2023. The focus was on articles applying complex computational techniques to analyze chronic wound images and clinical data. Exclusion criteria were non-image samples, review articles, and non-English or non-Spanish texts. From 2,791 articles identified, 93 full-text studies were selected for final analysis. The review identified significant advancements in tissue classification, wound measurement, segmentation, prediction of wound aetiology, risk indicators, and healing potential. The use of image-based and data-driven methods has proven to enhance diagnostic accuracy and treatment efficiency in chronic wound care. The integration of technology into chronic wound diagnosis has shown a transformative effect, improving diagnostic capabilities, patient care, and reducing healthcare costs. Continued research and innovation in computational techniques are essential to unlock their full potential in managing chronic wounds effectively. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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