[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117157-en":3,"doc-seo-117157-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117157,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","The impact of machine learning on the prediction of diabetic foot ulcers - A systematic review","Machine learning is assessed for its ability to support early detection and prediction of diabetic foot ulcers (DFUs), a major complication of diabetes mellitus. The review uses a PRISMA-informed search across major medical databases for studies published in English within the past ten years. Eighteen reports met inclusion criteria, addressing classification of healthy skin vs DFU and prediction of DFU progression, including infection status and healing based on wound characteristics. Reported performance ranges include sensitivity 74.53–98% and accuracy 64.6–99.32%.","The impact of machine learning on the prediction of diabetic foot ulcers – A systematic review  \nAuthor  \nWeatherall , Teagan , Avsar, Pinar, Nugent , Linda , Moore , Zena , McDermott , John H , Sreenan , Seamus , Wilson , Hannah , McEvoy, Natalie L , Derwin , Rosemarie , Chadwick , Paul , Patton , Declan  \nPublished 2024  \nJournal Title  \nJournal of Tissue Viability  \nVersion  \nVersion of Record (VoR)  \nDOI  \n10.1016/j.jtv.2024.07.004  \nRights statement  \n© 2024 The Authors. Published by Elsevier Ltd on behalf of Tissue Viability Society / Society of Tissue Viability. This is an open access article under the CC BY license ([http://](http://)[ ](http://)[creativecommons.org/licenses/by/4.0/](creativecommons.org/licenses/by/4.0/))  \nDownloaded from  \n[https://hdl.handle.net/10072/431809](https://hdl.handle.net/10072/431809)  \nGriffith Research Online  \n[https://research-repository.griffith.edu.au](https://research-repository.griffith.edu.au)  \nJournal of Tissue Viability xxx (xxxx) xxx  \nContents lists available at ScienceDirect  \nJournal of Tissue Viability  \njournal [homepage: www.elsevier.com/locate/jtv](homepage: www.elsevier.com/locate/jtv)  \n| The impact of machine learning on the prediction of diabetic foot ulcers – A systematic review\u003Cbr>Teagan Weatherall a,b,*, Pinar Avsar a,b, Linda Nugent a,b,c, Zena Moore a,b,c,d,e,f,\u003Cbr>g,h,i, John H. McDermott j, Seamus Sreenanj, Hannah Wilson a,b, Natalie L. McEvoyb, Rosemarie Derwin b, Paul Chadwick k,l, Declan Patton a,b,c,d,m\u003Cbr>a Skin Wounds and Trauma (SWaT) Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland b School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland\u003Cbr>c Fakeeh College of Medical Sciences, Jeddah, Saudi Arabia\u003Cbr>d School of Nursing and Midwifery, Griffith University, Southport, Queensland, Australia e Lida Institute, Shanghai, China\u003Cbr>f Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia g Department of Public Health, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium h University of Wales, Cardiff, UK\u003Cbr>i National Health and Medical Research Council Centre of Research Excellence in Wiser Wound Care, Menzies Health Institute Queensland, Southport, Queensland, Australia\u003Cbr>j Department of Endocrinology, Royal College of Surgeons in Ireland, Connolly Hospital Blanchardstown, Dublin, Ireland k Birmingham City University, Birmingham, UK\u003Cbr>l Spectral MD, London, UK\u003Cbr>m Faculty of Science, Medicine and Health, University of Wollongong, Wollongong, New South Wales, Australia |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Artificial intelligence Machine learning Diabetic foot ulcer Systematic review |  | Introduction: Globally, diabetes mellitus poses a significant health challenge as well as the associated complications of diabetes, such as diabetic foot ulcers (DFUs). The early detection of DFUs is important in the healing process and machine learning may be able to help inform clinical staff during the treatment process.\u003Cbr>Methods: A PRISMA-informed search of the literature was completed via the Cochrane Library and MEDLINE (OVID), EMBASE, CINAHL Plus and Scopus databases for reports published in English and in the last ten years. The primary outcome of interest was the impact of machine learning on the prediction of DFUs. The secondary outcome was the statistical performance measures reported. Data were extracted using a predesigned data extraction tool. Quality appraisal was undertaken using the evidence-based librarianship critical appraisal tool. Results: A total of 18 reports met the inclusion criteria. Nine reports proposed models to identify two classes, either healthy skin or a DFU. Nine reports proposed models to predict the progress of DFUs, for example, classing infection versus non-infection, or using wound characteristics to predict healing. A variety of machine","cbCaie7Bz7DDN9dB","https://ap.wps.com/l/cbCaie7Bz7DDN9dB","pdf",619573,1,12,"English","en",105,"# Abstract\n## Introduction\n## Methods\n## Results\n## Conclusions","[{\"question\":\"What was the main objective of the systematic review?\",\"answer\":\"To evaluate the impact of machine learning on the prediction of diabetic foot ulcers (DFUs).\"},{\"question\":\"How were studies selected for inclusion?\",\"answer\":\"Through a PRISMA-informed search of the Cochrane Library and MEDLINE, EMBASE, CINAHL Plus, and Scopus for English-language reports published in the last ten years.\"},{\"question\":\"What kinds of machine learning models were reported in the included studies?\",\"answer\":\"Models either classified two classes (healthy skin vs DFU) or predicted DFU progression, such as distinguishing infection vs non-infection or forecasting healing from wound characteristics.\"}]","The impact of machine learning on the prediction of diabetic foot ulcers - A systematic review | PDF",1785674150,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"the-impact-of-machine-learning-on-the-prediction-of-diabetic-foot-ulcers-a-systematic-review","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/the-impact-of-machine-learning-on-the-prediction-of-diabetic-foot-ulcers-a-systematic-review/117157/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What was the main objective of the systematic review?","Question",{"text":76,"@type":77},"To evaluate the impact of machine learning on the prediction of diabetic foot ulcers (DFUs).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were studies selected for inclusion?",{"text":81,"@type":77},"Through a PRISMA-informed search of the Cochrane Library and MEDLINE, EMBASE, CINAHL Plus, and Scopus for English-language reports published in the last ten years.",{"name":83,"@type":74,"acceptedAnswer":84},"What kinds of machine learning models were reported in the included studies?",{"text":85,"@type":77},"Models either classified two classes (healthy skin vs DFU) or predicted DFU progression, such as distinguishing infection vs non-infection or forecasting healing from wound characteristics.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":122},"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]