{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T07:45:40Z","timestamp":1792136740230,"version":"4.4.0"},"reference-count":19,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T00:00:00Z","timestamp":1792022400000},"content-version":"vor","delay-in-days":14,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Wound Journal"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Accurate wound size measurement is essential for monitoring healing, yet visual estimates are variable\u2014especially in telemedicine\u2010style assessments. To compare AI\u2010based wound size estimates with physician image\u2010based estimates using in\u2010person measurements as ground truth (GT). In a prospective dataset of bedside wound photographs (Dec 2024\u2013Apr 2025), GT height and width were measured in person by two experienced raters using a calibrated ruler. A set of ArUco markers placed adjacent to the wound at the same skin level enabled image\u2010based retrieval of real\u2010world size information. Five physicians estimated height and width from images in a telemedicine\u2010style workflow. Two deep\u2010learning segmentation architectures (TransNeXt\u2010Tiny [TransNeXt] and VWFormerConvNeXtS [VW\u2010Conv]) generated automated size estimates. Agreement and error were quantified using ICC(2,1), Pearson's\n                    <jats:italic>r<\/jats:italic>\n                    , MAE, bias and Bland\u2013Altman limits of agreement. In addition, pixel\u2010level segmentation performance of both models was quantified against expert\u2010annotated reference masks using Intersection\u2010over\u2010Union (IoU), the Dice coefficient, precision and recall, reported as macro\u2010averaged (per\u2010image) and micro\u2010averaged (pixel\u2010pooled) values. AI achieved excellent agreement for height (TransNeXt ICC\u2009=\u20090.96, MAE\u2009=\u20090.82\u2009cm; VW\u2010Conv ICC\u2009=\u20090.95, MAE\u2009=\u20090.68\u2009cm) with minimal bias. For width, AI showed moderate\u2010to\u2010strong agreement with a tendency to underestimation (TransNeXt ICC\u2009=\u20090.86, MAE\u2009=\u20090.69\u2009cm, bias\u2009=\u2009\u22120.42\u2009cm; VW\u2010Conv ICC\u2009=\u20090.77, MAE\u2009=\u20090.74\u2009cm, bias\u2009=\u2009\u22120.64\u2009cm). Individual physician performance was heterogeneous, while a human consensus improved agreement (height ICC\u2009=\u20090.94, MAE\u2009=\u20090.82\u2009cm; width ICC\u2009=\u20090.91, MAE\u2009=\u20090.65\u2009cm). At the pixel level, TransNeXt reached a macro\u2010averaged Dice of 0.886 (IoU 0.805) and VW\u2010Conv a macro\u2010averaged Dice of 0.818 (IoU 0.733), with TransNeXt showing the more consistent per\u2010image performance. AI\u2010based wound measurement was more consistent than single physician estimates and reached agreement comparable to, but not exceeding, a consensus of experts. Within the constraints of this single\u2010centre validation study, these findings support AI\u2010assisted measurement as an objective aid for standardised wound monitoring in telemedicine and clinical research; confirmation in larger, multicentre and prospectively recruited cohorts is required.\n                  <\/jats:p>","DOI":"10.1111\/iwj.71043","type":"journal-article","created":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T06:53:28Z","timestamp":1792133608000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>AI<\/scp>\n                    \u2010Based Wound Measurement in a Telemedicine\u2010Style Workflow: Agreement With In\u2010Person Reference Measurements Compared With Individual Physicians and Expert Consensus"],"prefix":"10.1111","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6158-9048","authenticated-orcid":false,"given":"Tassilo","family":"Dege","sequence":"first","affiliation":[{"name":"Department of Dermatology, Venereology and Allergology University Hospital W\u00fcrzburg  W\u00fcrzburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7123-7934","authenticated-orcid":false,"given":"Vanessa","family":"Borst","sequence":"additional","affiliation":[{"name":"Department of Computer Science University of W\u00fcrzburg  W\u00fcrzburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7060-2042","authenticated-orcid":false,"given":"Luisa","family":"Deutzmann","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Venereology and Allergology University Hospital W\u00fcrzburg  W\u00fcrzburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3514-4396","authenticated-orcid":false,"given":"Jan Hendrik","family":"Maiwald","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Venereology and Allergology University Hospital W\u00fcrzburg  W\u00fcrzburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9742-2063","authenticated-orcid":false,"given":"Samuel","family":"Kounev","sequence":"additional","affiliation":[{"name":"Department of Computer Science University of W\u00fcrzburg  W\u00fcrzburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3802-9046","authenticated-orcid":false,"given":"Astrid","family":"Schmieder","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Venereology and Allergology University Hospital W\u00fcrzburg  W\u00fcrzburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,15]]},"reference":[{"issue":"5","key":"e_1_2_12_2_1","doi-asserted-by":"crossref","first-page":"e217234","DOI":"10.1001\/jamanetworkopen.2021.7234","article-title":"Development of a Method for Clinical Evaluation of Artificial Intelligence\u2013Based Digital Wound Assessment Tools","volume":"4","author":"Howell R. S.","year":"2021","journal-title":"JAMA Network Open"},{"issue":"2","key":"e_1_2_12_3_1","doi-asserted-by":"crossref","first-page":"11","DOI":"10.53388\/BMEC2025011","article-title":"Role of Artificial Intelligence in Wound Care: A Narrative Review","volume":"4","author":"Annu C. H.","year":"2025","journal-title":"Biomedical Engineering Communications"},{"issue":"12","key":"e_1_2_12_4_1","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1089\/wound.2024.0095","article-title":"Prediction of Healing Trajectory of Chronic Wounds Using a Machine Learning Approach","volume":"14","author":"Dallmann A. C.","year":"2025","journal-title":"Advances in Wound Care"},{"issue":"6","key":"e_1_2_12_5_1","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1001\/jamadermatol.2023.0905","article-title":"Assessment of Diagnostic Performance of Dermatologists Cooperating With a Convolutional Neural Network in a Prospective Clinical Study: Human With Machine","volume":"159","author":"Winkler J. K.","year":"2023","journal-title":"JAMA Dermatology"},{"issue":"1","key":"e_1_2_12_6_1","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1111\/iwj.13603","article-title":"Clinical Validation of an Artificial Intelligence\u2010Enabled Wound Imaging Mobile Application in Diabetic Foot Ulcers","volume":"19","author":"Chan K. S.","year":"2022","journal-title":"International Wound Journal"},{"issue":"3","key":"e_1_2_12_7_1","doi-asserted-by":"crossref","first-page":"e1009852","DOI":"10.1371\/journal.pcbi.1009852","article-title":"Automatic Wound Detection and Size Estimation Using Deep Learning Algorithms","volume":"18","author":"Carri\u00f3n H.","year":"2022","journal-title":"PLoS Computational Biology"},{"issue":"1","key":"e_1_2_12_8_1","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1097\/ASW.0000000000000246","article-title":"Utilizing Image Processing Techniques for Wound Management and Evaluation in Clinical Practice: Establishing the Feasibility of Implementing Artificial Intelligence in Routine Wound Care","volume":"38","author":"Dabas M.","year":"2025","journal-title":"Advances in Skin & Wound Care"},{"issue":"6","key":"e_1_2_12_9_1","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.3390\/medicina61061099","article-title":"Automated Image\u2010Based Wound Area Assessment in Outpatient Clinics Using Computer\u2010Aided Methods: A Development and Validation Study","volume":"61","author":"Li K. C.","year":"2025","journal-title":"Medicina"},{"issue":"5","key":"e_1_2_12_10_1","doi-asserted-by":"crossref","first-page":"e70089","DOI":"10.1111\/wrr.70089","article-title":"Advancing Wound Care With 3\u2010D Imaging: Clinical Applications, Performance and Future Directions","volume":"33","author":"Mehl A. A.","year":"2025","journal-title":"Wound Repair and Regeneration"},{"key":"e_1_2_12_11_1","doi-asserted-by":"crossref","first-page":"100884","DOI":"10.1016\/j.imu.2022.100884","article-title":"Detect\u2010and\u2010Segment: A Deep Learning Approach to Automate Wound Image Segmentation","volume":"29","author":"Scebba G.","year":"2022","journal-title":"Informatics in Medicine Unlocked"},{"key":"e_1_2_12_12_1","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/978-3-032-06118-8_17","volume-title":"Machine Learning and Knowledge Discovery in Databases Applied Data Science Track","author":"Borst V.","year":"2026"},{"issue":"10","key":"e_1_2_12_13_1","doi-asserted-by":"crossref","first-page":"S13","DOI":"10.12968\/jowc.2019.28.Sup10.S13","article-title":"Convolutional Neural Networks for Wound Detection: The Role of Artificial Intelligence in Wound Care","volume":"28","author":"Ohura N.","year":"2019","journal-title":"Journal of Wound Care"},{"issue":"1","key":"e_1_2_12_14_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-025-02153-8","article-title":"Artificial Intelligence Methods for Diagnostic and Decision\u2010Making Assistance in Chronic Wounds: A Systematic Review","volume":"49","author":"Reifs Jim\u00e9nez D.","year":"2025","journal-title":"Journal of Medical Systems"},{"issue":"1","key":"e_1_2_12_15_1","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1186\/s12911-025-03144-y","article-title":"Developing an AI\u2010Powered Wound Assessment Tool: A Methodological Approach to Data Collection and Model Optimization","volume":"25","author":"Stefanelli A.","year":"2025","journal-title":"BMC Medical Informatics and Decision Making"},{"issue":"10","key":"e_1_2_12_16_1","doi-asserted-by":"crossref","first-page":"e70767","DOI":"10.1111\/iwj.70767","article-title":"Use of Artificial Intelligence\u2010Driven Wound Care Management to Enhance Access to Care Rural and Northern Communities","volume":"22","author":"Freeman S.","year":"2025","journal-title":"International Wound Journal"},{"issue":"3","key":"e_1_2_12_17_1","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1111\/iwj.13918","article-title":"Clinical Validation of a Smartphone Application for Automated Wound Measurement in Patients With Venous Leg Ulcers","volume":"20","author":"Fong K. Y.","year":"2023","journal-title":"International Wound Journal"},{"key":"e_1_2_12_18_1","doi-asserted-by":"crossref","first-page":"e51592","DOI":"10.2196\/51592","article-title":"Patient\u2010Centered Chronic Wound Care Mobile Apps: Systematic Identification, Analysis, and Assessment","volume":"12","author":"Dege T.","year":"2024","journal-title":"JMIR mHealth and uHealth"},{"key":"e_1_2_12_19_1","unstructured":"German Clinical Trials Register (DRKS).\u201cSegmentation and Progress Monitoring of Chronic Wounds Using Artificial Intelligence (WUNDERKINT): Pilot study. DRKS00038408. Registered 2025\u201011\u201013 \u201dhttps:\/\/drks.de\/search\/en\/trial\/DRKS00038408\/."},{"key":"e_1_2_12_20_1","unstructured":"R. R.Selvaraju M.Cogswell A.Das R.Vedantam D.Parikh andD.Batra Grad\u2010Cam: Visual Explanations From Deep Networks via Gradient\u2010Based Localization. In: Proceedings of the Proceedings of the IEEE International Conference on Computer Vision."}],"container-title":["International Wound Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/iwj.71043","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/iwj.71043","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/iwj.71043","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T06:53:31Z","timestamp":1792133611000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/iwj.71043"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":19,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2026,10]]}},"alternative-id":["10.1111\/iwj.71043"],"URL":"https:\/\/doi.org\/10.1111\/iwj.71043","archive":["Portico"],"relation":{},"ISSN":["1742-4801","1742-481X"],"issn-type":[{"value":"1742-4801","type":"print"},{"value":"1742-481X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"2025-12-31","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-09-08","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-10-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e71043"}}