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Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Diabetic foot ulcer (DFU) is a common and severe complication of diabetes that leads to amputation if not effectively managed. Intelligent offloading or rehabilitation devices or boots are used to manage or observe the ulcer and the process of treatment which requires the segmentation and clustering analysis of 3D scanned foot models. This study examines the effectiveness of two widely used clustering algorithms, K-Means and Gaussian Mixture Models (GMM), in segmenting scanned foot models to use in rehabilitation boots. The performance of K-Means and GMM was compared across 98 foot models. GMM consistently achieved higher silhouette scores (0.58 vs. 0.42 for\n                    <jats:italic>K<\/jats:italic>\n                    \u2009=\u20095), lower Davies-Bouldin scores (0.47 vs. 0.54 for\n                    <jats:italic>K<\/jats:italic>\n                    \u2009=\u20095), and more stable clustering across anatomical sections, despite requiring almost 20% more computation time. These results highlight GMM\u2019s superior ability to capture the complex nonlinear structures of diabetic feet, with implications for more precise and personalized offloading boot design. Precise segmentation of scanned foot models is a crucial step in various anatomical and medical applications, such as the design of custom orthotic devices, rehabilitation offloading boots, and the analysis of foot biomechanics\u2014particularly useful within DFU management.\n                  <\/jats:p>","DOI":"10.1007\/s10278-025-01746-6","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T18:00:30Z","timestamp":1762797630000},"page":"3759-3772","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Segmentation and Clustering Analysis of Scanned Foot Models for Intelligent Diabetic Foot Ulcer Offloading Boots"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3388-9398","authenticated-orcid":false,"given":"Yaser","family":"Sabzehmeidani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5729-464X","authenticated-orcid":false,"given":"Andrei-Alexandru","family":"Popa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"1746_CR1","doi-asserted-by":"publisher","unstructured":"Goyal M, Yap MH, Reeves ND, Rajbhandari S, Spragg J. 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Ethical approval was obtained from the SDU Research Ethics Committee (REC) under approval number 12.342.No animals were involved in this research.","order":1,"name":"Ethics","label":"Research Involving Human Participants","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Informed consent was obtained from all volunteers prior to participation. Participants were provided with detailed information about the study\u2019s purpose, procedures, potential risks, and their right to withdraw at any time.","order":2,"name":"Ethics","label":"Informed Consent","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}]}}