{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T05:45:56Z","timestamp":1785735956012,"version":"3.56.0"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate preoperative prediction of whether an initially limb-preserving strategy in diabetic foot management will culminate in minor or major amputation remains a clinical challenge. This study aimed to develop and evaluate using two temporally separated cohorts a machine-learning framework using routinely available baseline clinical, laboratory, and selected imaging and vascular variables.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Two temporally separated cohorts were used, with Dataset 1 for model development and Dataset 2 for temporally separated evaluation. A 20-repetition stratified outer-split workflow was implemented, incorporating two-step feature selection, Optuna-based hyperparameter optimization, training-only SMOTE, and threshold tuning to maximize the F2-score under a recall constraint of \u22650.70. Six classifiers were evaluated using average precision (AP), ROC-AUC, recall, precision, specificity, accuracy, and Brier score.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The major-amputation group exhibited a more severe baseline phenotype, including higher inflammatory burden, worse neuropathy and wound severity, and a higher prevalence of necrotizing fasciitis. Internally, multilayer perceptron achieved the highest AP (55.9% \u00b1 13.6%). In external evaluation, k-nearest neighbors achieved the highest AP (65.1% \u00b1 10.2%) and recall (72.8% \u00b1 19.6%), whereas multilayer perceptron showed higher precision and specificity. Key contributors included necrotizing fasciitis, neuropathy severity, hemoglobin, PEDIS classification, and inflammatory indices.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These findings suggest that prediction of amputation level is feasible, validated in a temporally separated cohort, and clinically interpretable, and may support future decision-support applications, although further validation is required before clinical implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1889329","type":"journal-article","created":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T05:30:05Z","timestamp":1785735005000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning\u2013based prediction of major amputation risk after initial limb-preserving surgery in diabetic foot"],"prefix":"10.3389","volume":"17","author":[{"given":"Kerim Bora","family":"Yilmaz","sequence":"first","affiliation":[{"name":"Department of General Surgery, University of Health Sciences Gulhane Training and Research Hospital","place":["Ankara, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gokalp","family":"Tulum","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Engineering Faculty, Istanbul Topkapi University","place":["Istanbul, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ferhat","family":"Cuce","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Health Sciences, Gulhane Training, and Research Hospital","place":["Ankara, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zehra","family":"Simsek","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Engineering Faculty, Istanbul Topkapi University","place":["Istanbul, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Necibe Sare","family":"Mert","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Engineering Faculty, Istanbul Topkapi University","place":["Istanbul, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sema","family":"Horasan Hatipoglu","sequence":"additional","affiliation":[{"name":"Department of General Surgery, University of Health Sciences Gulhane Training and Research Hospital","place":["Ankara, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cansu","family":"Bozkurt","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Health Sciences, Gulhane Training, and Research Hospital","place":["Ankara, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammet Ikbal","family":"Isik","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Health Sciences, Gulhane Training, and Research Hospital","place":["Ankara, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali Murat","family":"Basak","sequence":"additional","affiliation":[{"name":"Department of Orthopedics and Traumatology, University of Health Sciences, Gulhane Training and Research Hospital","place":["Ankara, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Onur","family":"Osman","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Engineering Faculty, Istanbul Topkapi University","place":["Istanbul, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,3]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1002\/dmrr.441","article-title":"Diagnosing and 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