{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T16:06:39Z","timestamp":1778688399346,"version":"3.51.4"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Shanghai Jinshan District Fifth Cycle Outstanding Young Talents Supporting Project","award":["JSKJ-KTYQ-2023-02"],"award-info":[{"award-number":["JSKJ-KTYQ-2023-02"]}]},{"name":"Shanghai Jinshan District Fifth Cycle Outstanding Young Talents Supporting Project","award":["JSKJ-KTYQ-2023-02"],"award-info":[{"award-number":["JSKJ-KTYQ-2023-02"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BioData Mining"],"DOI":"10.1186\/s13040-025-00477-2","type":"journal-article","created":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T12:33:55Z","timestamp":1755779635000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["The application of artificial intelligence models in predicting the risk of diabetic foot: a multicenter study"],"prefix":"10.1186","volume":"18","author":[{"given":"Yao","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyuan","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bichen","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Ju","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenqiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingzhe","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunmin","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunlei","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lihong","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihui","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,21]]},"reference":[{"key":"477_CR1","doi-asserted-by":"publisher","first-page":"101736","DOI":"10.1016\/j.mtbio.2025.101736","volume":"32","author":"X Jia","year":"2025","unstructured":"Jia X, Dou Z, Zhang Y, Yu C, Yang M, Xie H, Lin Y, Liu Z. Application of a novel thermal\/pH-responsive antibacterial Paeoniflorin hydrogel crosslinked with amino acids for accelerated diabetic foot ulcers healing. Mater Today Bio. 2025;32:101736.","journal-title":"Mater Today Bio"},{"issue":"6","key":"477_CR2","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1007\/s11655-024-3912-4","volume":"31","author":"FF Wu","year":"2025","unstructured":"Wu FF, Wang J, Liu GB. Clinical effects of Thread-Dragging therapy on gangrene of Non-ischemic diabetic foot ulcers. Chin J Integr Med. 2025;31(6):552\u20137.","journal-title":"Chin J Integr Med"},{"issue":"10","key":"477_CR3","doi-asserted-by":"publisher","first-page":"882","DOI":"10.12968\/jowc.2022.31.10.882","volume":"31","author":"L Swoboda","year":"2022","unstructured":"Swoboda L, Held J. Impaired wound healing in diabetes. J Wound Care. 2022;31(10):882\u20135.","journal-title":"J Wound Care"},{"key":"477_CR4","unstructured":"Borhade MB, Yashi K, Singh S, Diabetes. and Exercise. 2025 Feb 26. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan\u2013. PMID: 30252351."},{"issue":"12","key":"477_CR5","first-page":"e75517","volume":"16","author":"M Elmubark","year":"2024","unstructured":"Elmubark M, Fahal L, Ali F, Nasr H, Mohamed A, Igbokwe K. Assessment of risk factors leading to amputation among diabetic septic foot patients in khartoum, Sudan. Cureus. 2024;16(12):e75517.","journal-title":"Cureus"},{"issue":"1","key":"477_CR6","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s13098-024-01523-5","volume":"17","author":"MAA Elainein","year":"2025","unstructured":"Elainein MAA, Whdan MM, Samir M, Hamam NG, Mansour M, Mohamed MAM, Snosy MM, Othman MA, Sobieh AS, Saad MG, Labna MA, Allam S. Therapeutic potential of adipose-derived stem cells for diabetic foot ulcers: a systematic review and meta-analysis. Diabetol Metab Syndr. 2025;17(1):9.","journal-title":"Diabetol Metab Syndr"},{"key":"477_CR7","doi-asserted-by":"crossref","unstructured":"Salvotelli L, Stoico V, Perrone F, Cacciatori V, Negri C, Brangani C, Pichiri I, Targher G, Bonora E, Zoppini G. Prevalence of neuropathy in type 2 diabetic patients and its association with other diabetes complications: the Verona diabetic foot screening program. J Diabetes Complications. 2015 Nov-Dec;29(8):1066\u201370.","DOI":"10.1016\/j.jdiacomp.2015.06.014"},{"key":"477_CR8","doi-asserted-by":"crossref","unstructured":"Alonso-Fern\u00e1ndez M, Mediavilla-Bravo JJ, L\u00f3pez-Simarro F, Comas-Samper JM, Carrami\u00f1ana-Barrera F, Mancera-Romero J, de Santiago Nocito A. Grupo de Trabajo de diabetes de SEMERGEN. Evaluation of diabetic foot screening in primary care. Endocrinol Nutr. 2014 Jun-Jul;61(6):311\u20137. English, Spanish.","DOI":"10.1016\/j.endoen.2014.06.008"},{"issue":"1","key":"477_CR9","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1007\/s11936-024-01059-x","volume":"27","author":"G Echefu","year":"2025","unstructured":"Echefu G, Batalik L, Lukan A, Shah R, Nain P, Guha A, Brown SA. The digital revolution in medicine: applications in Cardio-Oncology. Curr Treat Options Cardiovasc Med. 2025;27(1):2.","journal-title":"Curr Treat Options Cardiovasc Med"},{"issue":"1","key":"477_CR10","doi-asserted-by":"publisher","first-page":"15950","DOI":"10.1038\/s41598-025-00292-z","volume":"15","author":"BR Nayana","year":"2025","unstructured":"Nayana BR, Pavithra MN, Chaitra S, Bhuvana Mohini TN, Stephan T, Mohan V, Agarwal N. EEG-based neurodegenerative disease diagnosis: comparative analysis of conventional methods and deep learning models. Sci Rep. 2025;15(1):15950.","journal-title":"Sci Rep"},{"key":"477_CR11","doi-asserted-by":"publisher","first-page":"1526098","DOI":"10.3389\/fendo.2025.1526098","volume":"16","author":"H Tao","year":"2025","unstructured":"Tao H, You L, Huang Y, Chen Y, Yan L, Liu D, Xiao S, Yuan B, Ren M. An interpreting machine learning models to predict amputation risk in patients with diabetic foot ulcers: a multi-center study. Front Endocrinol (Lausanne). 2025;16:1526098.","journal-title":"Front Endocrinol (Lausanne)"},{"issue":"1","key":"477_CR12","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1186\/s12911-024-02595-z","volume":"24","author":"Y Wu","year":"2024","unstructured":"Wu Y, Dong D, Zhu L, Luo Z, Liu Y, Xie X. Interpretable machine learning models for detecting peripheral neuropathy and lower extremity arterial disease in diabetics: an analysis of critical shared and unique risk factors. BMC Med Inf Decis Mak. 2024;24(1):200.","journal-title":"BMC Med Inf Decis Mak"},{"key":"477_CR13","doi-asserted-by":"publisher","first-page":"107418","DOI":"10.1016\/j.neunet.2025.107418","volume":"187","author":"W Lu","year":"2025","unstructured":"Lu W, Wang M, Yu Y, Ma L, Shi Y, Huang Z, Gong M. A novel self-supervised graph clustering method with reliable semi-supervision. Neural Netw. 2025;187:107418.","journal-title":"Neural Netw"},{"issue":"2","key":"477_CR14","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.1109\/TASE.2023.3239004","volume":"21","author":"D Wang","year":"2024","unstructured":"Wang D, Xian X, Song C. Joint learning of failure mode recognition and prognostics for degradation processes. IEEE Trans Autom Sci Eng. 2024;21(2):1421\u201333.","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"477_CR15","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s13721-025-00508-2","volume":"14","author":"SK Padhy","year":"2025","unstructured":"Padhy SK, Mohapatra A, Patra S. WE-XAI: explainable AI for CVD prediction using weighted feature selection and ensemble classifiers. Netw Model Anal Health Inf Bioinforma. 2025;14:13.","journal-title":"Netw Model Anal Health Inf Bioinforma"},{"key":"477_CR16","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/s13721-025-00541-1","volume":"14","author":"SK Padhy","year":"2025","unstructured":"Padhy SK, Mohapatra A, Patra S. A lightweight efficientNetB3 explainable model for enhancing prediction of cardiac arrhythmia using ECG signals. Netw Model Anal Health Inf Bioinforma. 2025;14:49.","journal-title":"Netw Model Anal Health Inf Bioinforma"},{"key":"477_CR17","doi-asserted-by":"publisher","first-page":"e63253","DOI":"10.2196\/63253","volume":"14","author":"A Aravindhan","year":"2025","unstructured":"Aravindhan A, Fenwick E, Wing Dan Chan A, Eyn Kidd Man R, Ee Tang W, Chuan Tan N, Sabanayagam C, Chay J, Pui Ng L, Teen Wong W, Fern Soo W, Wei Lim S, Lamoureux EL. Nonadherence to diabetes complications screening in a multiethnic Asian population: protocol for a mixed methods prospective study. JMIR Res Protoc. 2025;14:e63253.","journal-title":"JMIR Res Protoc"},{"issue":"4","key":"477_CR18","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1055\/s-0039-3402462","volume":"55","author":"RC Ferreira","year":"2020","unstructured":"Ferreira RC. Diabetic foot. Part 1: ulcers and infections. Rev Bras Ortop (Sao Paulo). 2020;55(4):389\u201396.","journal-title":"Rev Bras Ortop (Sao Paulo)"},{"key":"477_CR19","doi-asserted-by":"publisher","first-page":"105744","DOI":"10.1016\/j.puhe.2025.105744","volume":"244","author":"Z Zhou","year":"2025","unstructured":"Zhou Z, Jia Y, Yan H, Xu J, Wang S, Wen J. Risk prediction models for patients with recurrent diabetic foot ulcers: A systematic review. Public Health. 2025;244:105744.","journal-title":"Public Health"},{"key":"477_CR20","doi-asserted-by":"publisher","first-page":"108492","DOI":"10.1016\/j.cmpb.2024.108492","volume":"260","author":"S Vidivelli","year":"2025","unstructured":"Vidivelli S, Padmakumari P, Shanthi P. Multimodal autism detection: deep hybrid model with improved feature level fusion. Comput Methods Programs Biomed. 2025;260:108492.","journal-title":"Comput Methods Programs Biomed"},{"issue":"10","key":"477_CR21","doi-asserted-by":"publisher","first-page":"104105","DOI":"10.1063\/5.0016004","volume":"153","author":"C Schran","year":"2020","unstructured":"Schran C, Brezina K, Marsalek O. Committee neural network potentials control generalization errors and enable active learning. J Chem Phys. 2020;153(10):104105.","journal-title":"J Chem Phys"},{"issue":"3","key":"477_CR22","doi-asserted-by":"publisher","first-page":"583","DOI":"10.3390\/genes14030583","volume":"14","author":"CA Tsai","year":"2023","unstructured":"Tsai CA, Chang YJ. Efficient selection of Gaussian kernel SVM parameters for imbalanced data. Genes (Basel). 2023;14(3):583.","journal-title":"Genes (Basel)"},{"issue":"1","key":"477_CR23","doi-asserted-by":"publisher","first-page":"8968","DOI":"10.1038\/s41598-025-88632-x","volume":"15","author":"T Wu","year":"2025","unstructured":"Wu T, Yang H, Chen J, Kong W. Machine learning-based prediction models for renal impairment in Chinese adults with hyperuricaemia: risk factor analysis. Sci Rep. 2025;15(1):8968.","journal-title":"Sci Rep"},{"key":"477_CR24","doi-asserted-by":"publisher","first-page":"179311","DOI":"10.1016\/j.scitotenv.2025.179311","volume":"976","author":"M Al-Wardy","year":"2025","unstructured":"Al-Wardy M, Zarei E, Nikoo MR. Improving index-based coastal vulnerability assessment using machine learning in Oman. Sci Total Environ. 2025;976:179311.","journal-title":"Sci Total Environ"},{"key":"477_CR25","doi-asserted-by":"crossref","unstructured":"Gao R, Hu M, Li R, Luo X, Suganthan PN, Tanveer M. Stacked ensemble deep random vector functional link network with residual learning for Medium-Scale Time-Series forecasting. IEEE Trans Neural Netw Learn Syst. 2025;PP.","DOI":"10.1109\/TNNLS.2025.3529219"},{"issue":"1","key":"477_CR26","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/s13755-025-00351-9","volume":"13","author":"\u00d3 Escudero-Arnanz","year":"2025","unstructured":"Escudero-Arnanz \u00d3, Mart\u00ednez-Ag\u00fcero S, Mart\u00edn-Palomeque P, Marques G, Mora-Jim\u00e9nez A, \u00c1lvarez-Rodr\u00edguez I, Soguero-Ruiz J. Multimodal interpretable data-driven models for early prediction of multidrug resistance using multivariate time series. Health Inf Sci Syst. 2025;13(1):35.","journal-title":"Health Inf Sci Syst"},{"issue":"1","key":"477_CR27","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1186\/s13244-025-01966-y","volume":"16","author":"W Yue","year":"2025","unstructured":"Yue W, Han R, Wang H, Liang X, Zhang H, Li H, Yang Q. Development and validation of clinical-radiomics deep learning model based on MRI for endometrial cancer molecular subtypes classification. Insights Imaging. 2025;16(1):107.","journal-title":"Insights Imaging"},{"issue":"3","key":"477_CR28","doi-asserted-by":"publisher","first-page":"e42476","DOI":"10.1016\/j.heliyon.2025.e42476","volume":"11","author":"M Kalemati","year":"2025","unstructured":"Kalemati M, Zamani Emani M, Koohi S. InceptionDTA: predicting drug-target binding affinity with biological context features and inception networks. Heliyon. 2025;11(3):e42476.","journal-title":"Heliyon"},{"key":"477_CR29","doi-asserted-by":"publisher","first-page":"103126","DOI":"10.1016\/j.artmed.2025.103126","volume":"165","author":"W Fathy","year":"2025","unstructured":"Fathy W, Emeriaud G, Cheriet F. A comprehensive review of ICU readmission prediction models: from statistical methods to deep learning approaches. Artif Intell Med. 2025;165:103126.","journal-title":"Artif Intell Med"},{"key":"477_CR30","doi-asserted-by":"publisher","first-page":"105269","DOI":"10.1016\/j.jdent.2024.105269","volume":"149","author":"Y Long","year":"2024","unstructured":"Long Y, Xu X, Chen J, Liu S, Li J, Dong Y. An explainable predictive model of direct pulp capping in carious mature permanent teeth. J Dent. 2024;149:105269.","journal-title":"J Dent"},{"issue":"3","key":"477_CR31","first-page":"e81118","volume":"17","author":"Z Khan","year":"2025","unstructured":"Khan Z, Zeb S, Ashraf, Rumman, Ali A, Aleem F, Omair F. The relationship between plasma fibrinogen levels and the severity of diabetic foot ulcers in diabetic patients. Cureus. 2025;17(3):e81118.","journal-title":"Cureus"},{"issue":"4S Suppl 2","key":"477_CR32","doi-asserted-by":"publisher","first-page":"S349","DOI":"10.1097\/SAP.0000000000004209","volume":"94","author":"AJ Kammien","year":"2025","unstructured":"Kammien AJ, Evans BG, Hu KG, Colen DL. Geographic region and insurance status predict access to salvage procedures for diabetic Lower-Extremity wounds in the united States. Ann Plast Surg. 2025;94(4S Suppl 2):S349\u201352.","journal-title":"Ann Plast Surg"},{"issue":"19","key":"477_CR33","doi-asserted-by":"publisher","first-page":"e37635","DOI":"10.1016\/j.heliyon.2024.e37635","volume":"10","author":"W Xiaoling","year":"2024","unstructured":"Xiaoling W, Shengmei Z, BingQian W, Wen L, Shuyan G, Hanbei C, Chenjie Q, Yao D, Jutang L. Enhancing diabetic foot ulcer prediction with machine learning: A focus on localized examinations. Heliyon. 2024;10(19):e37635.","journal-title":"Heliyon"}],"container-title":["BioData Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13040-025-00477-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13040-025-00477-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13040-025-00477-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T10:06:37Z","timestamp":1757412397000},"score":1,"resource":{"primary":{"URL":"https:\/\/biodatamining.biomedcentral.com\/articles\/10.1186\/s13040-025-00477-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,21]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["477"],"URL":"https:\/\/doi.org\/10.1186\/s13040-025-00477-2","relation":{},"ISSN":["1756-0381"],"issn-type":[{"value":"1756-0381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,21]]},"assertion":[{"value":"30 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"No potential conflicts of interest relevant to this article were reported.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Duality of interest"}},{"value":"The study was approved by the Jinshan Institutional Ethics Committee (JIEC 2024-S35) and conducted in accordance with the Declaration of Helsinki. All participants provided informed consent.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics declarations"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}}],"article-number":"57"}}