{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T17:21:10Z","timestamp":1790961670470,"version":"4.1.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,23]]},"DOI":"10.7759\/cureus.88568","type":"journal-article","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T09:49:47Z","timestamp":1753264187000},"source":"Crossref","is-referenced-by-count":3,"title":["Machine Learning in Predicting Wound Healing and Limb Salvage Outcomes Following Lower Limb Revascularization: A Systematic Review of Prognostic Accuracy"],"prefix":"10.7759","author":[{"given":"Abdulkreem","family":"Al-Juhani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rodan","family":"Desoky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah","family":"Abdullah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elsayed","family":"Younes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sultan","family":"Khoja","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sereen S","family":"Aljohani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdalrahman","family":"Desoky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.123.033194","article-title":"Predicting outcomes following lower extremity endovascular revascularization using machine learning","volume":"13","author":"Ben Li","year":"2024","unstructured":"Ben Li, Badr Aljabri, Raj Verma, et al.. Predicting outcomes following lower extremity endovascular revascularization using machine learning. J Am Heart Assoc. 2024, 13:e033194. 10.1161\/JAHA.123.033194","journal-title":"J Am Heart Assoc"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1097\/SLA.0000000000004132","article-title":"Predicting the outcome of limb revascularization in patients with lower-extremity arterial trauma: Development and external validation of a supervised machine-learning algorithm to support surgical decisions","volume":"272","author":"Perkins ZB","year":"2020","unstructured":"Perkins ZB, Yet B, Sharrock A, Rickard R, Marsh W, Rasmussen TE, Tai NR. Predicting the outcome of limb revascularization in patients with lower-extremity arterial trauma: Development and external validation of a supervised machine-learning algorithm to support surgical decisions. Ann Surg. 2020, 272:564-72. 10.1097\/SLA.0000000000004132","journal-title":"Ann Surg"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2025.1540503","article-title":"Using machine learning models to predict post-revascularization thrombosis in PAD","volume":"8","author":"Ghandour S","year":"2025","unstructured":"Ghandour S, Rodriguez Alvarez AA, Cieri IF, et al.. Using machine learning models to predict post-revascularization thrombosis in PAD. Front Artif Intell. 2025, 8:1540503. 10.3389\/frai.2025.1540503","journal-title":"Front Artif Intell"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-52944-1","article-title":"Predicting outcomes following lower extremity open revascularization using machine learning","volume":"14","author":"Li B","year":"2024","unstructured":"Li B, Verma R, Beaton D, et al.. Predicting outcomes following lower extremity open revascularization using machine learning. Sci Rep. 2024, 14:2899. 10.1038\/s41598-024-52944-1","journal-title":"Sci Rep"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2024.2350","article-title":"Machine learning to predict outcomes of endovascular intervention for patients with PAD","volume":"7","author":"Li B","year":"2024","unstructured":"Li B, Warren BE, Eisenberg N, et al.. Machine learning to predict outcomes of endovascular intervention for patients with PAD. JAMA Netw Open. 2024, 7:e242350. 10.1001\/jamanetworkopen.2024.2350","journal-title":"JAMA Netw Open"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvs.2021.06.478","article-title":"Machine learning analysis of multispectral imaging and clinical risk factors to predict amputation wound healing","volume":"75","author":"Squiers JJ","year":"2022","unstructured":"Squiers JJ, Thatcher JE, Bastawros DS, et al.. Machine learning analysis of multispectral imaging and clinical risk factors to predict amputation wound healing. J Vasc Surg. 2022, 75:279-85. 10.1016\/j.jvs.2021.06.478","journal-title":"J Vasc Surg"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s00270-022-03111-4","article-title":"Interpretable machine learning for the prediction of amputation risk following lower extremity infrainguinal endovascular interventions for peripheral arterial disease","volume":"45","author":"Cox M","year":"2022","unstructured":"Cox M, Reid N, Panagides JC, et al.. Interpretable machine learning for the prediction of amputation risk following lower extremity infrainguinal endovascular interventions for peripheral arterial disease. Cardiovasc Intervent Radiol. 2022, 45:633-40. 10.1007\/s00270-022-03111-4","journal-title":"Cardiovasc Intervent Radiol"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcard.2023.04.040","article-title":"Application of machine learning algorithms in electronic medical records to predict amputation-free survival after first revascularization in patients with peripheral artery disease","volume":"383","author":"Liu Y","year":"2023","unstructured":"Liu Y, Xue J, Jiang J. Application of machine learning algorithms in electronic medical records to predict amputation-free survival after first revascularization in patients with peripheral artery disease. Int J Cardiol. 2023, 383:175-84. 10.1016\/j.ijcard.2023.04.040","journal-title":"Int J Cardiol"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3390\/medicina61020277","article-title":"Predictive factors of wound healing and limb salvage after successful below-the-knee endovascular angioplasty in patients with diabetic foot ulcer: A retrospective study","volume":"61","author":"Shin CS","year":"2025","unstructured":"Shin CS, Yoo KC. Predictive factors of wound healing and limb salvage after successful below-the-knee endovascular angioplasty in patients with diabetic foot ulcer: A retrospective study. Medicina (Kaunas). 2025, 61:277. 10.3390\/medicina61020277","journal-title":"Medicina (Kaunas)"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1177\/17085381241312467","article-title":"Limb salvage prediction in peripheral artery disease patients using angiographic computer vision","author":"Rusinovich Y","year":"2025","unstructured":"Rusinovich Y, Liashko V, Rusinovich V, et al.. Limb salvage prediction in peripheral artery disease patients using angiographic computer vision. Vascular. 2025, 17085381241312467. 10.1177\/17085381241312467","journal-title":"Vascular"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00552-y","article-title":"Machine learning in vascular surgery: A systematic review and critical appraisal","volume":"5","author":"Li B","year":"2022","unstructured":"Li B, Feridooni T, Cuen-Ojeda C, Kishibe T, de Mestral C, Mamdani M, Al-Omran M. Machine learning in vascular surgery: A systematic review and critical appraisal. NPJ Digit Med. 2022, 5:7. 10.1038\/s41746-021-00552-y","journal-title":"NPJ Digit Med"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0293684","article-title":"Predicting amputation using machine learning: A systematic review","volume":"18","author":"Yao PF","year":"2023","unstructured":"Yao PF, Diao YD, McMullen EP, Manka M, Murphy J, Lin C. Predicting amputation using machine learning: A systematic review. PLoS One. 2023, 18:e0293684. 10.1371\/journal.pone.0293684","journal-title":"PLoS One"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2405.05297","article-title":"Deep learning method to predict wound healing progress based on collagen fibers in wound tissue","author":"He J","year":"2024","unstructured":"He J, Wang X, Chen L, Cai Y, Wang Z. Deep learning method to predict wound healing progress based on collagen fibers in wound tissue. arXiv. 2024, 10.48550\/arXiv.2405.05297","journal-title":"arXiv"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2025.1526098","article-title":"An interpreting machine learning models to predict amputation risk in patients with diabetic foot ulcers: A multi-center study","volume":"16","author":"Tao H","year":"2025","unstructured":"Tao H, You L, Huang Y, et al.. 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. 10.3389\/fendo.2025.1526098","journal-title":"Front Endocrinol (Lausanne)"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-025-02944-6","article-title":"The role of explainable artificial intelligence in disease prediction: A systematic literature review and future research directions","volume":"25","author":"Alkhanbouli R","year":"2025","unstructured":"Alkhanbouli R, Matar Abdulla Almadhaani H, Alhosani F, Simsekler MC. The role of explainable artificial intelligence in disease prediction: A systematic literature review and future research directions. BMC Med Inform Decis Mak. 2025, 25:110. 10.1186\/s12911-025-02944-6","journal-title":"BMC Med Inform Decis Mak"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2024.1325434","article-title":"The role of machine learning in advancing diabetic foot: A review","volume":"15","author":"Guan H","year":"2024","unstructured":"Guan H, Wang Y, Niu P, et al.. The role of machine learning in advancing diabetic foot: A review. Front Endocrinol (Lausanne). 2024, 15:1325434. 10.3389\/fendo.2024.1325434","journal-title":"Front Endocrinol (Lausanne)"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2207899","article-title":"Surgery or endovascular therapy for chronic limb-threatening ischemia","volume":"387","author":"Farber A","year":"2022","unstructured":"Farber A, Menard MT, Conte MS, et al.. Surgery or endovascular therapy for chronic limb-threatening ischemia. N Engl J Med. 2022, 387:2305-16. 10.1056\/NEJMoa2207899","journal-title":"N Engl J Med"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins GS","year":"2024","unstructured":"Collins GS, Moons KG, Dhiman P, et al.. TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024, 385:e078378. 10.1136\/bmj-2023-078378","journal-title":"BMJ"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.20517\/ais.2024.68","article-title":"A review of artificial intelligence in wound care","volume":"4","author":"Ganesan O","year":"2024","unstructured":"Ganesan O, Morris MX, Guo L, Orgill D. A review of artificial intelligence in wound care. Art Int Surg. 2024, 4:364-75. 10.20517\/ais.2024.68","journal-title":"Art Int Surg"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1111\/iwj.13691","article-title":"An explainable machine learning model for predicting in-hospital amputation rate of patients with diabetic foot ulcer","volume":"19","author":"Xie P","year":"2022","unstructured":"Xie P, Li Y, Deng B, et al.. An explainable machine learning model for predicting in-hospital amputation rate of patients with diabetic foot ulcer. Int Wound J. 2022, 19:910-8. 10.1111\/iwj.13691","journal-title":"Int Wound J"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1186\/s13098-024-01360-6","article-title":"Prediction models for amputation after diabetic foot: Systematic review and critical appraisal","volume":"16","author":"Huang J","year":"2024","unstructured":"Huang J, Yang J, Qi H, Xu M, Xu X, Zhu Y. Prediction models for amputation after diabetic foot: Systematic review and critical appraisal. Diabetol Metab Syndr. 2024, 16:126. 10.1186\/s13098-024-01360-6","journal-title":"Diabetol Metab Syndr"},{"key":"ref22","doi-asserted-by":"crossref","DOI":"10.1016\/j.jvsvi.2024.100179","article-title":"The role of artificial intelligence in vascular care","volume":"3","author":"Nimmagadda N","year":"2025","unstructured":"Nimmagadda N, Aboian E, Kiang S, Fischer U. The role of artificial intelligence in vascular care. JVS-Vascular Insights. 2025, 3:100179.","journal-title":"JVS-Vascular Insights"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2022.33946","article-title":"Randomized clinical trials of machine learning interventions in health care: A systematic review","volume":"5","author":"Plana D","year":"2022","unstructured":"Plana D, Shung DL, Grimshaw AA, Saraf A, Sung JJY, Kann BH. Randomized clinical trials of machine learning interventions in health care: A systematic review. JAMA Netw Open. 2022, 5:e2233946. 10.1001\/jamanetworkopen.2022.33946","journal-title":"JAMA Netw Open"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.123.031880","article-title":"Artificial intelligence of arterial Doppler waveforms to predict major adverse outcomes among patients evaluated for peripheral artery disease","volume":"13","author":"McBane RD 2nd","year":"2024","unstructured":"McBane RD 2nd, Murphree DH, Liedl D, et al.. Artificial intelligence of arterial Doppler waveforms to predict major adverse outcomes among patients evaluated for peripheral artery disease. J Am Heart Assoc. 2024, 13:e031880. 10.1161\/JAHA.123.031880","journal-title":"J Am Heart Assoc"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.3390\/biomimetics9080465","article-title":"Current applications and future perspectives of artificial and biomimetic intelligence in vascular surgery and peripheral artery disease","volume":"9","author":"Martelli E","year":"2024","unstructured":"Martelli E, Capoccia L, Di Francesco M, et al.. Current applications and future perspectives of artificial and biomimetic intelligence in vascular surgery and peripheral artery disease. Biomimetics (Basel). 2024, 9:465. 10.3390\/biomimetics9080465","journal-title":"Biomimetics (Basel)"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.2196\/43963","article-title":"Development and integration of machine learning algorithm to identify peripheral arterial disease: Multistakeholder qualitative study","volume":"7","author":"Wang SM","year":"2023","unstructured":"Wang SM, Hogg HD, Sangvai D, et al.. Development and integration of machine learning algorithm to identify peripheral arterial disease: Multistakeholder qualitative study. JMIR Form Res. 2023, 7:e43963. 10.2196\/43963","journal-title":"JMIR Form Res"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-20835-y","article-title":"Computerised prediction of healing for venous leg ulcers","volume":"12","author":"Ngo QC","year":"2022","unstructured":"Ngo QC, Ogrin R, Kumar DK. Computerised prediction of healing for venous leg ulcers. Sci Rep. 2022, 12:17962. 10.1038\/s41598-022-20835-y","journal-title":"Sci Rep"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m689","article-title":"Artificial intelligence versus clinicians: Systematic review of design, reporting standards, and claims of deep learning studies","volume":"368","author":"Nagendran M","year":"2020","unstructured":"Nagendran M, Chen Y, Lovejoy CA, et al.. Artificial intelligence versus clinicians: Systematic review of design, reporting standards, and claims of deep learning studies. BMJ. 2020, 368:m689. 10.1136\/bmj.m689","journal-title":"BMJ"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1161\/CIR.0000000000001192","article-title":"Current status and principles for the treatment and prevention of diabetic foot ulcers in the cardiovascular patient population: A scientific statement from the American Heart Association","volume":"149","author":"Gallagher KA","year":"2024","unstructured":"Gallagher KA, Mills JL, Armstrong DG, et al.. Current status and principles for the treatment and prevention of diabetic foot ulcers in the cardiovascular patient population: A scientific statement from the American Heart Association. Circulation. 2024, 149:e232-53. 10.1161\/CIR.0000000000001192","journal-title":"Circulation"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2020-045253","article-title":"Cohort study evaluating the burden of wounds to the UK's National Health Service in 2017\/2018: Update from 2012\/2013","volume":"10","author":"Guest JF","year":"2020","unstructured":"Guest JF, Fuller GW, Vowden P. Cohort study evaluating the burden of wounds to the UK's National Health Service in 2017\/2018: Update from 2012\/2013. BMJ Open. 2020, 10:e045253. 10.1136\/bmjopen-2020-045253","journal-title":"BMJ Open"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1186\/s13047-023-00610-6","article-title":"Risk prediction models for diabetic foot ulcer development or amputation: A review of reviews","volume":"16","author":"Kaka AS","year":"2023","unstructured":"Kaka AS, Landsteiner A, Ensrud KE, et al.. Risk prediction models for diabetic foot ulcer development or amputation: A review of reviews. J Foot Ankle Res. 2023, 16:13. 10.1186\/s13047-023-00610-6","journal-title":"J Foot Ankle Res"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejvs.2023.08.014","article-title":"Poor survival in women referred for treatment of thoracic aortic aneurysms: Might utilisation of the aortic size index improve outcomes?","volume":"66","author":"Pouncey AL","year":"2023","unstructured":"Pouncey AL, Patel D, Freeman C, Sastry P, Bicknell C, Large S, Sharples L. Poor survival in women referred for treatment of thoracic aortic aneurysms: Might utilisation of the aortic size index improve outcomes?. Eur J Vasc Endovasc Surg. 2023, 66:e66. 10.1016\/j.ejvs.2023.08.014","journal-title":"Eur J Vasc Endovasc Surg"}],"container-title":["Cureus"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cureus.com\/articles\/378884-machine-learning-in-predicting-wound-healing-and-limb-salvage-outcomes-following-lower-limb-revascularization-a-systematic-review-of-prognostic-accuracy","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T09:49:48Z","timestamp":1753264188000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cureus.com\/articles\/378884-machine-learning-in-predicting-wound-healing-and-limb-salvage-outcomes-following-lower-limb-revascularization-a-systematic-review-of-prognostic-accuracy"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,23]]},"references-count":32,"URL":"https:\/\/doi.org\/10.7759\/cureus.88568","relation":{},"ISSN":["2168-8184"],"issn-type":[{"value":"2168-8184","type":"print"}],"subject":[],"published":{"date-parts":[[2025,7,23]]}}}