Machine Learning in Vascular Surgery: A Systematic Review and Critical Appraisal

Abstract

Background: Machine learning (ML) is a rapidly advancing field with increasing utility in health care. We conducted a systematic review and critical appraisal of ML applications in vascular surgery. Methods: MEDLINE, Embase, and Cochrane CENTRAL were searched from inception to March 1, 2021. Study screening, data extraction, and quality assessment were performed by two independent reviewers, with a third author resolving discrepancies. All original studies reporting ML applications in vascular surgery were included. Publication trends, disease conditions, methodologies, and outcomes were summarized. Critical appraisal was conducted using the PROBAST risk-of-bias and TRIPOD reporting adherence tools. The PROSPERO registration number is CRD42021240310. Findings: We included 212 studies from a pool of 2,235 unique articles. ML techniques were used for diagnosis, prognosis, and image segmentation in carotid stenosis, aortic aneurysm/dissection, peripheral artery disease, diabetic foot ulcer, venous disease, and renal artery stenosis. The number of publications on ML in vascular surgery increased from 1 (1991-1996) to 118 (2016-2021). Most studies were retrospective and single center, with no randomized controlled trials. Area under the receiver operating characteristic curve (AUROC) ranged from 0.61-1.00, with 79.5% [62/78] studies reporting AUROC ≥ 0.80. Out of 22 studies comparing ML techniques to existing prediction tools, clinicians, or traditional regression models, 20 performed better and 2 performed similarly. Overall, 94.8% (201/212) studies had high risk-of-bias and adherence to reporting standards was poor with a rate of 41.4%. Interpretation: Machine learning has been increasingly applied to a broad range of vascular surgical conditions with good predictive value. However, overall risk-of-bias and reporting adherence are suboptimal. Future studies should consider standardized tools such as PROBAST and TRIPOD to improve study quality and clinical applicability. Funding: None. Declaration of Interest: All authors declare no competing interests