ReviewNPJ digital medicine2022
Machine learning in vascular surgery: a systematic review and critical appraisal.
Review in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 8 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
57 citing papers in PubMed, 8 syntheses or guidelines pooled it, 119 citations in OpenAlex.
- Artificial intelligence in pediatric pain: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Comparison of machine learning methods versus traditional Cox regression for survival prediction in cancer using real-world data: a systematic literature review and meta-analysis.BMC medical research methodology · 2025Pooled it
- TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods: a Korean translation.Ewha medical journal · 2025Guideline
- Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review.Journal of pain and symptom management · 2024Pooled it
- Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.BMC medicine · 2024Pooled it
- Systematic evaluation of machine learning models for postoperative surgical site infection prediction.PloS one · 2024Pooled it
- Current status and quality of radiomics studies for predicting outcome in acute ischemic stroke patients: a systematic review and meta-analysis.Frontiers in neurology · 2023Pooled it
- Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis.Frontiers in neurology · 2023Pooled it
- Integrating artificial intelligence into venous thromboembolism care: Predictive models, implementation challenges, and future directions.Journal of vascular surgery. Venous and lymphatic disorders · 2026Article
- Deep-learning-based diagnostic models for transcutaneous oxygen pressure-defined peripheral arterial disease and lower extremity wound healing in patients with diabetic foot.BMC medical informatics and decision making · 2026Article
- Unsupervised cluster analysis identifies risk profiles driving heterogeneity and survival patterns in aortic aneurysm patients.Scientific reports · 2026Article
- Computer vision applications in vascular surgery: a systematic review and critical appraisal.NPJ digital medicine · 2026Article
- Machine learning-based prediction models to assess 30-day outcomes following intervention for superficial venous incompetence of the lower limbs.Journal of vascular surgery cases and innovative techniques · 2026Article
- Risk stratification in intermediate asymptomatic carotid artery disease: a multi-modal approach combining local flow dynamics and systemic clinical profiles.Frontiers in surgery · 2026Article
- An explainable machine learning model for type B aortic dissection identification: development and internal evaluation.Frontiers in public health · 2026Article
- A Comparative Study of Unsupervised and Deep Learning Methods for Automatic Segmentation of Abdominal Aortic Aneurysm on CT Images: Preliminary Results.Advances in experimental medicine and biology · 2026Article
- Advancements in Dental Science through Artificial Intelligence and Machine Learning: A Comprehensive Review.International journal of clinical pediatric dentistry · 2026Review
- Machine Learning and Abdominal Aortic Aneurysm: A New Paradigm in Prediction and Prognosis after Endovascular Aneurysm Repair.Annals of vascular diseases · 2026Review
- Secondary Prevention of AFAIS: Deploying Traditional Regression, Machine Learning, and Deep Learning Models to Validate and Update CHA2DS2-VASc for 90-Day Recurrence.Journal of clinical medicine · 2025Article
- Artificial intelligence and digital health in vascular surgery: a 2-decade bibliometric analysis of research landscapes and evolving frontiers.Journal of robotic surgery · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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. 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. We included 212 studies from a pool of 2235 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. The median area under the receiver operating characteristic curve (AUROC) was 0.88 (range 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%. Despite improvements over time, study quality and reporting remain inadequate. Future studies should consider standardized tools such as PROBAST and TRIPOD to improve study quality and clinical applicability.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.