Machine learning in vascular surgery: a systematic review and critical appraisal - PubMed

• Updated on: 2022-06-11
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doi: 10.1038/s41746-021-00552-y.
Machine learning in vascular surgery: a systematic review and critical appraisal
Affiliations
1 Department of Surgery, University of Toronto, 149 College St, Toronto, ON, M5T 1P5, Canada.
2 Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada.
3 Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada.
4 Health Sciences Library, St. Michael's Hospital, Unity Health Toronto, 209 Victoria St, Toronto, ON, M5B 1T8, Canada.
5 Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, 209 Victoria St, Toronto, ON, M5B 1T8, Canada.
6 Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, 155 College St, Toronto, ON, M5T 3M7, Canada.
7 Leslie Dan Faculty of Pharmacy, University of Toronto, 144 College St, Toronto, ON, M5S 3M2, Canada.
8 Department of Surgery, University of Toronto, 149 College St, Toronto, ON, M5T 1P5, Canada. mohammed.al-omran@unityhealth.to.
9 Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada. mohammed.al-omran@unityhealth.to.
10 Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada. mohammed.al-omran@unityhealth.to.
11 Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, 209 Victoria St, Toronto, ON, M5B 1T8, Canada. mohammed.al-omran@unityhealth.to.
12 Institute of Medical Science, University of Toronto, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada. mohammed.al-omran@unityhealth.to.
13 Department of Surgery, King Saud University, ZIP 4545, Riyadh, 11451, Kingdom of Saudi Arabia. mohammed.al-omran@unityhealth.to.
PMID: 35046493
Machine learning in vascular surgery: a systematic review and critical appraisal
Ben Li et al. NPJ Digit Med.
2022
doi: 10.1038/s41746-021-00552-y.
Authors
Affiliations
1 Department of Surgery, University of Toronto, 149 College St, Toronto, ON, M5T 1P5, Canada.
2 Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada.
3 Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada.
4 Health Sciences Library, St. Michael's Hospital, Unity Health Toronto, 209 Victoria St, Toronto, ON, M5B 1T8, Canada.
5 Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, 209 Victoria St, Toronto, ON, M5B 1T8, Canada.
6 Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, 155 College St, Toronto, ON, M5T 3M7, Canada.
7 Leslie Dan Faculty of Pharmacy, University of Toronto, 144 College St, Toronto, ON, M5S 3M2, Canada.
8 Department of Surgery, University of Toronto, 149 College St, Toronto, ON, M5T 1P5, Canada. mohammed.al-omran@unityhealth.to.
9 Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada. mohammed.al-omran@unityhealth.to.
10 Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada. mohammed.al-omran@unityhealth.to.
11 Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, 209 Victoria St, Toronto, ON, M5B 1T8, Canada. mohammed.al-omran@unityhealth.to.
12 Institute of Medical Science, University of Toronto, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada. mohammed.al-omran@unityhealth.to.
13 Department of Surgery, King Saud University, ZIP 4545, Riyadh, 11451, Kingdom of Saudi Arabia. mohammed.al-omran@unityhealth.to.
PMID: 35046493
Format
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.
© 2022. The Author(s).
References
Baştanlar, Y. & Özuysal, M. Introduction to machine learning. Methods Mol. Biol. 1107, 105–128 (2014). - PubMed
Rowe, M. An introduction to machine learning for clinicians. Acad. Med. J. Assoc. Am. Med. Coll. 94, 1433–1436 (2019).
Artificial intelligence in healthcare market with Covid-19 impact analysis by offering, technology, end-use application, end user and region – global forecast to 2026. https://www.reportlinker.com/p04897122/Artificial-Intelligence-in-Health... . (2021).
Panch, T., Szolovits, P. & Atun, R. Artificial intelligence, machine learning and health systems. J. Glob. Health 8, 020303 (2018). - PubMed - PMC
Bzdok, D., Altman, N. & Krzywinski, M. Statistics versus machine learning. Nat. Methods 15, 233–234 (2018). - PubMed - PMC
Show all 72 references