{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T17:37:27Z","timestamp":1771954647185,"version":"3.50.1"},"reference-count":123,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T00:00:00Z","timestamp":1616112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Research into machine learning (ML) for clinical vascular analysis, such as those useful for stroke and coronary artery disease, varies greatly between imaging modalities and vascular regions. Limited accessibility to large diverse patient imaging datasets, as well as a lack of transparency in specific methods, are obstacles to further development. This paper reviews the current status of quantitative vascular ML, identifying advantages and disadvantages common to all imaging modalities. Literature from the past 8 years was systematically collected from MEDLINE\u00ae and Scopus database searches in January 2021. Papers satisfying all search criteria, including a minimum of 50 patients, were further analysed and extracted of relevant data, for a total of 47 publications. Current ML image segmentation, disease risk prediction, and pathology quantitation methods have shown sensitivities and specificities over 70%, compared to expert manual analysis or invasive quantitation. Despite this, inconsistencies in methodology and the reporting of results have prevented inter-model comparison, impeding the identification of approaches with the greatest potential. The clinical potential of this technology has been well demonstrated in Computed Tomography of coronary artery disease, but remains practically limited in other modalities and body regions, particularly due to a lack of routine invasive reference measurements and patient datasets.<\/jats:p>","DOI":"10.3390\/diagnostics11030551","type":"journal-article","created":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T11:18:38Z","timestamp":1616152718000},"page":"551","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6443-0147","authenticated-orcid":false,"given":"Chris","family":"Boyd","sequence":"first","affiliation":[{"name":"Allied Health and Human Performance, University of South Australia, SA 5000, Australia"},{"name":"South Australia Medical Imaging, Adelaide, SA 5000, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7994-2743","authenticated-orcid":false,"given":"Greg","family":"Brown","sequence":"additional","affiliation":[{"name":"Allied Health and Human Performance, University of South Australia, SA 5000, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4430-3276","authenticated-orcid":false,"given":"Timothy","family":"Kleinig","sequence":"additional","affiliation":[{"name":"Department of Neurology, Royal Adelaide Hospital, Adelaide, SA 5000, Australia"},{"name":"Adelaide Medical School, The University of Adelaide, Adelaide, SA 5000, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph","family":"Dawson","sequence":"additional","affiliation":[{"name":"Discipline of Surgery, University of Adelaide, Adelaide, SA 5000, Australia"},{"name":"Department of Vascular &amp; Endovascular Surgery, Royal Adelaide Hospital, Adelaide, SA 5000, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark D.","family":"McDonnell","sequence":"additional","affiliation":[{"name":"Computational Learning Systems Laboratory, UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6043-0166","authenticated-orcid":false,"given":"Mark","family":"Jenkinson","sequence":"additional","affiliation":[{"name":"Oxford Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1315-1735","authenticated-orcid":false,"given":"Eva","family":"Bezak","sequence":"additional","affiliation":[{"name":"Cancer Research Institute, University of South Australia, Adelaide, SA 5001, Australia"},{"name":"Department of Physics, University of Adelaide, Adelaide, SA 5000, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,19]]},"reference":[{"key":"ref_1","unstructured":"GBD 2015 Mortality and Causes of Death Collaborators (2016). 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