{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T18:37:22Z","timestamp":1771267042746,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T00:00:00Z","timestamp":1659484800000},"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>We conducted a systematic review of the current status of machine learning (ML) algorithms\u2019 ability to identify multiple brain diseases, and we evaluated their applicability for improving existing scan acquisition and interpretation workflows. PubMed Medline, Ovid Embase, Scopus, Web of Science, and IEEE Xplore literature databases were searched for relevant studies published between January 2017 and February 2022. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. The applicability of ML algorithms for successful workflow improvement was qualitatively assessed based on the satisfaction of three clinical requirements. A total of 19 studies were included for qualitative synthesis. The included studies performed classification tasks (n = 12) and segmentation tasks (n = 7). For classification algorithms, the area under the receiver operating characteristic curve (AUC) ranged from 0.765 to 0.997, while accuracy, sensitivity, and specificity ranged from 80% to 100%, 72% to 100%, and 65% to 100%, respectively. For segmentation algorithms, the Dice coefficient ranged from 0.300 to 0.912. No studies satisfied all clinical requirements for successful workflow improvements due to key limitations pertaining to the study\u2019s design, study data, reference standards, and performance reporting. Standardized reporting guidelines tailored for ML in radiology, prospective study designs, and multi-site testing could help alleviate this.<\/jats:p>","DOI":"10.3390\/diagnostics12081878","type":"journal-article","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T20:52:01Z","timestamp":1659559921000},"page":"1878","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Automated Identification of Multiple Findings on Brain MRI for Improving Scan Acquisition and Interpretation Workflows: A Systematic Review"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4372-5821","authenticated-orcid":false,"given":"Kaining","family":"Sheng","sequence":"first","affiliation":[{"name":"Department of Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark"},{"name":"Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5763-6892","authenticated-orcid":false,"given":"Cecilie M\u00f8rck","family":"Offersen","sequence":"additional","affiliation":[{"name":"Department of Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark"},{"name":"Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4941-0265","authenticated-orcid":false,"given":"Jon","family":"Middleton","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Copenhagen, 2200 Copenhagen, Denmark"},{"name":"Cerebriu A\/S, 1127 Copenhagen, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6724-7281","authenticated-orcid":false,"given":"Jonathan Frederik","family":"Carlsen","sequence":"additional","affiliation":[{"name":"Department of Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark"},{"name":"Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas Clement","family":"Truelsen","sequence":"additional","affiliation":[{"name":"Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark"},{"name":"Department of Neurology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akshay","family":"Pai","sequence":"additional","affiliation":[{"name":"Department of Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark"},{"name":"Cerebriu A\/S, 1127 Copenhagen, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jacob","family":"Johansen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Copenhagen, 2200 Copenhagen, Denmark"},{"name":"Cerebriu A\/S, 1127 Copenhagen, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9380-1688","authenticated-orcid":false,"given":"Michael Bachmann","family":"Nielsen","sequence":"additional","affiliation":[{"name":"Department of Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark"},{"name":"Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1212\/CON.0000000000000391","article-title":"Introduction to Magnetic Resonance Imaging for Neurologists","volume":"22","author":"Radue","year":"2016","journal-title":"Contin. 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