{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T21:04:12Z","timestamp":1791493452049,"version":"4.3.4"},"reference-count":170,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T00:00:00Z","timestamp":1691971200000},"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>There is an expanding body of literature that describes the application of deep learning and other machine learning and artificial intelligence methods with potential relevance to neuroradiology practice. In this article, we performed a literature review to identify recent developments on the topics of artificial intelligence in neuroradiology, with particular emphasis on large datasets and large-scale algorithm assessments, such as those used in imaging AI competition challenges. Numerous applications relevant to ischemic stroke, intracranial hemorrhage, brain tumors, demyelinating disease, and neurodegenerative\/neurocognitive disorders were discussed. The potential applications of these methods to spinal fractures, scoliosis grading, head and neck oncology, and vascular imaging were also reviewed. The AI applications examined perform a variety of tasks, including localization, segmentation, longitudinal monitoring, diagnostic classification, and prognostication. While research on this topic is ongoing, several applications have been cleared for clinical use and have the potential to augment the accuracy or efficiency of neuroradiologists.<\/jats:p>","DOI":"10.3390\/diagnostics13162670","type":"journal-article","created":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T10:28:13Z","timestamp":1692008893000},"page":"2670","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges"],"prefix":"10.3390","volume":"13","author":[{"given":"Daniel T.","family":"Wagner","sequence":"first","affiliation":[{"name":"Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luke","family":"Tilmans","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Medicine, The Ohio State University, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marilyn","family":"Niedermeier","sequence":"additional","affiliation":[{"name":"College of Medicine, The Ohio State University, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matt","family":"Rohl","sequence":"additional","affiliation":[{"name":"College of Arts and Sciences, The Ohio State University, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sean","family":"Ryan","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Divya","family":"Yadav","sequence":"additional","affiliation":[{"name":"College of Medicine, The Ohio State University, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2781-3047","authenticated-orcid":false,"given":"Noah","family":"Takacs","sequence":"additional","affiliation":[{"name":"College of Medicine, The Ohio State University, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krystle","family":"Garcia-Fraley","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mensur","family":"Koso","sequence":"additional","affiliation":[{"name":"College of Medicine, The Ohio State University, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Engin","family":"Dikici","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6768-6452","authenticated-orcid":false,"given":"Luciano M.","family":"Prevedello","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan V.","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s41747-018-0061-6","article-title":"Artificial intelligence in medical imaging: Threat or opportunity? Radiologists again at the forefront of innovation in medicine","volume":"2","author":"Pesapane","year":"2018","journal-title":"Eur. Radiol. Exp."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e210152","DOI":"10.1148\/ryai.2021210152","article-title":"Imaging AI in practice: A demonstration of future workflow using integration standards","volume":"3","author":"Wiggins","year":"2021","journal-title":"Radiol. Artif. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e190058","DOI":"10.1148\/ryai.2019190058","article-title":"Will artificial intelligence replace radiologists?","volume":"1","author":"Langlotz","year":"2019","journal-title":"Radiol. Artif. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1148\/radiol.2018181928","article-title":"Emerging applications of artificial intelligence in neuro-oncology","volume":"290","author":"Rudie","year":"2019","journal-title":"Radiology"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e210064","DOI":"10.1148\/ryai.210064","article-title":"External validation of deep learning algorithms for radiologic diagnosis: A systematic review","volume":"4","author":"Yu","year":"2022","journal-title":"Radiol. Artif. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1148\/rg.2017160130","article-title":"Machine learning for medical imaging","volume":"37","author":"Erickson","year":"2017","journal-title":"Radiographics"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e153","DOI":"10.1161\/CIR.0000000000001052","article-title":"Heart disease and stroke statistics-2022 update: A report from the American Heart Association","volume":"145","author":"Tsao","year":"2022","journal-title":"Circulation"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e344","DOI":"10.1161\/STR.0000000000000211","article-title":"Guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 guidelines for the early management of acute ischemic stroke: A guideline for healthcare professionals from the American Heart Association\/American Stroke Association","volume":"50","author":"Powers","year":"2019","journal-title":"Stroke"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1056\/NEJMoa1713973","article-title":"Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging","volume":"378","author":"Albers","year":"2018","journal-title":"N. Engl. J. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1056\/NEJMoa1706442","article-title":"Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct","volume":"378","author":"Nogueira","year":"2018","journal-title":"N. Engl. J. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2393","DOI":"10.1161\/STROKEAHA.121.036204","article-title":"Accuracy of automated computer-aided diagnosis for stroke imaging: A critical evaluation of current evidence","volume":"53","author":"Wardlaw","year":"2022","journal-title":"Stroke"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2","DOI":"10.3174\/ajnr.A6883","article-title":"Artificial intelligence and acute stroke imaging","volume":"42","author":"Soun","year":"2021","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1186\/s42466-019-0028-y","article-title":"Computer-aided imaging analysis in acute ischemic stroke\u2014Background and clinical applications","volume":"1","author":"Mokli","year":"2019","journal-title":"Neurol. Res. Pract."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1136\/neurintsurg-2019-015135","article-title":"Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: A systematic review","volume":"12","author":"Murray","year":"2020","journal-title":"J. Neurointerv. Surg."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.neurad.2021.05.001","article-title":"Leveraging artificial intelligence in ischemic stroke imaging","volume":"49","author":"Shafaat","year":"2022","journal-title":"J. Neuroradiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4629859","DOI":"10.1155\/2019\/4629859","article-title":"Application of deep learning in neuroradiology: Brain haemorrhage classification using transfer learning","volume":"2019","author":"Dawud","year":"2019","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1002\/ana.26435","article-title":"Deep learning applications for acute stroke management","volume":"92","author":"Chavva","year":"2022","journal-title":"Ann. Neurol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1161\/STROKEAHA.119.028337","article-title":"Review of perfusion imaging in acute ischemic stroke: From time to tissue","volume":"51","author":"Demeestere","year":"2020","journal-title":"Stroke"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1161\/STROKEAHA.116.015472","article-title":"Predictive value of RAPID assessed perfusion thresholds on final infarct volume in SWIFT PRIME (solitaire with the intention for thrombectomy as primary endovascular treatment)","volume":"48","author":"Mokin","year":"2017","journal-title":"Stroke"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1136\/neurintsurg-2016-012494","article-title":"Admission CT perfusion may overestimate initial infarct core: The ghost infarct core concept","volume":"9","author":"Boned","year":"2017","journal-title":"J. Neurointerv. Surg."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2368","DOI":"10.1161\/STROKEAHA.118.020846","article-title":"Volumetric and spatial accuracy of computed tomography perfusion estimated ischemic core volume in patients with acute ischemic stroke","volume":"49","author":"Hoving","year":"2018","journal-title":"Stroke"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2328","DOI":"10.1161\/STROKEAHA.120.030696","article-title":"Predicting infarct core from computed tomography perfusion in acute ischemia with machine learning: Lessons from the ISLES challenge","volume":"52","author":"Hakim","year":"2021","journal-title":"Stroke"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"104849","DOI":"10.1016\/j.compbiomed.2021.104849","article-title":"Improvement of automatic ischemic stroke lesion segmentation in CT perfusion maps using a learned deep neural network","volume":"137","author":"Soltanpour","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"103487","DOI":"10.1016\/j.compbiomed.2019.103487","article-title":"Acute ischemic stroke lesion core segmentation in CT perfusion images using fully convolutional neural networks","volume":"115","author":"Clerigues","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_25","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). Medical Image Computing and Computer-Assisted Intervention 2015, Springer International Publishing."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"910259","DOI":"10.3389\/fneur.2022.910259","article-title":"Performance of machine learning for tissue outcome prediction in acute ischemic stroke: A systematic review and meta-analysis","volume":"13","author":"Wang","year":"2022","journal-title":"Front. Neurol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1038\/s41597-022-01401-7","article-title":"A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms","volume":"9","author":"Liew","year":"2022","journal-title":"Sci. Data"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2790","DOI":"10.1161\/STROKEAHA.119.026259","article-title":"Automated detection of intracranial large vessel occlusions on computed tomography angiography: A single center experience","volume":"50","author":"Amukotuwa","year":"2019","journal-title":"Stroke"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"656112","DOI":"10.3389\/fneur.2021.656112","article-title":"Validation of a deep learning tool in the detection of intracranial hemorrhage and large vessel occlusion","volume":"12","author":"McLouth","year":"2021","journal-title":"Front. Neurol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3133","DOI":"10.1161\/STROKEAHA.120.030326","article-title":"Deep learning based software to identify large vessel occlusion on noncontrast computed tomography","volume":"51","author":"Crespo","year":"2020","journal-title":"Stroke"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1177\/1971400921998952","article-title":"Validation of an artificial intelligence-driven large vessel occlusion detection algorithm for acute ischemic stroke patients","volume":"34","author":"Rava","year":"2021","journal-title":"Neuroradiol. J."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1159\/000519125","article-title":"Automated large artery occlusion detection in stroke: A single-center validation study of an artificial intelligence algorithm","volume":"51","author":"Rodrigues","year":"2022","journal-title":"Cerebrovasc. Dis."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1026609","DOI":"10.3389\/fneur.2022.1026609","article-title":"Head-to-head comparison of commercial artificial intelligence solutions for detection of large vessel occlusion at a comprehensive stroke center","volume":"13","author":"Schlossman","year":"2022","journal-title":"Front. Neurol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Tolhuisen, M.L., Ponomareva, E., Boers, A.M.M., Jansen, I.G.H., Koopman, M.S., Sales Barros, R., Berkhemer, O.A., van Zwam, W.H., van der Lugt, A., and Majoie, C.B.L.M. (2020). A convolutional neural network for anterior intra-arterial thrombus detection and segmentation on non-contrast computed tomography of patients with acute ischemic stroke. Appl. Sci., 10.","DOI":"10.3390\/app10144861"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"807145","DOI":"10.3389\/fneur.2022.807145","article-title":"Hyperdense artery sign in patients with acute ischemic stroke-automated detection with artificial intelligence-driven software","volume":"13","author":"Weyland","year":"2022","journal-title":"Front. Neurol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"247","DOI":"10.3174\/ajnr.A6923","article-title":"Evaluation of artificial intelligence-powered identification of large-vessel occlusions in a comprehensive stroke center","volume":"42","author":"Saban","year":"2021","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.neurad.2018.03.003","article-title":"Brain CT perfusion improves intracranial vessel occlusion detection on CT angiography","volume":"46","author":"Becks","year":"2019","journal-title":"J. Neuroradiol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1148\/radiol.2020200334","article-title":"Detecting large vessel occlusion at multiphase CT angiography by using a deep convolutional neural network","volume":"297","author":"Stib","year":"2020","journal-title":"Radiology"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1670","DOI":"10.1016\/S0140-6736(00)02237-6","article-title":"Validity and reliability of a quantitative computed tomography score in predicting outcome of hyperacute stroke before thrombolytic therapy. ASPECTS study group. Alberta stroke programme early CT score","volume":"355","author":"Barber","year":"2000","journal-title":"Lancet"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1212\/WNL.0000000000002860","article-title":"Early CT changes in patients admitted for thrombectomy: Intrarater and interrater agreement","volume":"87","author":"Farzin","year":"2016","journal-title":"Neurology"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1186\/s13244-022-01331-3","article-title":"Improving the diagnosis of acute ischemic stroke on non-contrast CT using deep learning: A multicenter study","volume":"13","author":"Chen","year":"2022","journal-title":"Insights Imaging"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"105791","DOI":"10.1016\/j.jstrokecerebrovasdis.2021.105791","article-title":"Alberta stroke program early CT score calculation using the deep learning-based brain hemisphere comparison algorithm","volume":"30","author":"Naganuma","year":"2021","journal-title":"J. Stroke Cerebrovasc. Dis."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1007\/s00234-020-02439-3","article-title":"Automated ASPECT scoring in acute ischemic stroke: Comparison of three software tools","volume":"62","author":"Hoelter","year":"2020","journal-title":"Neuroradiology"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1148\/radiol.2019181228","article-title":"Automated calculation of the Alberta stroke program early CT score: Feasibility and reliability","volume":"291","author":"Maegerlein","year":"2019","journal-title":"Radiology"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1007\/s00234-018-2098-x","article-title":"Automated ASPECT rating: Comparison between the frontier ASPECT score software and the Brainomix software","volume":"60","author":"Goebel","year":"2018","journal-title":"Neuroradiology"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3023","DOI":"10.1002\/hbm.25845","article-title":"Deep learning derived automated ASPECTS on non-contrast CT scans of acute ischemic stroke patients","volume":"43","author":"Cao","year":"2022","journal-title":"Hum. Brain Mapp."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1177\/1747493016681020","article-title":"E-ASPECTS software is non-inferior to neuroradiologists in applying the ASPECT score to computed tomography scans of acute ischemic stroke patients","volume":"12","author":"Nagel","year":"2017","journal-title":"Int. J. Stroke"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3277","DOI":"10.1161\/STROKEAHA.119.026430","article-title":"Automated calculation of Alberta stroke program early CT score: Validation in patients with large hemispheric infarct","volume":"50","author":"Albers","year":"2019","journal-title":"Stroke"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1007\/s00234-018-2066-5","article-title":"Detection of early infarction signs with machine learning-based diagnosis by means of the Alberta stroke program early CT score (ASPECTS) in the clinical routine","volume":"60","author":"Guberina","year":"2018","journal-title":"Neuroradiology"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2032","DOI":"10.1161\/STR.0000000000000069","article-title":"Guidelines for the management of spontaneous intracerebral hemorrhage: A guideline for healthcare professionals from the American Heart Association\/American Stroke Association","volume":"46","author":"Hemphill","year":"2015","journal-title":"Stroke"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1007\/s11940-010-0109-2","article-title":"Critical care management of acute intracerebral hemorrhage","volume":"13","author":"Goldstein","year":"2011","journal-title":"Curr. Treat. Options Neurol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/S2214-109X(13)70089-5","article-title":"Global and regional burden of first-ever ischaemic and haemorrhagic stroke during 1990-2010: Findings from the global burden of disease study 2010","volume":"1","author":"Krishnamurthi","year":"2013","journal-title":"Lancet Glob. Health"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"273","DOI":"10.3174\/ajnr.A6926","article-title":"Automated cerebral hemorrhage detection using RAPID","volume":"42","author":"Heit","year":"2021","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_54","first-page":"1077","article-title":"Automated detection and analysis of subdural hematomas using a machine learning algorithm","volume":"138","author":"Colasurdo","year":"2022","journal-title":"J. Neurosurg."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"e210168","DOI":"10.1148\/ryai.210168","article-title":"Utilization of artificial intelligence-based intracranial hemorrhage detection on emergent noncontrast CT images in clinical workflow","volume":"4","author":"Seyam","year":"2022","journal-title":"Radiol. Artif. Intell."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.1007\/s11547-022-01530-4","article-title":"Accuracy of artificial intelligence for the detection of intracranial hemorrhage and chronic cerebral microbleeds: A systematic review and pooled analysis","volume":"127","author":"Matsoukas","year":"2022","journal-title":"Radiol. Medica"},{"key":"ref_57","unstructured":"(2023, January 26). RSNA Intracranial Hemorrhage Detection. Available online: https:\/\/www.kaggle.com\/competitions\/rsna-intracranial-hemorrhage-detection\/overview."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"e190211","DOI":"10.1148\/ryai.2020190211","article-title":"Construction of a machine learning dataset through collaboration: The RSNA 2019 brain CT hemorrhage challenge","volume":"2","author":"Flanders","year":"2020","journal-title":"Radiol. Artif. Intell."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"102785","DOI":"10.1016\/j.nicl.2021.102785","article-title":"A deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans","volume":"32","author":"Wang","year":"2021","journal-title":"Neuroimage Clin."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"5012","DOI":"10.1007\/s00330-020-07558-2","article-title":"Deep learning shows good reliability for automatic segmentation and volume measurement of brain hemorrhage, intraventricular extension, and peripheral edema","volume":"31","author":"Zhao","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"17858","DOI":"10.1038\/s41598-019-54491-6","article-title":"Intracerebral haemorrhage segmentation in non-contrast CT","volume":"9","author":"Patel","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_62","unstructured":"Islam, M., Sanghani, P., See, A.A.Q., James, M.L., King, N.K.K., and Ren, H. (2019). Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Springer."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"e220096","DOI":"10.1148\/ryai.220096","article-title":"Semantic segmentation of spontaneous intracerebral hemorrhage, intraventricular hemorrhage, and associated edema on CT images using deep learning","volume":"4","author":"Kok","year":"2022","journal-title":"Radiol. Artif. Intell."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1093\/neuonc\/nop066","article-title":"Prevalence estimates for primary brain tumors in the United States by age, gender, behavior, and histology","volume":"12","author":"Porter","year":"2010","journal-title":"Neuro Oncol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"iv1","DOI":"10.1093\/neuonc\/noaa200","article-title":"CBTRUS statistical report: Primary brain and other central nervous system tumors diagnosed in the United States in 2013\u20132017","volume":"22","author":"Ostrom","year":"2020","journal-title":"Neuro Oncol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1186\/s13690-022-00965-5","article-title":"Burden and trends of brain and central nervous system cancer from 1990 to 2019 at the global, regional, and country levels","volume":"80","author":"Fan","year":"2022","journal-title":"Arch. Public Health"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s41016-018-0149-0","article-title":"Management of brain metastases: History and the present","volume":"5","author":"Liu","year":"2019","journal-title":"Chin. Neurosurg. J."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3773","DOI":"10.1200\/JCO.20.01255","article-title":"Survival in patients with brain metastases: Summary report on the updated diagnosis-specific graded prognostic assessment and definition of the eligibility quotient","volume":"38","author":"Sperduto","year":"2020","journal-title":"J. Clin. Oncol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.1148\/rg.2021210037","article-title":"Radiomics in oncology: A practical guide","volume":"41","author":"Shur","year":"2021","journal-title":"Radiographics"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Ozkara, B.B., Chen, M.M., Federau, C., Karabacak, M., Briere, T.M., Li, J., and Wintermark, M. (2023). Deep learning for detecting brain metastases on MRI: A systematic review and meta-analysis. Cancers, 15.","DOI":"10.3390\/cancers15020334"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1093\/neuonc\/noaa232","article-title":"Brain metastasis detection using machine learning: A systematic review and meta-analysis","volume":"23","author":"Cho","year":"2021","journal-title":"Neuro Oncol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2998","DOI":"10.1007\/s00330-021-08427-2","article-title":"A deep convolutional neural network-based automatic detection of brain metastases with and without blood vessel suppression","volume":"5","author":"Kikuchi","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1002\/jmri.26766","article-title":"Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI","volume":"51","author":"Grovik","year":"2020","journal-title":"J. Magn. Reason. Imaging"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1093\/neuonc\/noac025","article-title":"Development and validation of a deep-learning model for detecting brain metastases on 3D post-contrast MRI: A multi-center multi-reader evaluation study","volume":"24","author":"Yin","year":"2022","journal-title":"Neuro Oncol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"5773","DOI":"10.1002\/mp.15863","article-title":"Deep learning for brain metastasis detection and segmentation in longitudinal MRI data","volume":"49","author":"Huang","year":"2022","journal-title":"Med. Phys."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1007\/s11548-021-02326-z","article-title":"Deep learning approaches for automated classification and segmentation of head and neck cancers and brain tumors in magnetic resonance images: A meta-analysis study","volume":"16","author":"Badrigilan","year":"2021","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"105273","DOI":"10.1016\/j.compbiomed.2022.105273","article-title":"An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review","volume":"143","author":"Das","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"9638","DOI":"10.1007\/s00330-021-08035-0","article-title":"Performance of machine learning algorithms for glioma segmentation of brain MRI: A systematic literature review and meta-analysis","volume":"31","author":"Post","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1186\/s12859-022-04794-9","article-title":"A state-of-the-art technique to perform cloud-based semantic segmentation using deep learning 3D U-net architecture","volume":"23","author":"Shaukat","year":"2022","journal-title":"BMC Bioinform."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Sidibe, I., Tensaouti, F., Roques, M., Cohen-Jonathan-Moyal, E., and Laprie, A. (2022). Pseudoprogression in glioblastoma: Role of metabolic and functional MRI-systematic review. Biomedicines, 10.","DOI":"10.3390\/biomedicines10020285"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Sun, Y., Yan, L., Han, Y., Nan, H.Y., Xiao, G., Tian, Q., Pu, W.H., Li, Z.Y., Wei, X.C., and Wang, W. (2021). Differentiation of pseudoprogression from true progressionin glioblastoma patients after standard treatment: A machine learning strategy combinedwith radiomics features from T(1)-weighted contrast-enhanced imaging. BMC Med. Imaging, 21.","DOI":"10.1186\/s12880-020-00545-5"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"021219","DOI":"10.1117\/1.JMI.5.2.021219","article-title":"Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: Implications for personalized radiotherapy planning","volume":"5","author":"Rathore","year":"2018","journal-title":"J. Med. Imaging"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.3174\/ajnr.A5667","article-title":"Deep-learning convolutional neural networks accurately classify genetic mutations in gliomas","volume":"39","author":"Chang","year":"2018","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"001140","DOI":"10.1161\/JAHA.114.001140","article-title":"What are white matter hyperintensities made of? relevance to vascular cognitive impairment","volume":"4","author":"Wardlaw","year":"2015","journal-title":"J. Am. Heart Assoc."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1016\/j.nicl.2017.12.022","article-title":"White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks","volume":"17","author":"Guerrero","year":"2017","journal-title":"Neuroimage Clin."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"c3666","DOI":"10.1136\/bmj.c3666","article-title":"The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: Systematic review and meta-analysis","volume":"341","author":"Debette","year":"2010","journal-title":"BMJ"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.mayocp.2018.10.024","article-title":"Severity of white matter hyperintensities and effects on all-cause mortality in the Mayo Clinic Florida familial cerebrovascular diseases registry","volume":"94","author":"Hasan","year":"2019","journal-title":"Mayo Clin. Proc."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1164","DOI":"10.1093\/brain\/aww008","article-title":"White matter hyperintensities and imaging patterns of brain ageing in the general population","volume":"139","author":"Habes","year":"2016","journal-title":"Brain"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1016\/S1474-4422(13)70124-8","article-title":"Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration","volume":"12","author":"Wardlaw","year":"2013","journal-title":"Lancet Neurol."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/jmri.23671","article-title":"Clinical use of brain volumetry","volume":"37","author":"Giorgio","year":"2013","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1906","DOI":"10.1212\/WNL.0b013e3181a82634","article-title":"Longitudinal pattern of regional brain volume change differentiates normal aging from MCI","volume":"72","author":"Driscoll","year":"2009","journal-title":"Neurology"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.2337\/dc09-1923","article-title":"Progression of cerebral atrophy and white matter hyperintensities in patients with type 2 diabetes","volume":"33","author":"Tiehuis","year":"2010","journal-title":"Diabetes Care"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1159\/000321354","article-title":"Microvascular determinants of cognitive decline and brain volume change in elderly patients with type 2 diabetes","volume":"30","author":"Reijmer","year":"2010","journal-title":"Dement. Geriatr. Cogn. Disord."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1016\/j.neurobiolaging.2006.12.012","article-title":"Brain tissue volumes in the general elderly population: The rotterdam scan study","volume":"29","author":"Ikram","year":"2008","journal-title":"Neurobiol. Aging"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1212\/WNL.56.11.1539","article-title":"Cerebral white matter lesions and subjective cognitive dysfunction: The rotterdam scan study","volume":"56","author":"Oudkerk","year":"2001","journal-title":"Neurology"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.nicl.2017.10.007","article-title":"Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI","volume":"17","author":"Moeskops","year":"2018","journal-title":"NeuroImage Clin."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"3786","DOI":"10.1007\/s00330-020-07684-x","article-title":"To buy or not to buy\u2014Evaluating commercial AI solutions in radiology (the ECLAIR guidelines)","volume":"31","author":"Omoumi","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"102940","DOI":"10.1016\/j.nicl.2022.102940","article-title":"Automatic segmentation of white matter hyperintensities: Validation and comparison with state-of-the-art methods on both multiple sclerosis and elderly subjects","volume":"33","author":"Tran","year":"2022","journal-title":"NeuroImage Clin."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1002\/jmri.22004","article-title":"Automatic segmentation of white matter hyperintensities in the elderly using FLAIR images at 3T","volume":"31","author":"Gibson","year":"2010","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"2837","DOI":"10.1038\/s41598-020-57951-6","article-title":"On brain atlas choice and automatic segmentation methods: A comparison of MAPER & FreeSurfer using three atlas databases","volume":"10","author":"Yaakub","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"8054","DOI":"10.1007\/s00330-022-08832-1","article-title":"Correction to: Radiology Artificial Intelligence: A systematic review and evaluation of methods (RAISE)","volume":"32","author":"Kelly","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1016\/j.neuroimage.2018.07.005","article-title":"Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images","volume":"183","author":"Li","year":"2018","journal-title":"Neuroimage"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"2556","DOI":"10.1109\/TMI.2019.2905770","article-title":"Standardized assessment of automatic segmentation of white matter hyperintensities and results of the WMH segmentation challenge","volume":"38","author":"Kuijf","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/42.712135","article-title":"Design and construction of a realistic digital brain phantom","volume":"17","author":"Collins","year":"1998","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"813696","DOI":"10.1155\/2015\/813696","article-title":"MRBrainS challenge: Online evaluation framework for brain image segmentation in 3T MRI scans","volume":"2015","author":"Mendrik","year":"2015","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_106","first-page":"1993","article-title":"The multimodal brain tumor image segmentation benchmark (BRATS)","volume":"34","author":"Menze","year":"2015","journal-title":"TMI"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Styner, M., Lee, J., Chin, B., Chin, M., Commowick, O., Tran, H., Markovic-Plese, S., Jewells, V., and Warfield, S. (2008). 3D segmentation in the clinic: A grand challenge II: MS lesion segmentation. MIDAS J.","DOI":"10.54294\/lmkqvm"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"13650","DOI":"10.1038\/s41598-018-31911-7","article-title":"Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure","volume":"8","author":"Commowick","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.neucom.2019.12.050","article-title":"Deep convolutional neural network for accurate segmentation and quantification of white matter hyperintensities","volume":"384","author":"Liu","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Elizar, E., Zulkifley, M.A., Muharar, R., Zaman, M.H.M., and Mustaza, S.M. (2022). A review on multiscale-deep-learning applications. Sensors, 22.","DOI":"10.3390\/s22197384"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"1816","DOI":"10.1177\/1352458520970841","article-title":"Rising prevalence of multiple sclerosis worldwide: Insights from the atlas of MS, third edition","volume":"26","author":"Walton","year":"2020","journal-title":"Mult. Scler."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.neuroimage.2016.12.064","article-title":"Longitudinal multiple sclerosis lesion segmentation: Resource and challenge","volume":"148","author":"Carass","year":"2017","journal-title":"NeuroImage"},{"key":"ref_114","unstructured":"Commowick, O., Cervenansky, F., Cotton, F., and Dojat, M. (October, January 27). MSSEG-2 Challenge Proceedings: Multiple Sclerosis New Lesions Segmentation Challenge Using a Data Management and Processing Infrastructure. Proceedings of the MICCAI 2021\u201424th International Conference on Medical Image Computing and Computer Assisted Intervention, Strasbourg, France. Available online: https:\/\/hal.inria.fr\/hal-03358968."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"S10","DOI":"10.1093\/geronb\/gbx169","article-title":"Dementia prevalence in the United States in 2000 and 2012: Estimates based on a nationally representative study","volume":"73","author":"Hudomiet","year":"2018","journal-title":"J. Gerontol. B Psychol. Sci. Soc. Sci."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1016\/j.amjmed.2018.01.022","article-title":"Dementia","volume":"131","author":"Gale","year":"2018","journal-title":"Am. J. Med."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"3336","DOI":"10.1093\/brain\/awq277","article-title":"Brain beta-amyloid measures and magnetic resonance imaging atrophy both predict time-to-progression from mild cognitive impairment to Alzheimer\u2019s disease","volume":"133","author":"Jack","year":"2010","journal-title":"Brain"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"e011146","DOI":"10.1136\/bmjopen-2016-011146","article-title":"Prevalence and determinants of undetected dementia in the community: A systematic literature review and a meta-analysis","volume":"7","author":"Lang","year":"2017","journal-title":"BMJ Open"},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Alzheimer\u2019s Association (2022). 2022 Alzheimer\u2019s disease facts and figures. Alzheimers Dement., 18, 700\u2013789.","DOI":"10.1002\/alz.12638"},{"key":"ref_120","unstructured":"Prince, M.B.R., and Ferri, C. (2011). World Alzheimer Report 2011: The Benefits of Easly Diagnosis and Intervention, Institute of Psychiatry, King\u2019s College."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1186\/s13063-022-06809-5","article-title":"Digital detection of dementia (D(3)): A study protocol for a pragmatic cluster-randomized trial examining the application of patient-reported outcomes and passive clinical decision support systems","volume":"23","author":"Kleiman","year":"2022","journal-title":"Trials"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"119083","DOI":"10.1016\/j.neuroimage.2022.119083","article-title":"Ten years of image analysis and machine learning competitions in dementia","volume":"253","author":"Bron","year":"2022","journal-title":"Neuroimage"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1016\/j.jalz.2016.02.006","article-title":"Crowdsourced estimation of cognitive decline and resilience in Alzheimer\u2019s disease","volume":"12","author":"Allen","year":"2016","journal-title":"Alzheimers Dement."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"1","DOI":"10.59275\/j.melba.2021-2dcc","article-title":"The Alzheimer\u2019s disease prediction of longitudinal evolution (TADPOLE) challenge: Results after 1 year follow up","volume":"1","author":"Marinescu","year":"2021","journal-title":"J. Mach. Learn. Biomed. Imaging"},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"US Preventive Services Task Force, Owens, D.K., Davidson, K.W., Krist, A.H., Barry, M.J., Cabana, M., Caughey, A.B., Doubeni, C.A., Epling, J.W., and Kubik, M. (2020). Screening for cognitive impairment in older adults: US Preventive Services Task Force recommendation statement. JAMA, 323, 757\u2013763.","DOI":"10.1001\/jama.2020.0435"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"710932","DOI":"10.3389\/fpsyt.2021.710932","article-title":"Editorial: Predicting chronological age from structural neuroimaging: The predictive analytics competition 2019","volume":"12","author":"Fisch","year":"2021","journal-title":"Front. Psychiatry"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"627996","DOI":"10.3389\/fpsyt.2021.627996","article-title":"Optimising a simple fully convolutional network for accurate brain age prediction in the PAC 2019 challenge","volume":"12","author":"Gong","year":"2021","journal-title":"Front. Psychiatry"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1016\/j.neuroimage.2015.01.048","article-title":"Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge","volume":"111","author":"Bron","year":"2015","journal-title":"Neuroimage"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"534592","DOI":"10.3389\/fbioe.2020.534592","article-title":"Alzheimer\u2019s disease detection through whole-brain 3D-CNN MRI","volume":"8","author":"Folego","year":"2020","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.nicl.2016.11.025","article-title":"Differential diagnosis of mild cognitive impairment and Alzheimer\u2019s disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry","volume":"13","author":"Sorensen","year":"2016","journal-title":"Neuroimage Clin."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.neubiorev.2017.01.002","article-title":"Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications","volume":"74 Pt A","author":"Vieira","year":"2017","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"941","DOI":"10.3233\/JAD-220806","article-title":"Can T1-weighted magnetic resonance imaging significantly improve mini-mental state examination-based distinguishing between mild cognitive impairment and early-stage Alzheimer\u2019s disease?","volume":"92","author":"Marcisz","year":"2023","journal-title":"J. Alzheimers Dis."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.jalz.2010.03.002","article-title":"The Alzheimer\u2019s Disease Neuroimaging Initiative: Annual change in biomarkers and clinical outcomes","volume":"6","author":"Beckett","year":"2010","journal-title":"Alzheimers Dement."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.neuroimage.2015.07.087","article-title":"Assessing atrophy measurement techniques in dementia: Results from the MIRIAD atrophy challenge","volume":"123","author":"Cash","year":"2015","journal-title":"Neuroimage"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1007\/s00429-013-0687-3","article-title":"Latent feature representation with stacked auto-encoder for AD\/MCI diagnosis","volume":"220","author":"Suk","year":"2015","journal-title":"Brain Struct. Funct."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1136\/annrheumdis-2013-204428","article-title":"The global burden of low back pain: Estimates from the global burden of disease 2010 study","volume":"73","author":"Hoy","year":"2014","journal-title":"Ann. Rheum. Dis."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1016\/j.berh.2010.10.002","article-title":"The epidemiology of low back pain","volume":"24","author":"Hoy","year":"2010","journal-title":"Best Pract. Res. Clin. Rheumatol."},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Cui, Y., Zhu, J., Duan, Z., Liao, Z., Wang, S., and Liu, W. (2022). Artificial intelligence in spinal imaging: Current status and future directions. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph191811708"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"55","DOI":"10.4329\/wjr.v11.i4.55","article-title":"Imaging of the spine: Where do we stand?","volume":"11","author":"Nouh","year":"2019","journal-title":"World J. Radiol."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"102166","DOI":"10.1016\/j.media.2021.102166","article-title":"VerSe: A vertebrae labelling and segmentation benchmark for multi-detector CT images","volume":"73","author":"Sekuboyina","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.media.2018.01.004","article-title":"3D multi-scale FCN with random modality voxel dropout learning for intervertebral disc localization and segmentation from multi-modality MR images","volume":"45","author":"Li","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1177\/0846537120954293","article-title":"Artificial intelligence and deep learning in neuroradiology: Exploring the new frontier","volume":"72","author":"Kaka","year":"2021","journal-title":"Can. Assoc. Radiol. J."},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Altini, N., De Giosa, G., Fragasso, N., Coscia, C., Sibilano, E., Prencipe, B., Hussain, S.M., Brunetti, A., Buongiorno, D., and Guerriero, A. (2021). Segmentation and identification of vertebrae in CT scans using CNN, k-means clustering and k-NN. Informatics, 8.","DOI":"10.3390\/informatics8020040"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"1374","DOI":"10.1007\/s00586-017-4956-3","article-title":"ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist","volume":"26","author":"Jamaludin","year":"2017","journal-title":"Eur. Spine J."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1177\/0846537120947148","article-title":"Applications of artificial intelligence in musculoskeletal imaging: From the request to the report","volume":"72","author":"Gorelik","year":"2021","journal-title":"Can. Assoc. Radiol. J."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1148\/radiol.2021204289","article-title":"Deep learning model for automated detection and classification of central canal, lateral recess, and neural foraminal stenosis at lumbar spine MRI","volume":"300","author":"Hallinan","year":"2021","journal-title":"Radiology"},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1148\/radiol.2017162100","article-title":"Vertebral body compression fractures and bone density: Automated detection and classification on CT images","volume":"284","author":"Burns","year":"2017","journal-title":"Radiology"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1002\/jbmr.3849","article-title":"Artificial intelligence in musculoskeletal imaging: A paradigm shift","volume":"35","author":"Burns","year":"2020","journal-title":"J. Bone Miner. Res."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"20031","DOI":"10.1038\/s41598-020-76866-w","article-title":"Artificial intelligence for the detection of vertebral fractures on plain spinal radiography","volume":"10","author":"Murata","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"110726","DOI":"10.1016\/j.ejrad.2023.110726","article-title":"The role of artificial intelligence in the assessment of the spine and spinal cord","volume":"161","author":"Amrhein","year":"2023","journal-title":"Eur. J. Radiol."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"101542","DOI":"10.1016\/j.media.2019.101542","article-title":"Accurate automated Cobb angles estimation using multi-view extrapolation net","volume":"58","author":"Wang","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"102115","DOI":"10.1016\/j.media.2021.102115","article-title":"Evaluation and comparison of accurate automated spinal curvature estimation algorithms with spinal anterior-posterior X-ray images: The AASCE2019 challenge","volume":"72","author":"Wang","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"9083916","DOI":"10.1155\/2017\/9083916","article-title":"Computer-aided cobb measurement based on automatic detection of vertebral slopes using deep neural network","volume":"2017","author":"Zhang","year":"2017","journal-title":"Int. J. Biomed. Imaging"},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1053\/j.sult.2022.02.006","article-title":"Artificial intelligence in head and neck imaging","volume":"43","author":"Pham","year":"2022","journal-title":"Semin. Ultrasound CT MRI"},{"key":"ref_155","unstructured":"Chen, M., Chen, G., Wang, D., Zhang, J., Di, R., Li, F., Zhou, Z., Piao, S., Li, Y., and Dai, Y. (2021). Deep learning-based segmentation of cerebral aneurysms in 3D TOF-MRA using coarse-to-fine framework. arXiv."},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"DeJohn, C.R., Grant, S.R., and Seshadri, M. (2022). Application of machine learning methods to improve the performance of ultrasound in head and neck oncology: A literature review. Cancers, 14.","DOI":"10.3390\/cancers14030665"},{"key":"ref_157","doi-asserted-by":"crossref","unstructured":"Santer, M., Kloppenburg, M., Gottfried, T., Runge, A., Schmutzhard, J., Vorbach, S.M., Mangesius, J., Riedl, D., Mangesius, S., and Widmann, G. (2022). Current applications of artificial intelligence to classify cervical lymph nodes in patients with head and neck squamous cell Carcinoma\u2014A systematic review. Cancers, 14.","DOI":"10.3390\/cancers14215397"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"772663","DOI":"10.3389\/fonc.2021.772663","article-title":"Machine learning for head and neck cancer: A safe bet?-A clinically oriented systematic review for the radiation oncologist","volume":"11","author":"Volpe","year":"2021","journal-title":"Front. Oncol."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"8612519","DOI":"10.1155\/2017\/8612519","article-title":"Head and neck cancer tumor segmentation using support vector machine in dynamic contrast-enhanced MRI","volume":"2017","author":"Deng","year":"2017","journal-title":"Contrast Media Mol. Imaging"},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1007\/s10278-012-9520-4","article-title":"Region-based nasopharyngeal carcinoma lesion segmentation from MRI using clustering- and classification-based methods with learning","volume":"26","author":"Huang","year":"2013","journal-title":"J. Digit. Imaging"},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"2604","DOI":"10.1007\/s00330-017-5214-0","article-title":"Spectral multi-energy CT texture analysis with machine learning for tissue classification: An investigation using classification of benign parotid tumours as a testing paradigm","volume":"28","author":"Forghani","year":"2018","journal-title":"Eur. Radiol."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1097\/RCT.0000000000000682","article-title":"Computed tomography-based texture analysis to determine human papillomavirus status of oropharyngeal squamous cell carcinoma","volume":"42","author":"Ranjbar","year":"2018","journal-title":"J. Comput. Assist. Tomogr."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.3174\/ajnr.A5106","article-title":"MRI-based texture analysis to differentiate sinonasal squamous cell carcinoma from inverted papilloma","volume":"38","author":"Ramkumar","year":"2017","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s40644-019-0193-9","article-title":"The utility of MRI histogram and texture analysis for the prediction of histological diagnosis in head and neck malignancies","volume":"19","author":"Fujima","year":"2019","journal-title":"Cancer Imaging"},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"1934","DOI":"10.1038\/s41416-021-01386-x","article-title":"Artificial intelligence-based methods in head and neck cancer diagnosis: An overview","volume":"124","author":"Mahmood","year":"2021","journal-title":"Br. J. Cancer"},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1177\/0846537120942134","article-title":"Utilizing artificial intelligence for head and neck cancer outcomes prediction from imaging","volume":"72","author":"Chinnery","year":"2021","journal-title":"Can. Assoc. Radiol. J."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"4829","DOI":"10.1038\/s41467-020-18606-2","article-title":"Rapid vessel segmentation and reconstruction of head and neck angiograms using 3D convolutional neural network","volume":"11","author":"Fu","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"e195600","DOI":"10.1001\/jamanetworkopen.2019.5600","article-title":"Deep Learning\u2013Assisted diagnosis of cerebral aneurysms using the HeadXNet model","volume":"2","author":"Park","year":"2019","journal-title":"JAMA Netw. Open"},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1136\/neurintsurg-2021-018551","article-title":"Rupture risk prediction of cerebral aneurysms using a novel convolutional neural network-based deep learning model","volume":"15","author":"Yang","year":"2022","journal-title":"J. NeuroInterv. Surg."},{"key":"ref_170","doi-asserted-by":"crossref","unstructured":"Ryu, J.Y., Hong, H.K., Cho, H.G., Lee, J.S., Yoo, B.C., Choi, M.H., and Chung, H.Y. (2022). Deep learning for the automatic segmentation of extracranial venous malformations of the head and neck from MR images using 3D U-net. J. Clin. Med., 11.","DOI":"10.3390\/jcm11195593"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/16\/2670\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:33:03Z","timestamp":1760128383000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/16\/2670"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,14]]},"references-count":170,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["diagnostics13162670"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13162670","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,14]]}}}