{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T12:09:49Z","timestamp":1790078989768,"version":"4.0.1"},"reference-count":38,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["23K08521"],"award-info":[{"award-number":["23K08521"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"award":["23K08521"],"award-info":[{"award-number":["23K08521"]}],"id":[{"id":"https:\/\/ror.org\/00hhkn466","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Clin. Med.","JCM"],"abstract":"<jats:p>Background\/Objectives: Despite high sensitivity in deep learning models for identifying candidate locations of unruptured cerebral aneurysms (UCAs), excessive false-positive (FP) findings and insufficient characterization of their anatomical distribution remain barriers to clinical integration. We evaluated a multi-stage deep learning framework for reducing anatomy-specific FPs in TOF-MRA-based UCA screening. Methods: This retrospective multicenter study analyzed TOF-MRA images from 404 scans across seven institutions. A baseline model (Model-A) was compared with an updated model (Model-B) incorporating a secondary cluster-filtering algorithm. Reference standards were established by expert reviewers. Aneurysm-wise sensitivity was compared using McNemar\u2019s test, FPs per case using the Wilcoxon signed-rank test, and FP distributions by vascular location. Results: Aneurysm-wise sensitivity was 94.3% (231\/245) for Model-A and 93.9% (230\/245) for Model-B, with 1.16 and 0.879 FPs per case, respectively, representing a significant 24% FP reduction (p &lt; 0.001). Per-aneurysm sensitivity did not differ significantly (McNemar\u2019s test, p = 1.00). Model-B suppressed mimics in the internal carotid artery (ICA) and middle cerebral artery (MCA). Aneurysms were most frequent in the ICA (29%) and MCA (22%). Setting Model-A FPs as 100%, ICA and MCA accounted for 47.1% and 11.3% in Model-A and decreased to 34.5% and 7.7% in Model-B. In an exploratory regional analysis with Holm adjustment, FP reductions remained significant in the ICA (adjusted p &lt; 0.001) and MCA (adjusted p = 0.003). Conclusions: A secondary cluster-filtering algorithm reduced FP candidate findings by 24% with similar sensitivity point estimates (paired difference \u22120.4 percentage points, 95% CI \u22123.1 to +2.1). Region-specific error analysis revealed the anatomical dependency of FP reduction. The impact on reading workflow and patient outcomes requires prospective evaluation.<\/jats:p>","DOI":"10.3390\/jcm15187259","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T11:09:35Z","timestamp":1790075375000},"page":"7259","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Clinical Comparison of Two Versions of a Commercial Artificial Intelligence System for Identifying Candidate Locations of Cerebral Aneurysms: Reduction and Characterization of False-Positive Findings"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5234-1056","authenticated-orcid":false,"given":"Masashi","family":"Kuwabara","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8551, 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unruptured cerebral aneurysms in a Japanese cohort","volume":"366","author":"Morita","year":"2012","journal-title":"N. Engl. J. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1390","DOI":"10.1161\/01.STR.30.7.1390","article-title":"Prevalence of intracranial saccular aneurysms in a Japanese community based on a consecutive autopsy series during a 30-year observation period. The Hisayama study","volume":"30","author":"Iwamoto","year":"1999","journal-title":"Stroke"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"983","DOI":"10.3171\/2020.1.JNS192848","article-title":"A register-based SAH study in Japan: High incidence rate and recent decline trend based on lifestyle","volume":"134","author":"Ikawa","year":"2020","journal-title":"J. Neurosurg."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"391","DOI":"10.3171\/jns.1999.91.3.0391","article-title":"Family history of subarachnoid hemorrhage and the incidence of asymptomatic, unruptured cerebral aneurysms","volume":"91","author":"Nakagawa","year":"1999","journal-title":"J. Neurosurg."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/S1474-4422(13)70263-1","article-title":"Development of the PHASES score for prediction of risk of rupture of intracranial aneurysms: A pooled analysis of six prospective cohort studies","volume":"13","author":"Greving","year":"2014","journal-title":"Lancet Neurol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.wneu.2019.04.211","article-title":"Value of Brain Dock (Brain Screening) System in Japan","volume":"127","author":"Morita","year":"2019","journal-title":"World Neurosurg."},{"key":"ref_7","unstructured":"New Guidelines Development Committee for Brain Dock (2019). The Guideline for Brain Dock 2019, Kyobunsha."},{"key":"ref_8","first-page":"297","article-title":"The Guideline for Brain Dock 2003","volume":"64","author":"Saito","year":"2006","journal-title":"Nihon Rinsho"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"16202","DOI":"10.1038\/s41598-023-43418-x","article-title":"Effectiveness of tuning an artificial intelligence algorithm for cerebral aneurysm diagnosis: A study of 10,000 consecutive cases","volume":"13","author":"Kuwabara","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105487","DOI":"10.1016\/j.ijmedinf.2024.105487","article-title":"Deep learning-based platform performs high detection sensitivity of intracranial aneurysms in 3D brain TOF-MRA: An external clinical validation study","volume":"188","author":"Li","year":"2024","journal-title":"Int. J. Med. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"373","DOI":"10.3174\/ajnr.A6468","article-title":"Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives","volume":"41","author":"Shi","year":"2020","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1007\/s00062-019-00809-w","article-title":"Performance of a Deep-Learning Neural Network to Detect Intracranial Aneurysms from 3D TOF-MRA Compared to Human Readers","volume":"30","author":"Faron","year":"2020","journal-title":"Clin. Neuroradiol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1148\/radiol.2018180901","article-title":"Deep Learning for MR Angiography: Automated Detection of Cerebral Aneurysms","volume":"290","author":"Ueda","year":"2019","journal-title":"Radiology"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e21518","DOI":"10.1097\/MD.0000000000021518","article-title":"Incidental cerebral aneurysms detected by a computer-assisted detection system based on artificial intelligence: A case series","volume":"99","author":"Shimada","year":"2020","journal-title":"Medicine"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1002\/jmri.25842","article-title":"Deep neural network-based computer-assisted detection of cerebral aneurysms in MR angiography","volume":"47","author":"Nakao","year":"2018","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1148\/radiol.2020192154","article-title":"Deep Learning for Detecting Cerebral Aneurysms with CT Angiography","volume":"298","author":"Yang","year":"2021","journal-title":"Radiology"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1136\/jnis-2022-019456","article-title":"Detection of cerebral aneurysms using artificial intelligence: A systematic review and meta-analysis","volume":"15","author":"Din","year":"2023","journal-title":"J. Neurointerv. Surg."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: The convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G., and King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Med., 17.","DOI":"10.1186\/s12916-019-1426-2"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e271","DOI":"10.1016\/S2589-7500(19)30123-2","article-title":"A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis","volume":"1","author":"Liu","year":"2019","journal-title":"Lancet Digit. Health"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e261","DOI":"10.1016\/S2589-7500(23)00268-6","article-title":"A deep-learning model for intracranial aneurysm detection on CT angiography images in China: A stepwise, multicentre, early-stage clinical validation study","volume":"6","author":"Hu","year":"2024","journal-title":"Lancet Digit. Health"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bizjak, \u017d., and \u0160piclin, \u017d. (2023). A Systematic Review of Deep-Learning Methods for Intracranial Aneurysm Detection in CT Angiography. Biomedicines, 11.","DOI":"10.3390\/biomedicines11112921"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1007\/s11547-023-01620-x","article-title":"Utility of virtual stenting in treatment of cerebral aneurysms by flow diverter devices","volume":"128","author":"Briganti","year":"2023","journal-title":"Radiol. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"Br. Med. J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1590\/s1806-37562017000000424","article-title":"Understanding diagnostic tests. Part 2","volume":"43","author":"Patino","year":"2017","journal-title":"J. Bras. Pneumol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.bjae.2020.03.002","article-title":"Bayes\u2019 formula: A powerful but counterintuitive tool for medical decision-making","volume":"20","author":"Webb","year":"2020","journal-title":"BJA Educ."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chatzimichail, T., and Hatjimihail, A.T. (2024). A software tool for applying Bayes\u2019 theorem in medical diagnostics. BMC Med. Inform. Decis. Mak., 24, Erratum in BMC Med. Inform. Decis. Mak. 2024, 24, 43. https:\/\/doi.org\/10.1186\/s12911-025-02863-6.","DOI":"10.1186\/s12911-024-02721-x"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"E20","DOI":"10.3171\/2019.9.FOCUS19653","article-title":"Comprehensive review of imaging of intracranial aneurysms and angiographically negative subarachnoid hemorrhage","volume":"47","author":"Howard","year":"2019","journal-title":"Neurosurg. Focus"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e138","DOI":"10.1016\/S2589-7500(20)30003-0","article-title":"Changes in cancer detection and false-positive recall in mammography using artificial intelligence: A retrospective, multireader study","volume":"2","author":"Kim","year":"2020","journal-title":"Lancet Digit. Health"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111243","DOI":"10.1016\/j.jocn.2025.111243","article-title":"Advancing Intracranial Aneurysm Detection: A Comprehensive Systematic Review and Meta-analysis of Deep Learning Models Performance, Clinical Integration, and Future Directions","volume":"136","author":"Delfan","year":"2025","journal-title":"J. Clin. Neurosci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Jin, Y., Ye, H., Zhang, X., Liu, J., and Zhang, W. (2024). Classification, detection, and segmentation performance of image-based AI in intracranial aneurysm: A systematic review. BMC Med. Imaging, 24.","DOI":"10.1186\/s12880-024-01347-9"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"203","DOI":"10.2176\/jns-nmc.2024-0235","article-title":"Questionnaire Survey on the Current Use of Brain Docks and Their Compliance with Guidelines in Japan","volume":"65","author":"Kondo","year":"2025","journal-title":"Neurol. Med. Chir."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1969","DOI":"10.1161\/STROKEAHA.110.585059","article-title":"Small unruptured intracranial aneurysm verification study: SUAVe study, Japan","volume":"41","author":"Sonobe","year":"2010","journal-title":"Stroke"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.3349\/ymj.2021.62.11.1052","article-title":"A Deep Learning Model with High Standalone Performance for Diagnosis of Unruptured Intracranial Aneurysm","volume":"62","author":"Joo","year":"2021","journal-title":"Yonsei Med. J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"12018","DOI":"10.1038\/s41598-023-38586-9","article-title":"Automated detection of intracranial aneurysms using skeleton-based 3D patches, semantic segmentation, and auxiliary classification for overcoming data imbalance in brain TOF-MRA","volume":"13","author":"Ham","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"739","DOI":"10.2214\/AJR.16.16963","article-title":"Interpretive Error in Radiology","volume":"208","author":"Waite","year":"2017","journal-title":"Am. J. Roentgenol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3576","DOI":"10.1007\/s00330-020-06672-5","article-title":"Integrating artificial intelligence into the clinical practice of radiology: Challenges and recommendations","volume":"30","author":"Recht","year":"2020","journal-title":"Eur. Radiol."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/18\/7259\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T11:27:23Z","timestamp":1790076443000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/18\/7259"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,18]]},"references-count":38,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["jcm15187259"],"URL":"https:\/\/doi.org\/10.3390\/jcm15187259","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,18]]}}}