{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T12:51:58Z","timestamp":1775911918276,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T00:00:00Z","timestamp":1740009600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T00:00:00Z","timestamp":1740009600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100007465","name":"UiT The Arctic University of Norway","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100007465","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Intracranial atherosclerotic stenosis (ICAS) refers to a narrowing of intracranial arteries due to plaque buildup on the inside of the vessel walls restricting blood flow. Early detection of ICAS is crucial to prevent serious consequences such as stroke. Here we apply three different machine learning methods, such as support vector machines, multi-layer perceptrons and Kolmogorov-Arnold Networks to predict ICAS according to sparse risk factors from blood lipids and demographic data, including smoking habits, age, sex, diabetes, blood pressure lowering and cholesterol-lowering drugs and high-density lipoprotein. We achieved similar performance on classification compared to modern detection algorithms for ICAS in TOF-MRA (time-of-flight magnetic resonance angiography). The prevalence of ICAS in the population is relatively low, which is often case in medicine. While in the medical research community, the issue of low prevalence is established, machine learning-based research in medicine often does not take into account a critical viewpoint of the prevalence in clinical settings of their methods. We showed that with a balanced training\/test set an accuracy up to 81% was achievable, while with the inclusion of prevalence, the positive predictive value was at 19% to the prevalence data, changes the performance metrics. Therefore, we highlighted the discrepancy that can arise between the results reported by the models and their clinical relevance. Furthermore, the results demonstrate the predictive potential of limited risk factors, highlighting its potential contribution to a multi-modular classification algorithm based on MRAs.<\/jats:p>","DOI":"10.1186\/s12911-025-02896-x","type":"journal-article","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T16:41:46Z","timestamp":1740069706000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Intracranial stenosis prediction using a small set of risk factors in the Troms\u00f8 Study"],"prefix":"10.1186","volume":"25","author":[{"given":"Luca","family":"Bernecker","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liv-Hege","family":"Johnsen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Torgil Riise","family":"Vangberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,20]]},"reference":[{"issue":"11","key":"2896_CR1","doi-asserted-by":"publisher","first-page":"1106","DOI":"10.1016\/S1474-4422(13)70195-9","volume":"12","author":"CA Holmstedt","year":"2013","unstructured":"Holmstedt CA, Turan TN, Chimowitz MI. Atherosclerotic intracranial arterial stenosis: risk factors, diagnosis, and treatment. Lancet Neurol. 2013;12(11):1106\u201314.","journal-title":"Lancet Neurol"},{"issue":"5","key":"2896_CR2","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.1016\/0735-1097(91)90539-L","volume":"18","author":"AC Pearson","year":"1991","unstructured":"Pearson AC, Nagelhout D, Castello R, Gomez CR, Labovitz AJ. Atrial septal aneurysm and stroke: a transesophageal echocardiographic study. J Am Coll Cardiol. 1991;18(5):1223\u20139.","journal-title":"J Am Coll Cardiol"},{"key":"2896_CR3","doi-asserted-by":"crossref","unstructured":"Dearborn JL, Zhang Y, Qiao Y, Suri MFK, Liu L, Gottesman RF, Rawlings AM, Mosley TH, Alonso A, Knopman DS, Guallar E. Wasserman, B.A.: Intracranial atherosclerosis and dementia. Neurology 88(16) (2017).","DOI":"10.1212\/WNL.0000000000003837"},{"issue":"4","key":"2896_CR4","doi-asserted-by":"publisher","first-page":"1142","DOI":"10.1161\/STROKEAHA.107.496513","volume":"39","author":"M Mazighi","year":"2008","unstructured":"Mazighi M, et al. Autopsy prevalence of intracranial atherosclerosis in patients with fatal stroke. Stroke. 2008;39(4):1142\u20137.","journal-title":"Stroke"},{"key":"2896_CR5","doi-asserted-by":"crossref","unstructured":"Leung S, Yi et al. Pattern of cerebral atherosclerosis in Hong Kong Chinese. Severity in intracranial and extracranial vessels. Stroke 24.6 (1993): 779\u2013786.","DOI":"10.1161\/01.STR.24.6.779"},{"issue":"4","key":"2896_CR6","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1161\/CIRCULATIONAHA.105.578229","volume":"113","author":"SE Kasner","year":"2006","unstructured":"Kasner SE, Chimowitz MI, Lynn MJ, Howlett-Smith H, Stern BJ, Hertzberg VS, Frankel MR, Levine SR, Chaturvedi S, Benesch CG, Sila CA, Jovin TG, Romano JG, Cloft HJ. Warfarin Aspirin Symptomatic Intracranial Disease Trial investigators. Predictors of ischemic stroke in the territory of a symptomatic intracranial arterial stenosis. Circulation. 2006;113(4):555\u201363. https:\/\/doi.org\/10.1161\/CIRCULATIONAHA.105.578229. Epub 2006 Jan 23. PMID: 16432056.","journal-title":"Circulation"},{"key":"2896_CR7","doi-asserted-by":"crossref","unstructured":"Chen Z et al. Hemodynamic Impairment of Blood Pressure and Stroke Mechanisms in Symptomatic Intracranial Atherosclerotic Stenosis. Stroke (2024).","DOI":"10.1161\/STROKEAHA.123.046051"},{"issue":"12","key":"2896_CR8","doi-asserted-by":"publisher","first-page":"107399","DOI":"10.1016\/j.jstrokecerebrovasdis.2023.107399","volume":"32","author":"L-H Johnsen","year":"2023","unstructured":"Johnsen L-H, Herder M, Vangberg T, Isaksen JG, Mathiesen EB. Prevalence of intracranial artery stenosis in a general population using 3d-time of flight magnetic resonance angiography. J Stroke Cerebrovasc Dis. 2023;32(12):107399.","journal-title":"J Stroke Cerebrovasc Dis"},{"issue":"1","key":"2896_CR9","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.atherosclerosis.2011.12.020","volume":"221","author":"E L\u00b4opez-Cancio","year":"2012","unstructured":"L\u00b4opez-Cancio E, Dorado L, Mill\u00b4an M, Revert\u00b4e S, Sun\u02dcol A, Massuet A, Gal\u00b4an A, Alzamora MT, Pera G, Tor\u00b4an P, D\u00b4avalos A, Arenillas JF. The Barcelona-Asymptomatic Intracranial atherosclerosis (AsIA) study: prevalence and risk factors. Atherosclerosis. 2012;221(1):221\u20135. https:\/\/doi.org\/10.1016\/j.atherosclerosis.2011.12.020.","journal-title":"Atherosclerosis"},{"issue":"4","key":"2896_CR10","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1111\/ene.14144","volume":"27","author":"Q Sun","year":"2020","unstructured":"Sun Q, Wang Q, Wang X, Ji X, Sang S, Shao S, Zhao Y, Xiang Y, Xue Y, Li J, Wang G, Lv M, Xue F, Qiu C, Du Y. Prevalence and cardiovascular risk factors of asymptomatic intracranial arterial stenosis: the Kongcun Town Study in Shandong, China. Eur J Neurol. 2020;27(4):729\u201335. https:\/\/doi.org\/10.1111\/ene.14144.","journal-title":"Eur J Neurol"},{"key":"2896_CR11","doi-asserted-by":"publisher","unstructured":"Suri MFK, Johnston SC. Epidemiology of intracranial stenosis. J Neuroimaging. 2009;19(1). https:\/\/doi.org\/10.1111\/j.1552-6569.2009.00415.x.","DOI":"10.1111\/j.1552-6569.2009.00415.x"},{"issue":"4","key":"2896_CR12","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1016\/S1474-4422(21)00376-8","volume":"21","author":"J Gutierrez","year":"2022","unstructured":"Gutierrez J, Turan TN, Hoh BL, Chimowitz MI. Intracranial atherosclerotic stenosis: risk factors, diagnosis, and treatment. Lancet Neurol. 2022;21(4):355\u201368.","journal-title":"Lancet Neurol"},{"issue":"3","key":"2896_CR13","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1136\/jnis-2022-019456","volume":"15","author":"M Din","year":"2023","unstructured":"Din M, Agarwal S, Grzeda M, Wood DA, Modat M, Booth TC. Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis. J NeuroInterventional Surg. 2023;15(3):262\u201371. https:\/\/doi.org\/10.1136\/jnis-2022-019456. Chap. New devices and techniques.","journal-title":"J NeuroInterventional Surg"},{"key":"2896_CR14","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.mri.2022.09.006","volume":"94","author":"J Qiu","year":"2022","unstructured":"Qiu J, Tan G, Lin Y, Guan J, Dai Z, Wang F, Zhuang C, Wilman AH, Huang H, Cao Z, et al. Automated detection of intracranial artery stenosis and occlusion in magnetic resonance angiography: a preliminary study based on deep learning. Magn Reson Imaging. 2022;94:105\u201311.","journal-title":"Magn Reson Imaging"},{"key":"2896_CR15","doi-asserted-by":"publisher","first-page":"43325","DOI":"10.1109\/ACCESS.2020.2977669","volume":"8","author":"H Chung","year":"2020","unstructured":"Chung H, Kang KM, Al-Masni MA, Sohn C-H, Nam Y, Ryu K, Kim D-H. Stenosis detection from time-of-flight magnetic resonance angiography via deep learning 3d squeeze and excitation residual networks. IEEE Access. 2020;8:43325\u201335.","journal-title":"IEEE Access"},{"issue":"9","key":"2896_CR16","doi-asserted-by":"publisher","first-page":"3554","DOI":"10.1109\/JBHI.2021.3062002","volume":"25","author":"AGC Pacheco","year":"2021","unstructured":"Pacheco AGC, Krohling RA. An attention-based mechanism to combine images and Metadata in Deep Learning models Applied to skin Cancer classification. IEEE J Biomedical Health Inf. 2021;25(9):3554\u201363. https:\/\/doi.org\/10.1109\/JBHI.2021.3062002.","journal-title":"IEEE J Biomedical Health Inf"},{"key":"2896_CR17","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1007\/978-3-030-80432-940","volume-title":"Medical image understanding and analysis","author":"D Grant","year":"2021","unstructured":"Grant D, Papiez\u02d9 BW, Parsons G, Tarassenko L, Mahdi A. Deep learning classification of Cardiomegaly using combined imaging and non-imaging ICU data. In: Papiez\u02d9 BW, Yaqub M, Jiao J, Namburete AIL, Noble JA, editors. Medical image understanding and analysis.???: Springer; 2021. pp. 547\u201358. https:\/\/doi.org\/10.1007\/978-3-030-80432-940."},{"key":"2896_CR18","unstructured":"Liu Z, Wang Y, Vaidya S, Ruehle F, Halverson J, Solja\u02c7ci\u00b4c M, Hou TY, Tegmark M. Kan: Kolmogorov-arnold networks. arXiv preprint arXiv:2404.19756 (2024)."},{"issue":"3","key":"2896_CR19","first-page":"23","volume":"1","author":"D Meyer","year":"2001","unstructured":"Meyer D, Wien F. Support vector machines. R News. 2001;1(3):23\u20136.","journal-title":"R News"},{"key":"2896_CR20","unstructured":"Riedmiller M, Lernen A. Multi layer perceptron. Machine Learning Lab Special lecture. Univ Freiburg 24 (2014)."},{"issue":"9309","key":"2896_CR21","doi-asserted-by":"publisher","first-page":"881","DOI":"10.1016\/S0140-6736(02)07948-5","volume":"359","author":"DA Grimes","year":"2002","unstructured":"Grimes DA, Schulz KF. Uses and abuses of screening tests. Lancet. 2002;359(9309):881\u20134.","journal-title":"Lancet"},{"issue":"13","key":"2896_CR22","doi-asserted-by":"publisher","first-page":"1783","DOI":"10.1002\/1097-0258(20000715)19:13<1783::AID-SIM497>3.0.CO;2-B","volume":"19","author":"I Guggenmoos-Holzmann","year":"2000","unstructured":"Guggenmoos-Holzmann I, Houwelingen HC. The (in) validity of sensitivity and specificity. Stat Med. 2000;19(13):1783\u201392.","journal-title":"Stat Med"},{"issue":"7","key":"2896_CR23","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1177\/14034948221092294","volume":"50","author":"LA Hopstock","year":"2022","unstructured":"Hopstock LA, Grimsgaard S, Johansen H, Kanstad K, Wilsgaard T, Eggen AE. The seventh survey of the troms\u00f8 study (troms\u00f87) 2015\u20132016: study design, data collection, attendance, and prevalence of risk factors and disease in a multipurpose population-based health survey. Scand J Public Health. 2022;50(7):919\u201329.","journal-title":"Scand J Public Health"},{"issue":"4","key":"2896_CR24","first-page":"643","volume":"21","author":"OB Samuels","year":"2000","unstructured":"Samuels OB, Joseph GJ, Lynn MJ, Smith HA, Chimowitz MI. A standardized method for measuring intracranial arterial stenosis. Am J Neuroradiol. 2000;21(4):643\u20136. Accessed 2024-03-04.","journal-title":"Am J Neuroradiol"},{"key":"2896_CR25","doi-asserted-by":"crossref","unstructured":"Berrar D et al. Cross-validation. (2019). http:\/\/berrar.com\/resources\/Berrar EBCB 2nd edition Cross-validation preprint.pdf.","DOI":"10.1016\/B978-0-12-809633-8.20349-X"},{"issue":"8","key":"2896_CR26","doi-asserted-by":"publisher","first-page":"1636","DOI":"10.1161\/STROKEAHA.110.584672","volume":"41","author":"TN Turan","year":"2010","unstructured":"Turan TN, Makki AA, Tsappidi S, Cotsonis G, Lynn MJ, Cloft HJ, Chimowitz MI. Risk factors associated with severity and location of intracranial arterial stenosis. Stroke. 2010;41(8):1636\u201340.","journal-title":"Stroke"},{"issue":"1","key":"2896_CR27","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1093\/eurjpc\/zwaa052","volume":"29","author":"Z Chen","year":"2022","unstructured":"Chen Z, et al. Effects of individual and integrated cumulative burden of blood pressure, glucose, low-density lipoprotein cholesterol, and C-reactive protein on cardiovascular risk. Eur J Prev Cardiol. 2022;29(1):127\u201335.","journal-title":"Eur J Prev Cardiol"},{"key":"2896_CR28","unstructured":"Shreffler J, Huecker MR. Diagnostic testing accuracy: Sensitivity, specificity, predictive values and likelihood ratios. Europe PMC (2020)."},{"issue":"10","key":"2896_CR29","doi-asserted-by":"publisher","first-page":"2967","DOI":"10.1161\/STROKEAHA.119.025964","volume":"50","author":"S Shitara","year":"2019","unstructured":"Shitara S, Fujiyoshi A, Hisamatsu T, Torii S, Suzuki S, Ito T, Arima H, Shiino A, Nozaki K, Miura K, et al. Intracranial artery stenosis and its association with conventional risk factors in a general population of Japanese men. Stroke. 2019;50(10):2967\u20139.","journal-title":"Stroke"},{"issue":"3","key":"2896_CR30","first-page":"439","volume":"28","author":"C Choi","year":"2007","unstructured":"Choi C, Lee D, Lee J, Pyun H, Kang D, Kwon S, Kim J, Kim S, Suh D. Detection of intracranial atherosclerotic steno-occlusive disease with 3d time-offlight magnetic resonance angiography with sensitivity encoding at 3t. Am J Neuroradiol. 2007;28(3):439\u201346.","journal-title":"Am J Neuroradiol"},{"issue":"11","key":"2896_CR31","doi-asserted-by":"publisher","first-page":"108012","DOI":"10.1016\/j.jstrokecerebrovasdis.2024.108012","volume":"33","author":"J Mo","year":"2024","unstructured":"Mo J, et al. Lipoprotein-associated phospholipase A2 activity levels is associated with artery to artery embolism in symptomatic intracranial atherosclerotic disease. J Stroke Cerebrovasc Dis. 2024;33(11):108012.","journal-title":"J Stroke Cerebrovasc Dis"},{"issue":"3","key":"2896_CR32","doi-asserted-by":"publisher","first-page":"292","DOI":"10.2174\/0115672026323216240722194958","volume":"21","author":"J Mo","year":"2024","unstructured":"Mo J, et al. Association between Interleukin-6 and multiple Acute infarctions in symptomatic intracranial atherosclerotic disease. Curr Neurovasc Res. 2024;21(3):292\u20139.","journal-title":"Curr Neurovasc Res"},{"issue":"9","key":"2896_CR33","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1111\/j.1553-2712.1996.tb03538.x","volume":"3","author":"NMF Buderer","year":"1996","unstructured":"Buderer NMF. Statistical methodology: I. incorporating the prevalence of disease into the sample size calculation for sensitivity and specificity. Acad Emerg Med. 1996;3(9):895\u2013900.","journal-title":"Acad Emerg Med"},{"key":"2896_CR34","doi-asserted-by":"crossref","unstructured":"Jang J, Hwang D. M3t: three-dimensional medical image classifier using multiplane and multi-slice transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20718\u201320729 (2022).","DOI":"10.1109\/CVPR52688.2022.02006"},{"issue":"6","key":"2896_CR35","doi-asserted-by":"publisher","first-page":"39","DOI":"10.3390\/jimaging6060039","volume":"6","author":"AS Assiri","year":"2020","unstructured":"Assiri AS, Nazir S, Velastin SA. Breast tumor classification using an ensemble machine learning method. J Imaging. 2020;6(6):39.","journal-title":"J Imaging"},{"issue":"4","key":"2896_CR36","doi-asserted-by":"publisher","first-page":"3161","DOI":"10.1007\/s40747-021-00563-y","volume":"8","author":"J Amin","year":"2022","unstructured":"Amin J, Sharif M, Haldorai A, Yasmin M, Nayak RS. Brain tumor detection and classification using machine learning: a comprehensive survey. Complex Intell Syst. 2022;8(4):3161\u201383.","journal-title":"Complex Intell Syst"},{"key":"2896_CR37","doi-asserted-by":"crossref","unstructured":"Kigka VI, Sakellarios AI, Mantzaris MD, Tsakanikas VD, Potsika VT, Palombo D, Montecucco F, Fotiadis DI. A machine learning model for the identification of high risk carotid atherosclerotic plaques. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 2266\u20132269 (2021). IEEE.","DOI":"10.1109\/EMBC46164.2021.9630654"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-02896-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-025-02896-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-02896-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T16:41:55Z","timestamp":1740069715000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-025-02896-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,20]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2896"],"URL":"https:\/\/doi.org\/10.1186\/s12911-025-02896-x","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,20]]},"assertion":[{"value":"11 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was approved by the Regional Committee for Medical and Health Research Ethics, North Norway (REK NORD 619939). All participants provided informed consent before their inclusion in the study. The study adheres to the tenets of the Declaration of Helsinki.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical trial number"}}],"article-number":"95"}}