{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T22:49:39Z","timestamp":1761778179932,"version":"build-2065373602"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T00:00:00Z","timestamp":1736121600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T00:00:00Z","timestamp":1736121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","award":["MD-Phd\/Medical Scientist Training Program","HI22C0471"],"award-info":[{"award-number":["MD-Phd\/Medical Scientist Training Program","HI22C0471"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003669","name":"Korea Centers for Disease Control and Prevention","doi-asserted-by":"publisher","award":["2011-E71004-00","2012-E71005-00","2013-E71005-00","2014-E71003-00"],"award-info":[{"award-number":["2011-E71004-00","2012-E71005-00","2013-E71005-00","2014-E71003-00"]}],"id":[{"id":"10.13039\/501100003669","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-024-01372-8","type":"journal-article","created":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T12:39:05Z","timestamp":1736167145000},"page":"2761-2778","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning Analysis of White Matter Hyperintensity and its Association with Comprehensive Vascular Factors in Two Large General Populations"],"prefix":"10.1007","volume":"38","author":[{"given":"Grace Yoojin","family":"Lee","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun Ho","family":"Choi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongwon","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miso","family":"Jang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong-Kyu","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyo-Jung","family":"Nam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sungwon","family":"Park","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mi Jung","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoon Ho","family":"Hwang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seung Ku","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chol","family":"Shin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3438-2217","authenticated-orcid":false,"given":"Namkug","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,6]]},"reference":[{"key":"1372_CR1","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1016\/S1474-4422(23)00131-X","volume":"22","author":"M Duering","year":"2023","unstructured":"Duering M, et al.: Neuroimaging standards for research into small vessel disease-advances since 2013. Lancet Neurol 22:602-618, 2023","journal-title":"Lancet Neurol"},{"key":"1372_CR2","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.trci.2019.02.001","volume":"5","author":"J Alber","year":"2019","unstructured":"Alber J, et al.: White matter hyperintensities in vascular contributions to cognitive impairment and dementia (VCID): Knowledge gaps and opportunities. Alzheimers Dement (N Y) 5:107-117, 2019","journal-title":"Alzheimers Dement (N Y)"},{"key":"1372_CR3","doi-asserted-by":"crossref","first-page":"001140","DOI":"10.1161\/JAHA.114.001140","volume":"4","author":"JM Wardlaw","year":"2015","unstructured":"Wardlaw JM, Valdes Hernandez MC, Munoz-Maniega S: What are white matter hyperintensities made of? Relevance to vascular cognitive impairment. J Am Heart Assoc 4:001140, 2015","journal-title":"J Am Heart Assoc"},{"key":"1372_CR4","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1038\/nrneurol.2015.10","volume":"11","author":"ND Prins","year":"2015","unstructured":"Prins ND, Scheltens P: White matter hyperintensities, cognitive impairment and dementia: an update. Nat Rev Neurol 11:157-165, 2015","journal-title":"Nat Rev Neurol"},{"key":"1372_CR5","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.neubiorev.2020.11.007","volume":"120","author":"HY Hu","year":"2021","unstructured":"Hu HY, et al.: White matter hyperintensities and risks of cognitive impairment and dementia: A systematic review and meta-analysis of 36 prospective studies. Neurosci Biobehav Rev 120:16-27, 2021","journal-title":"Neurosci Biobehav Rev"},{"key":"1372_CR6","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1002\/alz.12221","volume":"17","author":"OKL Hamilton","year":"2021","unstructured":"Hamilton OKL, et al.: Cognitive impairment in sporadic cerebral small vessel disease: A systematic review and meta-analysis. Alzheimers Dement 17:665-685, 2021","journal-title":"Alzheimers Dement"},{"key":"1372_CR7","doi-asserted-by":"crossref","first-page":"c3666","DOI":"10.1136\/bmj.c3666","volume":"341","author":"S Debette","year":"2010","unstructured":"Debette S, Markus HS: The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ 341:c3666, 2010","journal-title":"BMJ"},{"key":"1372_CR8","doi-asserted-by":"crossref","first-page":"836","DOI":"10.1161\/01.STR.0000202585.26325.74","volume":"37","author":"EC van Straaten","year":"2006","unstructured":"van Straaten EC, et al.: Impact of white matter hyperintensities scoring method on correlations with clinical data: the LADIS study. Stroke 37:836-840, 2006","journal-title":"Stroke"},{"key":"1372_CR9","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/S2215-0366(20)30431-4","volume":"8","author":"U Clancy","year":"2021","unstructured":"Clancy U, Gilmartin D, Jochems ACC, Knox L, Doubal FN, Wardlaw JM: Neuropsychiatric symptoms associated with cerebral small vessel disease: a systematic review and meta-analysis. Lancet Psychiatry 8:225-236, 2021","journal-title":"Lancet Psychiatry"},{"key":"1372_CR10","doi-asserted-by":"crossref","first-page":"e2172","DOI":"10.1212\/WNL.0000000000011827","volume":"96","author":"R Ghaznawi","year":"2021","unstructured":"Ghaznawi R, Geerlings MI, Jaarsma-Coes M, Hendrikse J, de Bresser J, Group UC-SS: Association of White Matter Hyperintensity Markers on MRI and Long-term Risk of Mortality and Ischemic Stroke: The SMART-MR Study. Neurology 96:e2172-e2183, 2021","journal-title":"Neurology"},{"key":"1372_CR11","doi-asserted-by":"crossref","first-page":"e010460","DOI":"10.1161\/CIRCIMAGING.120.010460","volume":"13","author":"F Moroni","year":"2020","unstructured":"Moroni F, Ammirati E, Hainsworth AH, Camici PG: Association of White Matter Hyperintensities and Cardiovascular Disease: The Importance of Microcirculatory Disease. Circ Cardiovasc Imaging 13:e010460, 2020","journal-title":"Circ Cardiovasc Imaging"},{"key":"1372_CR12","doi-asserted-by":"crossref","first-page":"e030676","DOI":"10.1161\/JAHA.123.030676","volume":"12","author":"F Koohi","year":"2023","unstructured":"Koohi F, Harshfield EL, Markus HS: Contribution of Conventional Cardiovascular Risk Factors to Brain White Matter Hyperintensities. J Am Heart Assoc 12:e030676, 2023","journal-title":"J Am Heart Assoc"},{"key":"1372_CR13","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/S1474-4422(13)70060-7","volume":"12","author":"JM Wardlaw","year":"2013","unstructured":"Wardlaw JM, Smith C, Dichgans M: Mechanisms of sporadic cerebral small vessel disease: insights from neuroimaging. Lancet Neurol 12:483-497, 2013","journal-title":"Lancet Neurol"},{"key":"1372_CR14","doi-asserted-by":"crossref","first-page":"3037","DOI":"10.1161\/STROKEAHA.119.025822","volume":"50","author":"EL Scharf","year":"2019","unstructured":"Scharf EL, et al.: Cardiometabolic Health and Longitudinal Progression of White Matter Hyperintensity: The Mayo Clinic Study of Aging. Stroke 50:3037-3044, 2019","journal-title":"Stroke"},{"key":"1372_CR15","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1212\/WNL.0b013e31826b951e","volume":"79","author":"M Simoni","year":"2012","unstructured":"Simoni M, et al.: Age-and sex-specific rates of leukoaraiosis in TIA and stroke patients: population-based study. Neurology 79:1215-1222, 2012","journal-title":"Neurology"},{"key":"1372_CR16","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.neuroimage.2018.10.042","volume":"185","author":"V Sundaresan","year":"2019","unstructured":"Sundaresan V, et al.: Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference. Neuroimage 185:434-445, 2019","journal-title":"Neuroimage"},{"key":"1372_CR17","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.neuroimage.2017.03.024","volume":"170","author":"L Griffanti","year":"2018","unstructured":"Griffanti L, et al.: Classification and characterization of periventricular and deep white matter hyperintensities on MRI: A study in older adults. Neuroimage 170:174-181, 2018","journal-title":"Neuroimage"},{"key":"1372_CR18","doi-asserted-by":"crossref","first-page":"1653","DOI":"10.1016\/j.neurobiolaging.2015.01.005","volume":"36","author":"PA Nyquist","year":"2015","unstructured":"Nyquist PA, et al.: Age differences in periventricular and deep white matter lesions. Neurobiol Aging 36:1653-1658, 2015","journal-title":"Neurobiol Aging"},{"key":"1372_CR19","doi-asserted-by":"crossref","first-page":"e024606","DOI":"10.1161\/JAHA.121.024606","volume":"11","author":"Y Hannawi","year":"2022","unstructured":"Hannawi Y, et al.: Association of Vascular Properties With the Brain White Matter Hyperintensity in Middle-Aged Population. J Am Heart Assoc 11:e024606, 2022","journal-title":"J Am Heart Assoc"},{"key":"1372_CR20","doi-asserted-by":"crossref","first-page":"1955","DOI":"10.1038\/s41598-022-06019-8","volume":"12","author":"J Jimenez-Balado","year":"2022","unstructured":"Jimenez-Balado J, Corlier F, Habeck C, Stern Y, Eich T: Effects of white matter hyperintensities distribution and clustering on late-life cognitive impairment. Sci Rep 12:1955, 2022","journal-title":"Sci Rep"},{"key":"1372_CR21","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1111\/ene.15750","volume":"30","author":"Y Fu","year":"2023","unstructured":"Fu Y, et al.: Associations of Life\u2019s Simple 7 with cerebral white matter hyperintensities and microstructural integrity: UK Biobank cohort study. Eur J Neurol 30:1200-1208, 2023","journal-title":"Eur J Neurol"},{"key":"1372_CR22","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1001\/jama.2019.10551","volume":"322","author":"Group SMIftSR","year":"2019","unstructured":"Group SMIftSR, et al.: Association of Intensive vs Standard Blood Pressure Control With Cerebral White Matter Lesions. JAMA 322:524-534, 2019","journal-title":"JAMA"},{"key":"1372_CR23","doi-asserted-by":"crossref","first-page":"e1168","DOI":"10.1212\/WNL.0000000000007093","volume":"92","author":"A de Havenon","year":"2019","unstructured":"de Havenon A, Majersik JJ, Tirschwell DL, McNally JS, Stoddard G, Rost NS: Blood pressure, glycemic control, and white matter hyperintensity progression in type 2 diabetics. Neurology 92:e1168-e1175, 2019","journal-title":"Neurology"},{"key":"1372_CR24","first-page":"e147","volume":"54","author":"OH Del Brutto","year":"2023","unstructured":"Del Brutto OH, Rumbea DA, Mera RM: Carotid-Intima Media Thickness and White Matter Hyperintensities Severity Among Older Adults of Amerindian Ancestry. Stroke 54:e147-e148, 2023","journal-title":"Stroke"},{"key":"1372_CR25","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1161\/STROKEAHA.117.018943","volume":"49","author":"D Della-Morte","year":"2018","unstructured":"Della-Morte D, et al.: Carotid Intima-Media Thickness Is Associated With White Matter Hyperintensities: The Northern Manhattan Study. Stroke 49:304-311, 2018","journal-title":"Stroke"},{"key":"1372_CR26","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.atherosclerosis.2019.04.230","volume":"287","author":"E Ammirati","year":"2019","unstructured":"Ammirati E, et al.: Progression of brain white matter hyperintensities in asymptomatic patients with carotid atherosclerotic plaques and no indication for revascularization. Atherosclerosis 287:171-178, 2019","journal-title":"Atherosclerosis"},{"key":"1372_CR27","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1161\/01.STR.32.6.1318","volume":"32","author":"L-O Wahlund","year":"2001","unstructured":"Wahlund L-O, et al.: A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke 32:1318-1322, 2001","journal-title":"Stroke"},{"key":"1372_CR28","first-page":"421","volume":"8","author":"F Fazekas","year":"1987","unstructured":"Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA: MR signal abnormalities at 1.5 T in Alzheimer\u2019s dementia and normal aging. American Journal of Neuroradiology 8:421-426, 1987","journal-title":"American Journal of Neuroradiology"},{"key":"1372_CR29","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/0022-510X(93)90041-V","volume":"114","author":"P Scheltens","year":"1993","unstructured":"Scheltens P, et al.: A semiquantative rating scale for the assessment of signal hyperintensities on magnetic resonance imaging. Journal of the neurological sciences 114:7-12, 1993","journal-title":"Journal of the neurological sciences"},{"key":"1372_CR30","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T: U-net: Convolutional networks for biomedical image segmentation. Proc. Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5\u20139, 2015, proceedings, part III 18: City","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1372_CR31","doi-asserted-by":"crossref","first-page":"105065","DOI":"10.1016\/j.cmpb.2019.105065","volume":"183","author":"J Hong","year":"2020","unstructured":"Hong J, Park BY, Lee MJ, Chung CS, Cha J, Park H: Two-step deep neural network for segmentation of deep white matter hyperintensities in migraineurs. Comput Methods Programs Biomed 183:105065, 2020","journal-title":"Comput Methods Programs Biomed"},{"key":"1372_CR32","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1002\/hbm.25784","volume":"43","author":"P Mojiri Forooshani","year":"2022","unstructured":"Mojiri Forooshani P, et al.: Deep Bayesian networks for uncertainty estimation and adversarial resistance of white matter hyperintensity segmentation. Hum Brain Mapp 43:2089-2108, 2022","journal-title":"Hum Brain Mapp"},{"key":"1372_CR33","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1002\/hbm.25695","volume":"43","author":"X Li","year":"2022","unstructured":"Li X, et al.: White matter hyperintensities segmentation using an ensemble of neural networks. Hum Brain Mapp 43:929-939, 2022","journal-title":"Hum Brain Mapp"},{"key":"1372_CR34","doi-asserted-by":"crossref","first-page":"102184","DOI":"10.1016\/j.media.2021.102184","volume":"73","author":"V Sundaresan","year":"2021","unstructured":"Sundaresan V, Zamboni G, Rothwell PM, Jenkinson M, Griffanti L: Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images. Med Image Anal 73:102184, 2021","journal-title":"Med Image Anal"},{"key":"1372_CR35","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1016\/j.neuroimage.2018.07.005","volume":"183","author":"H Li","year":"2018","unstructured":"Li H, et al.: Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images. Neuroimage 183:650-665, 2018","journal-title":"Neuroimage"},{"key":"1372_CR36","doi-asserted-by":"crossref","first-page":"118140","DOI":"10.1016\/j.neuroimage.2021.118140","volume":"237","author":"G Park","year":"2021","unstructured":"Park G, Hong J, Duffy BA, Lee JM, Kim H: White matter hyperintensities segmentation using the ensemble U-Net with multi-scale highlighting foregrounds. Neuroimage 237:118140, 2021","journal-title":"Neuroimage"},{"key":"1372_CR37","doi-asserted-by":"crossref","first-page":"101873","DOI":"10.1016\/j.compmedimag.2021.101873","volume":"89","author":"L Liang","year":"2021","unstructured":"Liang L, et al.: An anatomical knowledge-based MRI deep learning pipeline for white matter hyperintensity quantification associated with cognitive impairment. Comput Med Imaging Graph 89:101873, 2021","journal-title":"Comput Med Imaging Graph"},{"key":"1372_CR38","doi-asserted-by":"crossref","first-page":"2556","DOI":"10.1109\/TMI.2019.2905770","volume":"38","author":"HJ Kuijf","year":"2019","unstructured":"Kuijf HJ, et al.: Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge. IEEE Trans Med Imaging 38:2556-2568, 2019","journal-title":"IEEE Trans Med Imaging"},{"key":"1372_CR39","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18:203-211, 2021","journal-title":"Nat Methods"},{"key":"1372_CR40","doi-asserted-by":"crossref","unstructured":"Sundaresan V, Dinsdale NK, Jenkinson M, Griffanti L: Omni-Supervised Domain Adversarial Training for White Matter Hyperintensity Segmentation in the UK Biobank. 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI):1\u20134, 2022","DOI":"10.1109\/ISBI52829.2022.9761539"},{"key":"1372_CR41","doi-asserted-by":"crossref","first-page":"102215","DOI":"10.1016\/j.media.2021.102215","volume":"74","author":"V Sundaresan","year":"2021","unstructured":"Sundaresan V, Zamboni G, Dinsdale NK, Rothwell PM, Griffanti L, Jenkinson M: Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images. Med Image Anal 74:102215, 2021","journal-title":"Med Image Anal"},{"key":"1372_CR42","doi-asserted-by":"crossref","first-page":"709","DOI":"10.5665\/sleep.2632","volume":"36","author":"H Kim","year":"2013","unstructured":"Kim H, et al.: Obstructive sleep apnea as a risk factor for cerebral white matter change in a middle-aged and older general population. Sleep 36:709-715B, 2013","journal-title":"Sleep"},{"key":"1372_CR43","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1038\/ng.357","volume":"41","author":"YS Cho","year":"2009","unstructured":"Cho YS, et al.: A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet 41:527-534, 2009","journal-title":"Nat Genet"},{"key":"1372_CR44","doi-asserted-by":"crossref","unstructured":"Grabner G, Janke AL, Budge MM, Smith D, Pruessner J, Collins DL: Symmetric atlasing and model based segmentation: an application to the hippocampus in older adults. Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2006: 9th International Conference, Copenhagen, Denmark, October 1\u20136, 2006 Proceedings, Part II 9:58\u201366, 2006","DOI":"10.1007\/11866763_8"},{"key":"1372_CR45","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1016\/0735-1097(90)90282-T","volume":"15","author":"AS Agatston","year":"1990","unstructured":"Agatston AS, Janowitz WR, Hildner FJ, Zusmer NR, Viamonte M, Jr., Detrano R: Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol 15:827-832, 1990","journal-title":"J Am Coll Cardiol"},{"key":"1372_CR46","unstructured":"Cury RC, et al.: CAD-RADS: Coronary Artery Disease - Reporting and Data System: An Expert Consensus Document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Radiology (ACR) and the North American Society for Cardiovascular Imaging (NASCI). Endorsed by the American College of Cardiology. J Am Coll Radiol 13:1458\u20131466 e1459, 2016"},{"key":"1372_CR47","doi-asserted-by":"crossref","first-page":"e025641","DOI":"10.1161\/JAHA.122.025641","volume":"11","author":"SH Kim","year":"2022","unstructured":"Kim SH, et al.: Prevalence of Isolated Nocturnal Hypertension and Development of Arterial Stiffness, Left Ventricular Hypertrophy, and Silent Cerebrovascular Lesions: The KoGES (Korean Genome and Epidemiology Study). J Am Heart Assoc 11:e025641, 2022","journal-title":"J Am Heart Assoc"},{"key":"1372_CR48","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.jclinepi.2017.12.006","volume":"98","author":"AF Schmidt","year":"2018","unstructured":"Schmidt AF, Finan C: Linear regression and the normality assumption. J Clin Epidemiol 98:146-151, 2018","journal-title":"J Clin Epidemiol"},{"key":"1372_CR49","doi-asserted-by":"crossref","first-page":"2594","DOI":"10.1161\/STROKEAHA.120.032674","volume":"52","author":"MC Johansen","year":"2021","unstructured":"Johansen MC, et al.: Association of Coronary Artery Atherosclerosis With Brain White Matter Hyperintensity. Stroke 52:2594-2600, 2021","journal-title":"Stroke"},{"key":"1372_CR50","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.neuroimage.2016.07.018","volume":"141","author":"L Griffanti","year":"2016","unstructured":"Griffanti L, et al.: BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities. Neuroimage 141:191-205, 2016","journal-title":"Neuroimage"},{"key":"1372_CR51","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.neuroimage.2017.10.034","volume":"166","author":"F Alfaro-Almagro","year":"2018","unstructured":"Alfaro-Almagro F, et al.: Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166:400-424, 2018","journal-title":"Neuroimage"},{"key":"1372_CR52","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","volume":"36","author":"K Kamnitsas","year":"2017","unstructured":"Kamnitsas K, et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal 36:61-78, 2017","journal-title":"Med Image Anal"},{"key":"1372_CR53","doi-asserted-by":"crossref","first-page":"101867","DOI":"10.1016\/j.compmedimag.2021.101867","volume":"88","author":"R Balakrishnan","year":"2021","unstructured":"Balakrishnan R, Valdes Hernandez MDC, Farrall AJ: Automatic segmentation of white matter hyperintensities from brain magnetic resonance images in the era of deep learning and big data - A systematic review. Comput Med Imaging Graph 88:101867, 2021","journal-title":"Comput Med Imaging Graph"},{"key":"1372_CR54","doi-asserted-by":"crossref","first-page":"101791","DOI":"10.1016\/j.media.2020.101791","volume":"65","author":"L Liu","year":"2020","unstructured":"Liu L, Kurgan L, Wu FX, Wang J: Attention convolutional neural network for accurate segmentation and quantification of lesions in ischemic stroke disease. Med Image Anal 65:101791, 2020","journal-title":"Med Image Anal"},{"key":"1372_CR55","doi-asserted-by":"crossref","first-page":"120494","DOI":"10.1016\/j.neuroimage.2023.120494","volume":"285","author":"JF Strain","year":"2023","unstructured":"Strain JF, et al.: Accuracy of TrUE-Net in comparison to established white matter hyperintensity segmentation methods: An independent validation study. Neuroimage 285:120494, 2023","journal-title":"Neuroimage"},{"key":"1372_CR56","doi-asserted-by":"crossref","first-page":"e0285683","DOI":"10.1371\/journal.pone.0285683","volume":"18","author":"MS Rovang","year":"2023","unstructured":"Rovang MS, et al.: Segmenting white matter hyperintensities on isotropic three-dimensional Fluid Attenuated Inversion Recovery magnetic resonance images: Assessing deep learning tools on a Norwegian imaging database. PLoS One 18:e0285683, 2023","journal-title":"PLoS One"},{"key":"1372_CR57","doi-asserted-by":"crossref","first-page":"e020830","DOI":"10.1136\/bmjopen-2017-020830","volume":"8","author":"H Ye","year":"2018","unstructured":"Ye H, Wang Y, Qiu J, Wu Q, Xu M, Wang J: White matter hyperintensities and their subtypes in patients with carotid artery stenosis: a systematic review and meta-analysis. BMJ Open 8:e020830, 2018","journal-title":"BMJ Open"},{"key":"1372_CR58","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1291\/hypres.31.75","volume":"31","author":"T Ohmine","year":"2008","unstructured":"Ohmine T, et al.: Association between arterial stiffness and cerebral white matter lesions in community-dwelling elderly subjects. Hypertension Research 31:75-81, 2008","journal-title":"Hypertension Research"},{"key":"1372_CR59","doi-asserted-by":"crossref","first-page":"1490","DOI":"10.1161\/HYPERTENSIONAHA.121.18135","volume":"78","author":"J Gronewold","year":"2021","unstructured":"Gronewold J, et al.: Association of Blood Pressure, Its Treatment, and Treatment Efficacy With Volume of White Matter Hyperintensities in the Population-Based 1000BRAINS Study. Hypertension 78:1490-1501, 2021","journal-title":"Hypertension"},{"key":"1372_CR60","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1177\/17474930211043364","volume":"17","author":"I Wilkinson","year":"2022","unstructured":"Wilkinson I, Webb AJS: Consistency of associations of systolic and diastolic blood pressure with white matter hyperintensities: A meta-analysis. Int J Stroke 17:291-298, 2022","journal-title":"Int J Stroke"},{"key":"1372_CR61","doi-asserted-by":"crossref","first-page":"111208","DOI":"10.1016\/j.ejrad.2023.111208","volume":"170","author":"R Shen","year":"2024","unstructured":"Shen R, et al.: Atherosclerotic plaque characteristics in extracranial carotid artery may indicate closer association with white matter hyperintensities than intracranial arteries: A CARE-II study. Eur J Radiol 170:111208, 2024","journal-title":"Eur J Radiol"},{"key":"1372_CR62","first-page":"e1811","volume":"94","author":"FF Zhai","year":"2020","unstructured":"Zhai FF, et al.: Carotid atherosclerosis, dilation, and stiffness relate to cerebral small vessel disease. Neurology 94:e1811-e1819, 2020","journal-title":"Neurology"},{"key":"1372_CR63","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1016\/j.jcmg.2013.11.014","volume":"7","author":"TZ Naqvi","year":"2014","unstructured":"Naqvi TZ, Lee MS: Carotid intima-media thickness and plaque in cardiovascular risk assessment. JACC Cardiovasc Imaging 7:1025-1038, 2014","journal-title":"JACC Cardiovasc Imaging"},{"key":"1372_CR64","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1161\/01.ATV.0000194077.23234.ae","volume":"26","author":"T Edvardsen","year":"2006","unstructured":"Edvardsen T, et al.: Coronary artery atherosclerosis is related to reduced regional left ventricular function in individuals without history of clinical cardiovascular disease: the Multiethnic Study of Atherosclerosis. Arterioscler Thromb Vasc Biol 26:206-211, 2006","journal-title":"Arterioscler Thromb Vasc Biol"},{"key":"1372_CR65","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1186\/s40478-023-01590-1","volume":"11","author":"V Rajeev","year":"2023","unstructured":"Rajeev V, et al.: Chronic cerebral hypoperfusion: a critical feature in unravelling the etiology of vascular cognitive impairment. Acta Neuropathol Commun 11:93, 2023","journal-title":"Acta Neuropathol Commun"},{"key":"1372_CR66","doi-asserted-by":"crossref","first-page":"3398","DOI":"10.1093\/brain\/awr253","volume":"134","author":"GF Mitchell","year":"2011","unstructured":"Mitchell GF, et al.: Arterial stiffness, pressure and flow pulsatility and brain structure and function: the Age, Gene\/Environment Susceptibility--Reykjavik study. Brain 134:3398-3407, 2011","journal-title":"Brain"},{"key":"1372_CR67","doi-asserted-by":"crossref","first-page":"2175","DOI":"10.1038\/s41467-020-15932-3","volume":"11","author":"E Persyn","year":"2020","unstructured":"Persyn E, Hanscombe KB, Howson JMM, Lewis CM, Traylor M, Markus HS: Genome-wide association study of MRI markers of cerebral small vessel disease in 42,310 participants. Nat Commun 11:2175, 2020","journal-title":"Nat Commun"},{"key":"1372_CR68","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.neurobiolaging.2018.02.002","volume":"66","author":"M Ten Kate","year":"2018","unstructured":"Ten Kate M, et al.: White matter hyperintensities and vascular risk factors in monozygotic twins. Neurobiol Aging 66:40-48, 2018","journal-title":"Neurobiol Aging"},{"key":"1372_CR69","doi-asserted-by":"crossref","first-page":"e023159","DOI":"10.1161\/JAHA.121.023159","volume":"11","author":"TR Austin","year":"2022","unstructured":"Austin TR, et al.: Association of Brain Volumes and White Matter Injury With Race, Ethnicity, and Cardiovascular Risk Factors: The Multi-Ethnic Study of Atherosclerosis. J Am Heart Assoc 11:e023159, 2022","journal-title":"J Am Heart Assoc"},{"key":"1372_CR70","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.neurobiolaging.2022.11.012","volume":"122","author":"C Morrison","year":"2023","unstructured":"Morrison C, Dadar M, Manera AL, Collins DL: Racial differences in white matter hyperintensity burden in older adults. Neurobiol Aging 122:112-119, 2023","journal-title":"Neurobiol Aging"},{"key":"1372_CR71","doi-asserted-by":"crossref","first-page":"e010533","DOI":"10.1161\/JAHA.118.010533","volume":"7","author":"CH Sudre","year":"2018","unstructured":"Sudre CH, et al.: Cardiovascular Risk Factors and White Matter Hyperintensities: Difference in Susceptibility in South Asians Compared With Europeans. J Am Heart Assoc 7:e010533, 2018","journal-title":"J Am Heart Assoc"},{"issue":"Suppl A100","key":"1372_CR72","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1111\/ijs.12270","volume":"9","author":"V Mok","year":"2014","unstructured":"Mok V, et al.: Race-ethnicity and cerebral small vessel disease--comparison between Chinese and White populations. Int J Stroke 9 Suppl A100:36-42, 2014","journal-title":"Int J Stroke"},{"key":"1372_CR73","first-page":"1053","volume":"65","author":"AM Brickman","year":"2008","unstructured":"Brickman AM, et al.: Brain morphology in older African Americans, Caribbean Hispanics, and whites from northern Manhattan. Archives of neurology 65:1053-1061, 2008","journal-title":"Archives of neurology"},{"key":"1372_CR74","doi-asserted-by":"crossref","first-page":"2325","DOI":"10.1038\/s41598-021-81883-4","volume":"11","author":"S Grosu","year":"2021","unstructured":"Grosu S, et al.: Associated factors of white matter hyperintensity volume: a machine-learning approach. Sci Rep 11:2325, 2021","journal-title":"Sci Rep"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01372-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01372-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01372-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T22:48:32Z","timestamp":1761778112000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-024-01372-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,6]]},"references-count":74,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["1372"],"URL":"https:\/\/doi.org\/10.1007\/s10278-024-01372-8","relation":{},"ISSN":["2948-2933"],"issn-type":[{"type":"electronic","value":"2948-2933"}],"subject":[],"published":{"date-parts":[[2025,1,6]]},"assertion":[{"value":"27 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 December 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This cross-sectional study was approved by the institutional review board at the AMC (IRB 2021\u20130682) and KoGES (IRB 2006AS0045). The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Written informed consent from participants was waived by the ethics committee at the AMC because of the retrospective nature of the study. For KoGES, written informed consent was obtained from all participants.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"This paper does not involve any identifiable individual data.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}