{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T12:09:46Z","timestamp":1790683786317,"version":"4.1.0"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2021,6,3]],"date-time":"2021-06-03T00:00:00Z","timestamp":1622678400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hong Kong Innovation and Technology Fund\u2014Midstream Research Programme","award":["MRP\/037\/17X"],"award-info":[{"award-number":["MRP\/037\/17X"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Retinal vessels are known to be associated with various cardiovascular and cerebrovascular disease outcomes. Recent research has shown significant correlations between retinal characteristics and the presence of cerebral small vessel disease as measured by white matter hyperintensities from cerebral magnetic resonance imaging. Early detection of age-related white matter changes using retinal images is potentially helpful for population screening and allow early behavioural and lifestyle intervention. This study investigates the ability of the machine-learning method for the localization of brain white matter hyperintensities. All subjects were age 65 or above without any history of stroke and dementia and recruited from local community centres and community networks. Subjects with known retinal disease or disease influencing vessel structure in colour retina images were excluded. All subjects received MRI on the brain, and age-related white matter changes grading was determined from MRI as the primary endpoint. The presence of age-related white matter changes on each of the six brain regions was also studied. Retinal images were captured using a fundus camera, and the analysis was done based on a machine-learning approach. A total of 240 subjects are included in the study. The analysis of various brain regions included the left and right sides of frontal lobes, parietal\u2013occipital lobes and basal ganglia. Our results suggested that data from both eyes are essential for detecting age-related white matter changes in the brain regions, but the retinal parameters useful for estimation of the probability of age-related white matter changes in each of the brain regions may differ for different locations. Using a classification and regression tree approach, we also found that at least three significant heterogeneous subgroups of subjects were identified to be essential for the localization of age-related white matter changes. Namely those with age-related white matter changes in the right frontal lobe, those without age-related white matter changes in the right frontal lobe but with age-related white matter changes in the left parietal\u2013occipital lobe, and the rest of the subjects. Outcomes such as risks of severe grading of age-related white matter changes and the proportion of hypertension were significantly related to these subgroups. Our study showed that automatic retinal image analysis is a convenient and non-invasive screening tool for detecting age-related white matter changes and cerebral small vessel disease with good overall performance. The localization analysis for various brain regions shows that the classification models on each of the six brain regions can be done, and it opens up potential future clinical application.<\/jats:p>","DOI":"10.1093\/braincomms\/fcab124","type":"journal-article","created":{"date-parts":[[2021,6,2]],"date-time":"2021-06-02T20:50:43Z","timestamp":1622667043000},"source":"Crossref","is-referenced-by-count":23,"title":["Machine-learning method for localization of cerebral white matter hyperintensities in healthy adults based on retinal images"],"prefix":"10.1093","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7238-845X","authenticated-orcid":false,"given":"Benny","family":"Zee","sequence":"first","affiliation":[{"name":"Centre for Clinical Research and Biostatistics, Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China"},{"name":"Clinical Trials and Biostatistics Lab, CUHK Shenzhen Research 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China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhua","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Neurology, First Affiliated Hospital of Sun Yat-Sen University, Guangdong, China"},{"name":"Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department, National Key Discipline, Guangzhou 510080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinsheng","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Neurology, First Affiliated Hospital of Sun Yat-Sen University, Guangdong, China"},{"name":"Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department, National Key Discipline, Guangzhou 510080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bonnie","family":"Lam","sequence":"additional","affiliation":[{"name":"Margaret KL Cheung Research Centre for Management of Parkinsonism, Therese Pei Fong Chow Research Centre for Prevention of Dementia and Gerald Choa Neuroscience Centre, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China"},{"name":"Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adrian","family":"Wong","sequence":"additional","affiliation":[{"name":"Margaret KL Cheung Research Centre for Management of Parkinsonism, Therese Pei Fong Chow Research Centre for Prevention of Dementia and Gerald Choa Neuroscience Centre, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China"},{"name":"Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Shi","sequence":"additional","affiliation":[{"name":"BrainNow Research Institute, Shenzhen, Guangdong Province, China"},{"name":"Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Allen","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chloe","family":"Kwok","sequence":"additional","affiliation":[{"name":"Centre for Clinical Research and Biostatistics, Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Lai","sequence":"additional","affiliation":[{"name":"Centre for Clinical Research and Biostatistics, Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincent","family":"Mok","sequence":"additional","affiliation":[{"name":"Margaret KL Cheung Research Centre for Management of Parkinsonism, Therese Pei Fong Chow Research Centre for Prevention of Dementia and Gerald Choa Neuroscience Centre, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China"},{"name":"Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, 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