Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
564-06-016 ISMRM Abstract

Automated White Matter Lesion Detection Among People Living with HIV: A Preliminary Analysis

Primary: Neuro - White Matter
Secondary: Analysis Methods - Segmentation and Detection
564-06-016 · Segmentation for Neuro Applications · Wednesday, 13 May, 4:55 PM–5:50 PM · Digital Posters Row E
Keywords: White Matter MRI White matter hyperintensity HIV Fazekas
Accepted
Sophia J Wang 1, Yannan Yu2, David Saloner3, Felicia C Chow4, Jared Narvid2
1Radiology, UCSF, San Francisco, United States of America
2UCSF-Radiology, United States of America
3University of California San Francisco — Radiology and biomedical imaging, San Francisco, United States of America
4Neurology and Medicine (Infectious Diseases), UCSF, San Francisco, United States of America
Presenting Author: Sophia J Wang

Synopsis

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References

1. Okawa R, Hayashi N, Takahashi T, Atarashi R, Yasui G, Mihara B. Comparison of qualitative and fully automated quantitative tools for classifying severity of white matter hyperintensity. J Stroke Cerebrovasc Dis Off J Natl Stroke Assoc. 2024;33(8):107772. doi:10.1016/j.jstrokecerebrovasdis.2024.107772 [doi]
2. Andere A, Jindal G, Molino J, et al. Volumetric White Matter Hyperintensity Ranges Correspond to Fazekas Scores on Brain MRI. J Stroke Cerebrovasc Dis Off J Natl Stroke Assoc. 2022;31(4):106333. doi:10.1016/j.jstrokecerebrovasdis.2022.106333 [doi]
3. Haddow LJ, Dudau C, Chandrashekar H, et al. Cross-sectional study of unexplained white matter lesions in HIV positive individuals undergoing brain magnetic resonance imaging. AIDS Patient Care STDs. 2014;28(7):341-349. doi:10.1089/apc.2013.0230 [doi]
4. Mina Y, Wu T, Hsieh HC, et al. Association of White Matter Hyperintensities With HIV Status and Vascular Risk Factors. Neurology. 2021;96(14):e1823-e1834. doi:10.1212/WNL.0000000000011702 [doi]
5. Zerna C, Yu AYX, Modi J, et al. Association of White Matter Hyperintensities With Short-Term Outcomes in Patients With Minor Cerebrovascular Events. Stroke. 2018;49(4):919-923. doi:10.1161/STROKEAHA.117.017429 [doi]
6. Cedres N, Ferreira D, Machado A, et al. Predicting Fazekas scores from automatic segmentations of white matter signal abnormalities. Aging. 2020;12(1):894-901. doi:10.18632/aging.102662 [doi]
7. Robinson-Papp J, Navis A, Dhamoon MS, Clark US, Gutierrez-Contreras J, Morgello S. The Use of Visual Rating Scales to Quantify Brain MRI Lesions in Patients with HIV Infection. J Neuroimaging Off J Am Soc Neuroimaging. 2018;28(2):217-224. doi:10.1111/jon.12466 [doi]
8. Chen YT, Huang YC, Chen HL, et al. Automatic segmentation of white matter lesions on multi-parametric MRI: convolutional neural network versus vision transformer. BMC Neurol. 2025;25(1):5. doi:10.1186/s12883-024-04010-6 [doi]
9. Schmidt P, Gaser C, Arsic M, et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. NeuroImage. 2012;59(4):3774-3783. doi:10.1016/j.neuroimage.2011.11.032 [doi]
10. Petersen KJ, Strain J, Cooley S, Vaida F, Ances BM. Machine Learning Quantifies Accelerated White-Matter Aging in Persons With HIV. J Infect Dis. 2022;226(1):49-58. doi:10.1093/infdis/jiac156 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/564-06-016