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Impact of white matter hyperintensity volumes estimated by automated methods using deep learning on stroke outcomes in small vessel occlusion stroke.
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- Author(s): Lee, Minwoo; Chong Hyun Suh; Jong-Hee Sohn; Chulho Kim; Sang-Won Han; Joo Hye Sung; Kyung-Ho Yu; Jae-Sung Lim; Sang-Hwa Lee
- Source:
Frontiers in Aging Neuroscience; 2024, p1-9, 9p- Subject Terms:
NIH Stroke Scale; RISK assessment; PEARSON correlation (Statistics); RESEARCH funding; COMPUTER software; T-test (Statistics); QUESTIONNAIRES; PROBABILITY theory; LOGISTIC regression analysis; FUNCTIONAL assessment; TREATMENT effectiveness; REPORTING of diseases; DESCRIPTIVE statistics; MAGNETIC resonance imaging; CHI-squared test; MANN Whitney U Test; SEVERITY of illness index; ODDS ratio; WHITE matter (Nerve tissue); DEEP learning; CLINICAL deterioration; AUTOMATION; STROKE; DIGITAL image processing; CONFIDENCE intervals; DATA analysis software; ALGORITHMS - Source:
- Additional Information
- Abstract: Introduction: Although white matter hyperintensity (WMH) shares similar vascular risk and pathology with small vessel occlusion (SVO) stroke, there were few studies to evaluate the impact of the burden of WMH volume on early and delayed stroke outcomes in SVO stroke. Materials and methods: Using a multicenter registry database, we enrolled SVO stroke patients between August 2013 and November 2022. The WMH volume was estimated by automated methods using deep learning (VUNO Med-DeepBrain, Seoul, South Korea), which was a commercially available segmentation model. After propensity score matching (PSM), we evaluated the impact of WMH volume on early neurological deterioration (END) and poor functional outcomes at 3-month modified Ranking Scale (mRS), defined as mRS score >2 at 3 months, after an SVO stroke. Results: Among 1,718 SVO stroke cases, the prevalence of subjects with severe WMH (Fazekas score ≥ 3) was 68.9%. After PSM, END and poor functional outcomes at 3-month mRS (mRS > 2) were higher in the severe WMH group (END: 6.9 vs. 13.5%, p < 0.001; 3-month mRS > 2: 11.4 vs. 24.7%, p < 0.001). The logistic regression analysis using the PSM cohort showed that total WMH volume increased the risk of END [odd ratio [OR], 95% confidence interval [CI]; 1.01, 1.00-1.02, p = 0.048] and 3-month mRS > 2 (OR, 95% CI; 1.02, 1.01-1.03, p < 0.001). Deep WMH was associated with both END and 3-month mRS > 2, but periventricular WMH was associated with 3-month mRS > 2 only. Conclusion: This study used automated methods using a deep learning segmentation model to assess the impact of WMH burden on outcomes in SVO stroke. Our findings emphasize the significance of WMH burden in SVO stroke prognosis, encouraging tailored interventions for better patient care. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Frontiers in Aging Neuroscience is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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