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. 2024 Jun 21:16:1399457.
doi: 10.3389/fnagi.2024.1399457. eCollection 2024.

Impact of white matter hyperintensity volumes estimated by automated methods using deep learning on stroke outcomes in small vessel occlusion stroke

Minwoo Lee #  1 , Chong Hyun Suh #  2 , Jong-Hee Sohn  3   4 , Chulho Kim  3   4 , Sang-Won Han  3   4 , Joo Hye Sung  3   4 , Kyung-Ho Yu  1 , Jae-Sung Lim  5 , Sang-Hwa Lee  3   4
Affiliations

Impact of white matter hyperintensity volumes estimated by automated methods using deep learning on stroke outcomes in small vessel occlusion stroke

Minwoo Lee et al. Front Aging Neurosci. .

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.

Keywords: SVO stroke; machine learning; small vessel disease; stroke outcome; white matter hyperintensity.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Distribution of quantitative WMH volume according to the Fazekas scale in the PSM cohort. WMH, white matter hyperintensity; PSM, propensity score matching; P-Fazekas, periventricular Fazekas scale; D-Fazekas, Deep Fazekas scale. *Using the linear contrast test.
Figure 2
Figure 2
Distribution of END according to WMH burden in the total and PSM cohorts. END, early neurologic deterioration; WMH, white matter hyperintensity; PSM, propensity score matching. *Using the chi-square test.
Figure 3
Figure 3
Distribution of 3-month mRS > 2 according to WMH burden in the total and PSM cohorts. mRS, modified Rankin Scale; WMH, white matter hyperintensity; PSM, propensity score matching. *Using the chi-square test.
See this image and copyright information in PMC

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