Deep-learning-based detection of large vessel occlusion: A comparison of CT and diffusion-weighted imaging
Background Rapid and accurate identification of large vessel occlusion (LVO) is crucial for determining eligibility for endovascular treatment. We aimed to validate whether computed tomography combined with clinical information (CT&CI) or diffusion-weighted imaging (DWI) offers better predictive...
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SAGE Publishing
2025-04-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076251334040 |
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| author | JaeYoung Kang JunYoung Park YoungJae Kim BumJoon Kim SangHee Ha KwangGi Kim |
| author_facet | JaeYoung Kang JunYoung Park YoungJae Kim BumJoon Kim SangHee Ha KwangGi Kim |
| author_sort | JaeYoung Kang |
| collection | DOAJ |
| description | Background Rapid and accurate identification of large vessel occlusion (LVO) is crucial for determining eligibility for endovascular treatment. We aimed to validate whether computed tomography combined with clinical information (CT&CI) or diffusion-weighted imaging (DWI) offers better predictive accuracy for anterior circulation LVO. Methods Computed tomography combined with clinical information and DWI data from patients diagnosed with acute ischemic stroke were collected. Three deep-learning models, convolutional neural network, EfficientNet-B2, and DenseNet121, were used to compare CT&CI and DWI for detecting anterior circulation LVO. Results A total of 456 patients, 228 patients with LVO [68.91 ± 12.84 years, 63.60% male; initial National Institutes of Health Stroke Scale (NIHSS) score: median 11 (7–14)] and without LVO [67.06 ± 12.29 years, 64.04% male; initial NIHSS score: median 2 (1–4)] were enrolled. Diffusion-weighted imaging achieved better results than CT&CI did in each performance metric. In DenseNet121, the area under the curves (AUCs) were found to be 0.833 and 0.756, respectively, while in EfficientNet-B2, the AUCs were 0.815 and 0.647, respectively. Conclusions In detecting the presence of anterior circulation LVO, DWI showed better results in each performance metric than CT&CI did, and the best-performing deep-learning model was DenseNet121. |
| format | Article |
| id | doaj-art-0ea87129bd224933b3aa1f04c664bbd0 |
| institution | OA Journals |
| issn | 2055-2076 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SAGE Publishing |
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| spelling | doaj-art-0ea87129bd224933b3aa1f04c664bbd02025-08-20T02:19:51ZengSAGE PublishingDigital Health2055-20762025-04-011110.1177/20552076251334040Deep-learning-based detection of large vessel occlusion: A comparison of CT and diffusion-weighted imagingJaeYoung Kang0JunYoung Park1YoungJae Kim2BumJoon Kim3SangHee Ha4KwangGi Kim5 , Daegu, Republic of Korea Department of Health Science and Technology, Gachon Advanced Institute for Health Science and Technology, , Incheon, Republic of Korea Gachon Biomedical and Convergence Institute, , Incheon, Republic of Korea , University of Ulsan, Seoul, Republic of Korea Department of Neurology, Gil Medical Center, , Incheon, Republic of Korea Department of Biomedical Engineering, College of Medicine, , Incheon, Republic of Korea Background Rapid and accurate identification of large vessel occlusion (LVO) is crucial for determining eligibility for endovascular treatment. We aimed to validate whether computed tomography combined with clinical information (CT&CI) or diffusion-weighted imaging (DWI) offers better predictive accuracy for anterior circulation LVO. Methods Computed tomography combined with clinical information and DWI data from patients diagnosed with acute ischemic stroke were collected. Three deep-learning models, convolutional neural network, EfficientNet-B2, and DenseNet121, were used to compare CT&CI and DWI for detecting anterior circulation LVO. Results A total of 456 patients, 228 patients with LVO [68.91 ± 12.84 years, 63.60% male; initial National Institutes of Health Stroke Scale (NIHSS) score: median 11 (7–14)] and without LVO [67.06 ± 12.29 years, 64.04% male; initial NIHSS score: median 2 (1–4)] were enrolled. Diffusion-weighted imaging achieved better results than CT&CI did in each performance metric. In DenseNet121, the area under the curves (AUCs) were found to be 0.833 and 0.756, respectively, while in EfficientNet-B2, the AUCs were 0.815 and 0.647, respectively. Conclusions In detecting the presence of anterior circulation LVO, DWI showed better results in each performance metric than CT&CI did, and the best-performing deep-learning model was DenseNet121.https://doi.org/10.1177/20552076251334040 |
| spellingShingle | JaeYoung Kang JunYoung Park YoungJae Kim BumJoon Kim SangHee Ha KwangGi Kim Deep-learning-based detection of large vessel occlusion: A comparison of CT and diffusion-weighted imaging Digital Health |
| title | Deep-learning-based detection of large vessel occlusion: A comparison of CT and diffusion-weighted imaging |
| title_full | Deep-learning-based detection of large vessel occlusion: A comparison of CT and diffusion-weighted imaging |
| title_fullStr | Deep-learning-based detection of large vessel occlusion: A comparison of CT and diffusion-weighted imaging |
| title_full_unstemmed | Deep-learning-based detection of large vessel occlusion: A comparison of CT and diffusion-weighted imaging |
| title_short | Deep-learning-based detection of large vessel occlusion: A comparison of CT and diffusion-weighted imaging |
| title_sort | deep learning based detection of large vessel occlusion a comparison of ct and diffusion weighted imaging |
| url | https://doi.org/10.1177/20552076251334040 |
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