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Ryu WS, Schellingerhout D, Lee H, Lee KJ, Kim CK, Kim BJ, Chung JW, Lim JS, Kim JT, Kim DH, Cha JK, Sunwoo L, Kim D, Suh SI, Bang OY, Bae HJ, Kim DE. Deep Learning-Based Automatic Classification of Ischemic Stroke Subtype Using Diffusion-Weighted Images. J Stroke 2024; 26:300-311. [PMID: 38836277 PMCID: PMC11164582 DOI: 10.5853/jos.2024.00535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 04/04/2024] [Accepted: 05/07/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND AND PURPOSE Accurate classification of ischemic stroke subtype is important for effective secondary prevention of stroke. We used diffusion-weighted image (DWI) and atrial fibrillation (AF) data to train a deep learning algorithm to classify stroke subtype. METHODS Model development was done in 2,988 patients with ischemic stroke from three centers by using U-net for infarct segmentation and EfficientNetV2 for subtype classification. Experienced neurologists (n=5) determined subtypes for external test datasets, while establishing a consensus for clinical trial datasets. Automatically segmented infarcts were fed into the model (DWI-only algorithm). Subsequently, another model was trained, with AF included as a categorical variable (DWI+AF algorithm). These models were tested: (1) internally against the opinion of the labeling experts, (2) against fresh external DWI data, and (3) against clinical trial dataset. RESULTS In the training-and-validation datasets, the mean (±standard deviation) age was 68.0±12.5 (61.1% male). In internal testing, compared with the experts, the DWI-only and the DWI+AF algorithms respectively achieved moderate (65.3%) and near-strong (79.1%) agreement. In external testing, both algorithms again showed good agreements (59.3%-60.7% and 73.7%-74.0%, respectively). In the clinical trial dataset, compared with the expert consensus, percentage agreements and Cohen's kappa were respectively 58.1% and 0.34 for the DWI-only vs. 72.9% and 0.57 for the DWI+AF algorithms. The corresponding values between experts were comparable (76.0% and 0.61) to the DWI+AF algorithm. CONCLUSION Our model trained on a large dataset of DWI (both with or without AF information) was able to classify ischemic stroke subtypes comparable to a consensus of stroke experts.
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Affiliation(s)
- Wi-Sun Ryu
- Department of Neurology, Dongguk University Ilsan Hospital, Goyang, Korea
- Artificial Intelligence Research Center, JLK Inc., Seoul, Korea
| | - Dawid Schellingerhout
- Department of Neuroradiology and Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Hoyoun Lee
- Artificial Intelligence Research Center, JLK Inc., Seoul, Korea
| | - Keon-Joo Lee
- Department of Neurology, Korea University Guro Hospital, Seoul, Korea
| | - Chi Kyung Kim
- Department of Neurology, Korea University Guro Hospital, Seoul, Korea
| | - Beom Joon Kim
- Department of Neurology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Jong-Won Chung
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jae-Sung Lim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Joon-Tae Kim
- Department of Neurology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
| | - Dae-Hyun Kim
- Department of Neurology, Dong-A University Hospital, Busan, Korea
| | - Jae-Kwan Cha
- Department of Neurology, Dong-A University Hospital, Busan, Korea
| | - Leonard Sunwoo
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Dongmin Kim
- Artificial Intelligence Research Center, JLK Inc., Seoul, Korea
| | - Sang-Il Suh
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
| | - Oh Young Bang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Hee-Joon Bae
- Department of Neurology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Dong-Eog Kim
- Department of Neurology, Dongguk University Ilsan Hospital, Goyang, Korea
- National Priority Research Center for Stroke, Goyang, Korea
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Brainin M. Polypills for stroke prevention: they work and are effective. Eur J Neurol 2021; 28:3879-3880. [PMID: 34510625 DOI: 10.1111/ene.15100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 09/03/2021] [Indexed: 11/30/2022]
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