Deep Learning-Based Automatic Classification of Ischemic Stroke Subtype Using Diffusion-Weighted Images

Autor: Wi-Sun Ryu, Dawid Schellingerhout, Hoyoun Lee, Keon-Joo Lee, Chi Kyung Kim, Beom Joon Kim, Jong-Won Chung, Jae-Sung Lim, Joon-Tae Kim, Dae-Hyun Kim, Jae-Kwan Cha, Leonard Sunwoo, Dongmin Kim, Sang-Il Suh, Oh Young Bang, Hee-Joon Bae, Dong-Eog Kim
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Journal of Stroke, Vol 26, Iss 2, Pp 300-311 (2024)
Druh dokumentu: article
ISSN: 2287-6391
2287-6405
DOI: 10.5853/jos.2024.00535
Popis: 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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