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

delete2024-05-31
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OA
AI
W
Wi‐Sun Ryu
D
Dawid Schellingerhout
H
Hoyoun Lee
K
Keon‐Joo Lee
C
Chi Kyung Kim
B
Beom Joon Kim
J
Jong‐Won Chung
J
Jae‐Sung Lim
J
Joon‐Tae Kim
D
Dae‐Hyun Kim
J
Jae‐Kwan Cha
L
Leonard Sunwoo
D
Dongmin Kim
S
Sang‐il Suh
O
Oh Young Bang
H
Hee‐Joon Bae
D
Dong‐Eog Kim *
DOI:10.5853/jos.2024.00535delete
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Abstract

Abstract

En 中文
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.
Keywords:
Deep learning
Artificial intelligence
Diffusion magnetic resonance imaging
Atrial fibrillation
Ischemic stroke
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