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Abstract #1903

Automatic feature extraction and machine learning prediction of stroke functional outcome based on histogram information of baseline ADC

Yoon-Chul Kim1, In-Young Baek2, Ji-Eun Lee2, Ha Na Song2, and Woo-Keun Seo2
1Clinical Research Institute, Samsung Medical Center, Sungkyunkwan Univ. Sch. of Med., Seoul, Republic of Korea, 2Department of Neurology, Samsung Medical Center, Sungkyunkwan Univ. School of Medicine, Seoul, Republic of Korea

This study demonstrates an automatic method that predicts favorable/unfavorable clinical outcome based on pre-treatment DWI data and machine learning (ML) in acute ischemic stroke. We present the use of ADC histogram information in the brain tissue as features for the ML prediction. In the histogram analysis, the 5 or 10 percentile value of the ADC distribution was indicative of clinical outcome regardless of success/failure of recanalization. The ROC analysis in unseen test subjects resulted in an area under the curve (AUC) of 0.79 with the proposed feature extraction, which was greater than 0.71 with the DWI lesion volume only.

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