Deep learning-based temporal muscle quantification on MRI predicts adverse outcomes in acute ischemic stroke

To develop a deep learning (DL) pipeline for accurate slice selection, temporal muscle (TM) segmentation, TM thickness (TMT) and area (TMA) quantification, and assessment of the prognostic role of TMT and TMA in acute ischemic stroke (AIS) patients.

A total of 1020 AIS patients were enrolled. Participants were divided into three datasets: Dataset 1 (n = 295) for slice selection using ResNet50 model, Dataset 2 (n = 258) for TM segmentation employing TransUNet-based algorithm, and Dataset 3 (n = 467) for evaluating DL-based quantification of TMT and TMA as prognostic factors in AIS. The ability of the DL system to select slices was assessed using accuracy, ±1 slice accuracy and mean absolute error. The Dice similarity coefficient (DSC) is used to assess the performance of the DL system on TM segmentation. The association between automatic quantification of TMT and TMA and 6-month outcomes was determined.

Automatic slice selection achieved a mean accuracy of 72.91 %, 97.94 % ± 1 slice accuracy with a mean absolute error of 1.54 mm, while TM segmentation on T1WI achieved a mean DSC of 0.858. Automatically extracted TMT and TMA were each independently associated with 6-month poor outcomes in AIS patients after adjusting for age, sex, onodera nutritional prognosis index, systemic immune-inflammation index, albumin levels, and smoking/drinking history (TMT: hazard ratio 0.736, 95 % confidence interval 0.528-0.931; TMA: hazard ratio 0.702, 95 % confidence interval 0.541-0.910).

TMT and TMA are robust prognostic markers in AIS patients, and our end-to-end DL pipeline enables rapid, automated quantification that integrates seamlessly into clinical workflows, supporting scalable risk stratification and personalized rehabilitation planning.