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

Ruibin Huang1, Jiawei Chen2, Huanpeng Wang1

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Deep learning accurately quantifies temporal muscle thickness (TMT) and area (TMA) in acute ischemic stroke (AIS) patients. These measurements are robust prognostic markers, aiding in risk stratification and personalized rehabilitation.

Keywords:
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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in healthcare
  • Neurology and stroke research

Background:

  • Acute ischemic stroke (AIS) poses significant health challenges.
  • Accurate prognostic markers are crucial for patient management and rehabilitation planning.
  • Temporal muscle (TM) measurements may offer prognostic insights in AIS.

Purpose of the Study:

  • To develop a deep learning (DL) pipeline for automated analysis of temporal muscle (TM) parameters in AIS patients.
  • To assess the prognostic value of TM thickness (TMT) and TM area (TMA) in predicting 6-month outcomes after AIS.
  • To enable rapid and scalable quantification of TMT and TMA for clinical integration.

Main Methods:

  • A DL pipeline was developed using ResNet50 for slice selection and TransUNet for TM segmentation.
  • The pipeline was trained and validated on datasets comprising 1020 AIS patients.
  • Performance was evaluated using accuracy, ±1 slice accuracy, mean absolute error, and Dice similarity coefficient (DSC).
  • The association between DL-quantified TMT and TMA and 6-month outcomes was analyzed.

Main Results:

  • The DL system achieved high accuracy in slice selection (72.91%) and TM segmentation (DSC of 0.858).
  • Automatically quantified TMT and TMA were independently associated with poor 6-month outcomes in AIS patients.
  • Hazard ratios indicated a protective effect of higher TMT and TMA on outcomes (TMT: 0.736, TMA: 0.702).

Conclusions:

  • Temporal muscle thickness (TMT) and area (TMA) are significant prognostic markers in AIS.
  • The developed end-to-end DL pipeline provides automated, rapid quantification of TMT and TMA.
  • This technology supports scalable risk stratification and personalized rehabilitation planning for AIS patients.