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Differentiation between multiple sclerosis and neuromyelitis optica spectrum disorder using a deep learning model.

Jin Myoung Seok1, Wanzee Cho2, Yeon Hak Chung3,4

  • 1Department of Neurology, Soonchunhyang University Hospital Cheonan, Soonchunhyang University College of Medicine, Cheonan, South Korea.

Scientific Reports
|July 19, 2023
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Summary

A new deep learning model accurately differentiates multiple sclerosis (MS) from neuromyelitis optica spectrum disorder (NMOSD) using brain MRI scans. This AI tool can aid clinicians in diagnosing these central nervous system inflammatory conditions.

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Area of Science:

  • Neurology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are central nervous system autoimmune inflammatory diseases with overlapping clinical and radiological features.
  • Accurate differential diagnosis between MS and NMOSD is crucial for timely and effective treatment initiation.

Purpose of the Study:

  • To develop and evaluate a deep learning model for distinguishing between MS and NMOSD using brain MRI data.
  • To assess the diagnostic performance of the proposed model in a clinical setting.

Main Methods:

  • A modified ResNet18 convolutional neural network was employed.
  • The model was trained using 5-channel images derived from selected 2D slices of 3D FLAIR MRI sequences.
  • Grad-CAM was utilized for model interpretability to identify key classification features.

Main Results:

  • The deep learning model achieved an overall accuracy of 76.1%.
  • Sensitivity and specificity were reported as 77.3% and 74.8%, respectively.
  • The model demonstrated a positive predictive value of 76.9% and a negative predictive value of 78.6%, with an AUC of 0.85.
  • Analysis indicated that white matter lesions were the primary features driving the classification.

Conclusions:

  • The developed deep learning model shows promise in aiding the differential diagnosis of MS and NMOSD.
  • The model's ability to identify white matter lesions as key discriminators highlights its potential clinical utility.
  • This AI-powered approach may support clinicians in managing patients with these complex neurological disorders.