  Background: An early diagnosis together with an accurate disease progression
monitoring of multiple sclerosis is an important component of successful
disease management. Prior studies have established that multiple sclerosis is
correlated with speech discrepancies. Early research using objective acoustic
measurements has discovered measurable dysarthria.
  Objective: To determine the potential clinical utility of machine learning
and deep learning/AI approaches for the aiding of diagnosis, biomarker
extraction and progression monitoring of multiple sclerosis using speech
recordings.
  Methods: A corpus of 65 MS-positive and 66 healthy individuals reading the
same text aloud was used for targeted acoustic feature extraction utilizing
automatic phoneme segmentation. A series of binary classification models was
trained, tuned, and evaluated regarding their Accuracy and area-under-curve.
  Results: The Random Forest model performed best, achieving an Accuracy of
0.82 on the validation dataset and an area-under-curve of 0.76 across 5 k-fold
cycles on the training dataset. 5 out of 7 acoustic features were statistically
significant.
  Conclusion: Machine learning and artificial intelligence in automatic
analyses of voice recordings for aiding MS diagnosis and progression tracking
seems promising. Further clinical validation of these methods and their mapping
onto multiple sclerosis progression is needed, as well as a validating utility
for English-speaking populations.
