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Background: An early diagnosis together with an accurate disease progressionmonitoring of multiple sclerosis is an important component of successfuldisease management. Prior studies have established that multiple sclerosis iscorrelated with speech discrepancies. Early research using objective acousticmeasurements has discovered measurable dysarthria.  Objective: To determine the potential clinical utility of machine learningand deep learning/AI approaches for the aiding of diagnosis, biomarkerextraction and progression monitoring of multiple sclerosis using speechrecordings.  Methods: A corpus of 65 MS-positive and 66 healthy individuals reading thesame text aloud was used for targeted acoustic feature extraction utilizingautomatic phoneme segmentation. A series of binary classification models wastrained, tuned, and evaluated regarding their Accuracy and area-under-curve.  Results: The Random Forest model performed best, achieving an Accuracy of0.82 on the validation dataset and an area-under-curve of 0.76 across 5 k-foldcycles on the training dataset. 5 out of 7 acoustic features were statisticallysignificant.  Conclusion: Machine learning and artificial intelligence in automaticanalyses of voice recordings for aiding MS diagnosis and progression trackingseems promising. Further clinical validation of these methods and their mappingonto multiple sclerosis progression is needed, as well as a validating utilityfor English-speaking populations.

Assessing clinical utility of Machine Learning and Artificial  Intelligence approaches to analyze speech recordings in Multiple Sclerosis: A  Pilot Study