Abstract
Deep Learning for the Assisted Diagnosis of Movement Disorders, Including Isolated Dystonia.
Highlights
Functional movement disorder (FMD) is a disorder that is altered by distraction or non-physiological maneuvers; this disorder is clinically different from movement disorders known to be caused by neurological disease [1]
The biomarker-based on DystoniaNet depicted an overall accuracy of 98.8%, including 3.5% cases in which the network referred the case for further analysis with a diagnosis time of 0.36 s per subject; this was a significant improvement over shallow machine learning networks
The assisted diagnosis of isolated dystonia using deep learning has opened a new direction where computational intelligence plays its part in the early diagnosis of movement disorders
Summary
Functional movement disorder (FMD) is a disorder that is altered by distraction or non-physiological maneuvers (this includes excessive placebo response); this disorder is clinically different from movement disorders known to be caused by neurological disease [1]. Dystonia is a movement disorder classified by involuntary patterned or twisting body movements, which further results in atypical postures [2]. In a recent research paper by Valeriani and Simonyan [4], a deep learning-based method was proposed, i.e., DystoniaNet, which can recognize a microstructural neural network biomarker for the diagnosis of dystonia from raw MRIs. The biomarker-based on DystoniaNet depicted an overall accuracy of 98.8%, including 3.5% cases in which the network referred the case for further analysis with a diagnosis time of 0.36 s per subject; this was a significant improvement over shallow machine learning networks. The assisted diagnosis of isolated dystonia using deep learning has opened a new direction where computational intelligence plays its part in the early diagnosis of movement disorders
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