A microstructural neural network biomarker for dystonia diagnosis identified by a DystoniaNet deep learning platform

What is it about?

Dystonia is a debilitating movement disorder causing involuntary muscle contractions and abnormal postures that lead to chronic stress, social isolation, and decreased quality of life. Diagnosis of dystonia is a challenging due to the absence of a biomarker, with patients remaining undiagnosed for up to 10 years. A microstructural neural network biomarker was identified for objective and accurate diagnosis of isolated dystonia based on the disorder pathophysiology using an advanced deep learning algorithm, DystoniaNet, and raw structural brain images of large cohorts of patients with isolated focal dystonia and healthy controls. DystoniaNet significantly outperformed shallow machine-learning pipelines and substantially exceeded the current agreement rates between clinicians, reaching an overall accuracy of 98.8% in diagnosing different forms of isolated focal dystonia. DystoniaNet may serve as an objective, robust, and generalizable algorithmic platform of dystonia diagnosis for enhanced clinical decision-making. Implementation of the identified biomarker for objective and accurate diagnosis of dystonia may be transformative for clinical management of this disorder.

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