Improving the Accuracy of Diagnosis for Multiple-System Atrophy Using Deep Learning-Based Method

Abstract

Multiple-system atrophy (MSA) is primarily an autonomic disorder with parkinsonism or cerebellar ataxia. Clinical diagnosis of MSA at an early stage is challenging because the symptoms change over the course of the disease. Recently, various artificial intelligence-based programs have been developed to improve the diagnostic accuracy of neurodegenerative diseases, but most are limited to the evaluation of diagnostic imaging. In this study, we examined the validity of diagnosis of MSA using a pointwise linear model (deep learning-based method). The goal of the study was to identify features associated with disease differentiation that were found to be important in deep learning. A total of 3377 registered MSA cases from FY2004 to FY2008 were used to train the model. The diagnostic probabilities of SND (striatonigral degeneration), SDS (Shy-Drager syndrome), and OPCA (olivopontocerebellar atrophy) were estimated to be 0.852 ± 0.107, 0.650 ± 0.235, and 0.858 ± 0.270, respectively. In the pointwise linear model used to identify and visualize features involved in individual subtypes, autonomic dysfunction was found to be a more prominent component of SDS compared to SND and OPCA. Similarly, respiratory failure was identified as a characteristic of SDS, dysphagia was identified as a characteristic of SND, and brain-stem atrophy was identified as a characteristic of OPCA.