Automatic stridor detection using small training set via patch-wise few-shot learning for diagnosis of multiple system atrophy.

ABSTRACT: Stridor is a rare but important non-motor symptom that can support the diagnosis and prediction of worse prognosis in multiple system atrophy. Recording sounds generated during sleep by video-polysomnography is recommended for detecting stridor, but the analysis is labor intensive and time consuming. A method for automatic stridor detection should be developed using technologies such as artificial intelligence (AI) or machine learning. However, the rarity of stridor hinders the collection of sufficient data from diverse patients. Therefore, an AI method with high diagnostic performance should be devised to address this limitation. We propose an AI method for detecting patients with stridor by combining audio splitting and reintegration with few-shot learning for diagnosis. We used video-po

SUBMITTER: Ahn JH 

PROVIDER: S-EPMC10323004 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature