In this study, we contribute to this growing body of clinical AI validation evidence and experimental design methodologies with an interoperable AI scoring engine for sleep studies in Adult and Pediatric populations.
In this study, we clinically validate the performance of AI for interoperable, PPG-based sleep staging and sleep disordered breathing event detection.
This study looks to identify CPAP adherence phenotypes in order to assist with the creation of more precise and personalized patient categorization and treatment.
This research abstract examines the ability to utilize Dynamic Phenotype Learning (DPL) as an innovative machine learning technique to identify OSA subtypes that can better predict clinical risk and success with therapies.
In this study, we demonstrate how AI methodologies can be utilized together with existing EMR data to create new screening tools to increase diagnostic sensitivity and facilitate discovery of preclinical OSA phenotypes.
In this study, we show deep neural networks (a subset of machine learning) can accurately predict the brain age of healthy patients based on their raw, PSG derived, EEG recordings.