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.
This research demonstrates a novel application for AI to aid clinicians in maintaining CPAP adherence across identifiable phenotypes.
This research demonstrated a promising opportunity to estimate OSA severity with a host of EEG study types using applied artificial intelligence.
This research highlights how interpretable machine learning models show strong promise as another means for determining therapeutic CPAP pressures.
This work demonstrates that consensus-based reference for sleep study analysis may be constructed and used for Inter-Scorer Reliability (ISR) assessments to enable measurement of scoring agreement with greater reproducibility.
In this study, we utilize a Computational Phenotyping approach using Polysomnography (PSG) data to predict adverse health outcomes based on common clinical variables and interpretable physiological features, providing a clear explanation as to why each estimation is made.