How will artificial intelligence (AI) advance sleep medicine?
This research abstract addresses various components and methods deployed in AI and covers examples of how AI is used to screen, endotype, diagnose, and treat sleep disorders.
Clinical Validation of AI Scoring in Adult and Pediatric Clinical PSG Samples Compared to Prospective, Double-Blind Scoring Panel
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.
Clinical Validation of AI Analysis of PPG Based Sleep-Wake Staging, Total Sleep Time, and Respiratory Rate
In this study, we clinically validate the performance of AI for interoperable, PPG-based sleep staging and sleep disordered breathing event detection.
Redefining Positive Airway Pressure (PAP) Adherence Phenotypes Utilizing Deep Neural Networks and Unsupervised Clustering
This study looks to identify CPAP adherence phenotypes in order to assist with the creation of more precise and personalized patient categorization and treatment.
Dynamic Phenotype Learning: A Novel Machine Learning Approach To Develop And Discover New OSA Sub-Types
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.
Evaluation of Electronic Medical Record Artificial Intelligence Screening Tools for Undiagnosed OSA
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.
EEG-Based Deep Neural Network Model for Brain Age Prediction and its Association with Patient Health Conditions
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.
Using AI to Predict Future CPAP Adherence and the Impact of Behavioral and Technical Interventions
This research demonstrates a novel application for AI to aid clinicians in maintaining CPAP adherence across identifiable phenotypes.
Using Novel EEG Phenotypes and Artificial Intelligence to Estimate OSA Severity
This research demonstrated a promising opportunity to estimate OSA severity with a host of EEG study types using applied artificial intelligence.
“Be thoughtful, be mindful, and take your time in situations where bias could be present.”
Andrea Ramberg, RPSGT, CCSH EnsoData Clinical Director
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