Sleep Architecture Associations with Brain Age: A Multi-Site Model Validation
This research study evaluates large multi-site datasets and assesses the relationship of N3/REM sleep duration with the predicted brain age.
Deep Learning to Predict PAP Adherence in Obstructive Sleep Apnea
In this study, EnsoData shows how ML algorithms based on PAP usage can predict future adherence, offering potential for personalized treatment decisions and preemptive interventions when upcoming non-adherence is predicted.
Artificial Intelligence to Aid in Diagnosis of Type I Narcolepsy
This research study demonstrated that Machine Learning methods can automatically detect Type I Narcolepsy using in PSG-EEG with promising degrees of accuracy.
Use of Artificial Intelligence for Early Characterization of Patients with RBD
This study demonstrates the ability of AI approaches produced high specificity and moderate sensitivity for REM Behavior Disorder and the potential to expand early detection and diagnosis of RBD.
Deep Learning Classification of Future PAP Adherence based on CMS and other Adherence Criteria
This research study shows how AI can deliver strong predictive performance for PAP adherence within the first few weeks of therapy, enabling early PAP intervention or transition to alternative therapies sooner in the process and improving patient outcomes.