Utilizing Artificial Intelligence to Optimize Diagnosis of Obstructive Sleep Apnea

2019 Strategic Research Grant

Michelle Zeidler, MD, MS
University of California Los Angeles – David Geffen School of Medicine

Key Project Outcomes

Using demographic, anthropometric, medical and psychiatric comorbidities, and patient-reported outcomes, we were unable to predict which patient would not be a good candidate for a home sleep test vs an in-laboratory sleep study. These results suggest the decision to select a home sleep test rather than in-laboratory polysomnography as the initial test to diagnose obstructive sleep apnea should be based on patient preferences and clinical judgment on a case-by-case basis.