0567 Enhancing Central Sleep Apnea Detection via AI-Driven Autoscoring by a Wearable Home Sleep Apnea Test

Abstract Introduction Artificial intelligence (AI) is reshaping medical diagnostics by improving accuracy, consistency, and scalability across diverse clinical domains. The Wesper Lab Home Sleep Apnea Test (HSAT) Autoscoring Algorithm (AA) applies AI-based analysis to direct measurements of respiratory effort, airflow, and SpO₂. This study validated the accuracy of the AA for scoring central apnea events compared with manual human scoring (MHS). Methods We performed a multi-tiered validation using two datasets to assess the accuracy of the AA for determining the central apnea index (CAI). The primary analysis compared CAI from simultaneous HSAT and polysomnography (PSG) studies in 45 patients (27 males, 18 females; mean age 48.8 ± 14.7 yr) scored by blinded technologists. The secondary analysis evaluated the AA against 45 HSAT recordings scored by a blinded scorer. The tertiary analysis examined 129 HSAT sessions from 104 patients (81 males, 46 females, 2 undetermined; mean age 49.9 ± 13.8 yr; BMI 33.9 ± 7.6) manually rescored across 11 U.S. clinics. Agreement between AA and manual scoring for CAI was assessed using Pearson correlation and Bland–Altman limits of agreement (LOA). Results In the primary analysis comparing the AA to PSG studies, the AA demonstrated strong agreement for CAI (r = 0.95, 95% LOA = –2.8 to 2.9 events/h; p < 0.001). In the secondary analysis of 45 HSAT studies manually scored by blinded technologists, the AA maintained excellent concordance (r = 0.93, 95% LOA = –1.49 to 1.90; p = 0.0038). In the tertiary analysis of 129 real-world HSATs manually rescored, the AA achieved r = 0.98 with Bland–Altman 95% LOA of –3.53 to 3.45 for CAI, exceeding the predefined performance goal (p < 0.0001). Conclusion The AA demonstrated excellent accuracy for detecting central sleep apnea across all validation tiers. By integrating AI with direct respiratory effort and airflow signals, the system overcomes key limitations of traditional HSATs that rely on indirect or surrogate markers. Implementation of AI-based autoscoring may enhance diagnostic precision, reduce human scoring variability, and expand the scalability of home-based sleep testing for central sleep apnea. Support (if any)