Clinical Validation of Artificial Intelligence Algorithms for the Diagnosis of Adult Obstructive Sleep Apnea and Sleep Staging From Oximetry and Photoplethysmography— SleepAI - Attia, Shirel; Oksenberg, Arie; Levy, Jeremy; Ang, Angeleene; Shani‐Hershkovich, Revital; Adler, Alissa; Katsav, Shlomit; Haimov, Sharon; Alexandrovich, Alexandra; Tauman, Riva; Behar, Joachim A. (Journal of sleep research, 2025)
Home sleep apnea tests (HSATs) have emerged as alternatives to in‐laboratory polysomnography (PSG), but Type IV HSATs often show limited diagnostic performance. This study clinically validates SleepAI, a novel remote digital health system that applies AI algorithms to raw oximetry data for automated sleep staging and obstructive sleep apnea (OSA) diagnosis. SleepAI algorithms were trained on over 10,000 PSG recordings. The system consists of a wearable oximeter connected via Bluetooth to a mobile app transmitting raw data to a cloud‐based platform for AI‐driven analysis. Clinical validation was conducted in 53 subjects with suspected OSA, who used SleepAI for three nights at home and one night in a sleep centre alongside PSG. SleepAI's apnea‐hypopnea index (AHI) estimates and three‐class sleep staging (Wake, REM, NREM) were compared to PSG references. For OSA severity classification (non‐OSA, mild, moderate, severe), SleepAI achieved an overall accuracy of 89%, with F1‐scores of 1.0, 1.0, 0.9, and 0.88, respectively. The three‐stage sleep classification achieved a Cohen's kappa of 0.75. Night‐to‐night AHI variability showed that 37.5% of participants experienced a one‐level severity change across nights at home. No significant differences in sleep metrics were found between the first and subsequent nights at home, indicating no sleep disturbance by SleepAI. These findings support the SleepAI system as a promising and scalable alternative to existing Type IV HSATs, with the potential to address key clinical gaps by improving diagnostic accuracy and accessibility.