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A recently developed artificial intelligence (AI) algorithm has the potential to revolutionize the diagnosis of sleep disorders, such as apneas, by replacing the cumbersome and costly equipment traditionally used in sleep studies. EnsoData, a Wisconsin-based company, revealed that its algorithm has received clearance from the FDA. This innovative system analyzes data obtained during sleep studies using pulse oximeters, which are more affordable and readily available compared to the standard polysomnography equipment typically used.
The newly cleared AI tool represents an advancement of a previous algorithm created by EnsoData called EnsoSleep. Unlike its predecessor, EnsoSleep PPG focuses on interpreting light-based measurements gathered by pulse oximetry technology found in various smartwatches, rings, and wearables. Through deep learning AI, it sifts through changes in oxygen saturation levels and heart rate to identify sleep-disordered respiratory events, map a patient’s sleep cycles, and monitor other sleep metrics. Subsequently, healthcare professionals review the algorithm’s findings, highlighting key indicators before providing patients with a comprehensive sleep report.
Chris Fernandez, EnsoData’s co-founder and chief research officer, emphasized the democratizing impact of their interoperable AI tools on accurately measuring sleep and diagnosing sleep disorders. By leveraging FDA-cleared pulse oximetry devices and sensors already widely in use, the technology becomes more accessible, improving patient outcomes and overall health. Fernandez highlighted the ubiquity of photoplethysmography (PPG) signals, which are commonly collected in healthcare settings and consumer wearables, underscoring the transformative potential of AI technology.
EnsoData suggested that its technology could bridge the gap between the estimated 30 million Americans with sleep apnea and the majority of cases that remain undiagnosed, potentially mitigating the risks of cardiovascular, neurodegenerative, and metabolic disorders exacerbated by untreated sleep apnea. EnsoData CEO Justin Mortara emphasized that the business might identify, diagnose, and treat sleep-disordered breathing events—including sleep apnea—more quickly if its capacity to collect and analyze PPG signals from wearable pulse oximeters has increased.
In summary, EnsoData’s FDA-cleared AI algorithm represents a significant advancement in sleep disorder diagnosis, offering a more accessible and cost-effective alternative to traditional sleep study equipment. By leveraging pulse oximetry technology and deep learning AI, the algorithm provides accurate assessments of sleep-related respiratory events, potentially improving patient outcomes and reducing the prevalence of undiagnosed sleep disorders.