Reported by PR Newswire / Nov 4, 2024.
[Technavio has announced its latest market research report titled Global REM Sleep Behavior Disorder Market 2024-2028]
NEW YORK, Nov. 4, 2024 /PRNewswire/ -- Report on how AI is redefining market landscape - The Global Rem sleep behavior disorder market size is estimated to grow by USD 438.6 million from 2024-2028, according to Technavio. The market is estimated to grow at a CAGR of 6.7% during the forecast period. Rising prevalence of neurological disorders is driving market growth, with a trend towards integration of AI and ML into rem sleep behavior disorder detection.
Key Market Trends Fueling Growth
The REM sleep behavior disorder market is experiencing notable progress with the adoption of artificial intelligence (AI) and machine learning (ML) technologies. These innovations are reshaping sleep disorder detection and management, presenting lucrative opportunities for enhanced patient outcomes. AI and ML models have transformed the analysis of extensive sleep-related datasets, enabling precise prediction and diagnosis of various sleep disorders, including insomnia and sleep apnea. These predictive models utilize demographic and physiological data, such as age, sex, and body mass index (BMI), to identify individuals at risk of developing sleep disorders. This early identification facilitates timely interventions, improving patient care and outcomes.
Furthermore, ML models effectively analyze electroencephalogram (EEG) signals to predict neurological diseases and sleep disorders. They are particularly proficient in determining the timing of phenoconversion in idiopathic REM sleep behavior disorder, offering crucial insights for early diagnosis and treatment.
Companies like HoneyNaps are leading the charge in REM sleep behavior disorder innovation. In September 2023, HoneyNaps secured FDA approval for its SOMNUM AI sleep disorder diagnosis software. This software streamlines the diagnostic process by automatically recognizing sleep stages, respiratory events, arousals, and leg movements using previously recorded physiological data from sleep studies.
Comments