Innovación en sueño

Advances in sleep medicine have driven significant improvements in the diagnosis and treatment of sleep disorders such as obstructive sleep apnoea (OSA). This disorder affects one billion people worldwide, and traditionally diagnosis relies on polysomnography (PSG), a laborious method that requires specialist staff. However, the integration of artificial intelligence (AI) in sleep medicine has allowed the automatic analysis of sleep stages and respiratory events with high accuracy. Machine learning algorithms and neural networks have proven effective at automatically coding sleep, with success rates comparable to those of human experts. These advances allow for improved efficiency in sleep labs and personalised OSA treatment. In addition, techniques such as cluster analysis are used to identify symptomatic patterns and phenotypes, which improve understanding of the pathophysiology of OSA and optimise CPAP therapy. However, implementation of AI in hospitals faces technological, ethical and legal barriers. Challenges include data quality, patient privacy and need for specialist staff. Despite these obstacles, AI and Big Data have the potential to transform sleep disorders healthcare by improving both diagnosis and adherence to treatment, provided that regulatory and cultural barriers are overcome.