Perspectivas actuales sobre el Síndrome de Apnea Obstructiva Del Sueño Revisión sistemática

Obstructive sleep apnea syndrome (OSAS) poses serious health risks, which is why its early detection is crucial for effective treatment. Objective: This work aims to analyze the potential of artificial intelligence (AI) in the detection of OSAS, specifically using polysomnography data. Method: For this purpose, a bibliographic review was carried out through an exhaustive search of the scientific literature related to OSAS and its diagnosis. Results: According to the studies analyzed, AI models accurately predict the risk of OSA. Machine learning methods show promise in reviewing snoring sounds and facial images for OSA diagnosis. Conclusion: AI-based technology improves the OSAS detection process through non-invasive and efficient methods. Incorporating AI into multiple diagnostic approaches provides a comprehensive strategy for early diagnosis of OSAS. However, further validation in various populations is still necessary.