Avances en la detección temprana del síndrome de apnea obstructiva del sueño: aplicación integrativa de tecnologías de inteligencia artificial

Introduction: obstructive sleep apnea syndrome (OSAS) poses serious health risks, which is why its early detection is crucial for effective treatment. Objective: this paper aims to analyze the potential of artificial intelligence (AI) in the detection of OSAS, specifically using polysomnography data. Material and methods: to this end, a literature review was carried out through an exhaustive search of the scientific literature related to OSAS and its diagnosis. Results: according to the studies reviewed, AI models accurately predict the risk of OSAS. Machine learning methods show promise in analyzing snoring sounds and facial images for diagnosing OSAS. Conclusion: the incorporation of AI into multiple diagnostic approaches provides a comprehensive strategy for the early detection of OSAS. However, further validation in diverse populations is still needed.