The Sleep Revolution project: the concept and objectives

Area of Science:

• Sleep Medicine
• Artificial Intelligence
• Digital Health

Background:

• Obstructive sleep apnea (OSA) is a prevalent condition with significant health and economic impacts.
• Current diagnostic methods, like the apnea-hypopnea index, poorly correlate with OSA comorbidities and symptoms.
• Existing polysomnography analysis is labor-intensive, costly, and contributes to widespread underdiagnosis.

Purpose of the Study:

• To develop advanced machine learning tools for more accurate estimation of obstructive sleep apnea severity and phenotypes.
• To enable personalized treatment strategies for OSA patients, enhancing patient engagement.
• To reduce the cost and increase the accessibility of sleep studies by automating signal analysis.

Main Methods:

• Utilizing machine learning algorithms to analyze large datasets of sleep recordings.
• Developing a digital platform with a mobile application for patient engagement, including electronic sleep diaries, cognitive tests, and questionnaires.
• Leveraging extensive collaboration across 39 centers with expertise in sleep medicine, computer science, and industry.

Main Results:

• The project aims to improve the estimation of obstructive sleep apnea severity and identify distinct patient phenotypes.
• Expected outcomes include more personalized and effective treatment options for individuals with OSA.
• Anticipated reduction in manual labor for sleep study analysis, leading to decreased costs and increased availability.

Conclusions:

• The Sleep Revolution project seeks to overcome the limitations of current OSA diagnostics through innovative technology.
• The initiative has the potential to create new standardized guidelines for sleep medicine, improving patient care globally.
• By integrating machine learning and digital health tools, the project aims to revolutionize OSA diagnosis and management.