To access the session recording, navigate to the content tab and click the view video button. 

Session Schedule

Find a specific presentation in the course by navigating to the timestamp indicated below.

0:00:00
Introduction

0:01:20
Harnessing big data and artificial intelligence in sleep medicine: opportunities and challenges
Henri Korkalainen (Finland)

0:19:55
Subjective vs. objective sleep parameters - What do they tell us?
Erna Sif Arnardóttir (Iceland)

0:39:25
Probabilistic approach to analyze sleep structure – From scoring sleep to modelling sleep
Samu Kainulainen (Finland)

0:56:20
The future of sleep laboratories: Translating sleep revolution findings into clinical practice
Ludger Grote (Sweden)

1:14:00
Personalized treatment modalities – The role of physical activity and exercise in the management of SDB
Katrin Ýr Friðgeirsdóttir (Iceland)

Summary

Recent advances in technology and data analytics have revolutionized the diagnostic landscape for sleep-disordered breathing (SDB). Innovations such as ambulatory monitoring, machine learning-powered automated analysis, and digital tools for tracking symptoms and patient-reported outcome measures (PROMs) are paving the way for personalized diagnostic and treatment pathways. However, significant challenges remain before state-of-the-art research and cutting-edge technology can be translated into clinical practice.

This symposium will present key findings from the Sleep Revolution project, funded by the European Union Horizon 2020 program (Grant Agreement no. 965417), which concluded in September 2025. The project brought together 39 clinical and scientific partners from Europe and Australia, leveraging multidisciplinary expertise and vast databases. This collaborative effort has generated significant advancements with the potential to make a lasting impact on the field of sleep medicine. Attendees will gain insights into cutting-edge diagnostic technologies, including machine learning and digital tools that enhance diagnostic accuracy. The symposium will also address strategies to overcome barriers to clinical implementation, fostering patient-centered care and personalized treatment modalities.

This symposium aims to provide participants with a comprehensive understanding of the latest advances in diagnostic tools and technologies for SDB. The participants will be able to identify the mechanics, advantages, and potential limitations of novel diagnostic approaches, enabling them to evaluate and incorporate these tools into their clinical practice or research. In addition, this symposium will give participants the knowledge required to translate diagnostic findings into personalized patient care.

The symposium will feature five talks, each addressing critical aspects of advancing the diagnosis and management of SDB. Dr. Korkalainen explores the transformative role of machine learning and big data analytical approaches in enhancing the diagnosis and management of SDB. The talk will provide a balanced discussion on both the benefits and potential pitfalls of the novel approaches when moving towards more advanced, personalized approaches. Dr. Arnardottir will discuss the integration of subjective and objective data collection, highlighting the use of the Sleep Revolution mobile application for objective measurements and PROMs for subjective data. Dr. Kainulainen will then expand the discussion to probabilistic multi-source modeling of sleep, presenting insights from retrospective analyses of the ESADA database and its applications in sleep medicine. Dr. Grote will further underscore the transformative potential of the proposed approaches in revolutionizing the diagnosis and management of SDB, paving the way for more accurate, efficient, and personalized care. Finally, Friðgeirsdóttir will focus on the importance of precise SDB diagnosis and characterization, highlighting the role of lifestyle interventions and physical activity in delivering personalized treatment modalities for improved patient outcomes.

Learning Objectives:

Upon completion of this CME activity, participants will be able to:
• Gain an understanding of the transformative potential of big data and AI to revolutionize sleep medicine, including their role in advancing diagnostics and personalized care. They will also learn to identify the limitations and assess the risks associated with integrating these technologies into clinical practice
• Explore the key differences between subjective and objective sleep parameters, understand how each type of data contributes to sleep diagnosis, and assess the strengths and limitations of both approaches. They will gain insights into how combining these parameters can enhance clinical decision-making and patient care in sleep medicine
• Learn how to apply sleep modeling to phenotype OSA patients, interpret and utilize hypnodensities, and understand how they outperform traditional hypnograms. They will also explore techniques for creating hypnodensity maps to detect abnormalities and gain insights into novel hypnodensity parameters and their connection to clinical outcomes
• Gain an understanding of how findings from the Sleep Revolution project can be applied to improve sleep laboratory practices. They will learn strategies for integrating innovative diagnostic tools and technologies into daily clinical workflows, and explore how these advancements can enhance patient care and treatment outcomes
• Distinguish between physical activity and exercise, understand how an active lifestyle benefits patients with SDB, and recognize the importance of exercise in managing SDB. Additionally, they will explore the role of lifestyle changes, including exercise, in optimizing SDB management and patient outcomes