An optimized framework for processing multicentric polysomnographic data incorporating expert human oversight - Holm, Benedikt; Jouan, Gabriel; Hardarson, Emil; Sigurðardottir, Sigríður; Hoelke, Kenan; Murphy, Conor; Arnardóttir, Erna Sif; Óskarsdóttir, María; Islind, Anna Sigríður (Frontiers in Neuroinformatics, 2024)
Polysomnographic recordings are essential for diagnosing many sleep disorders, yet their detailed analysis presents considerable challenges. With the rise of machine learning methodologies, researchers have created various algorithms to automatically score and extract clinically relevant features from polysomnography, but less research has been devoted to how exactly the algorithms should be incorporated into the workflow of sleep technologists. This paper presents a sophisticated data collection platform developed under the Sleep Revolution project, to harness polysomnographic data from multiple European centers. A tripartite platform is presented: a user-friendly web platform for uploading three-night polysomnographic recordings, a dedicated splitter that segments these into individual one-night recordings, and an advanced processor that enhances the one-night polysomnography with contemporary automatic scoring algorithms. The platform is evaluated using real-life data and human scorers, whereby scoring time, accuracy and trust are quantified. Additionally, the scorers were interviewed about their trust in the platform, along with the impact of its integration into their workflow. We found that incorporating AI into the workflow of sleep technologists both decreased the time to score by up to 65 minutes and increased the agreement between technologists by as much as 0.17 κ.