ORIGINAL RESEARCH article
Front. Med.
Sec. Pulmonary Medicine
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1617530
This article is part of the Research TopicApplication of Multimodal Data and Artificial Intelligence in Pulmonary DiseasesView all 7 articles
Validation of the Somnolyzer 24×7 automatic scoring system in children with suspected Obstructive Sleep Apnea
Provisionally accepted
Ignacio Boira*
Violeta EstebanJosé Norberto Sancho-ChustEsther PastorPaula Fernández-MartínezAnastasiya Torba
Eusebi Chiner- San Juan de Alicante University Hospital, Alicante, Spain
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Introduction: Manual scoring of polysomnography data is a laborious and complex process. Automatic scoring by current computer algorithms shows high agreement with manual scoring. The primary objective of this study was to measure the overall validity of the Somnolyzer 24×7 automatic polysomnography scoring system in children.We conducted a single-center, prospective, observational study in children undergoing diagnostic polysomnography for suspected obstructive sleep apnea (OSA) from December 2023 to December 2024. We included children aged three to 15 years with suspected obstructive sleep apnea (OSA). Each polysomnogram was scored manually by three experts and automatically by the Somnolyzer 24×7 system.Results: Our analysis included 75 children (60% girls), of whom 9% did not have OSA, 20% had mild OSA, 31% moderate OSA, and 40% severe OSA. There was a high level of agreement between manual and automatic scoring of the respiratory disturbance index (RDI). The mean correlation (Pearson correlation coefficient) of RDI scored by the three experts was 0.93 (95% confidence interval [CI] 0.92 to 0.95), similar to the correlation between manual and automatic scoring (0.92, 95% CI 0.90 to 0.94). The correlation between the different manual scorings and between manual and automatic scoring was maintained in the different sleep stages (N1: 0.93 vs 0.90, N2: 0.76 vs 0.73, N3: 0.72 vs 0.76, REM: 0.86 vs 0.82).The Somnolyzer 24×7 automatic scoring system shows strong correlation with manual scoring in respiratory events and sleep architecture. Our results suggest this system could be used for polysomnography scoring in children.
Keywords: obstructive sleep apnea, Sleep Disorders, Children, Somnolyzer, Polysomnography, artificial intelligence
Received: 24 Apr 2025; Accepted: 03 Jun 2025.
Copyright: © 2025 Boira, Esteban, Sancho-Chust, Pastor, Fernández-Martínez, Torba and Chiner. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ignacio Boira, San Juan de Alicante University Hospital, Alicante, Spain
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