Clinical Evaluation of an AI-Based Prototype for Contactless Respiratory Monitoring in Children

URN zum Zitieren dieses Dokuments:: urn:nbn:de:bvb:355-epub-786659
DOI zum Zitieren dieses Dokuments:: 10.5283/epub.78665

Seebauer, Ludwig Maximilian ; Geis, Marcel ; Köhler, Niklas Alexander ; Nöh, Claudius ; Frey, Jochen ; Groß, Volker ; Sohrabi, Keywan ; Kerzel, Sebastian

Lizenz: Creative Commons Namensnennung 4.0 International
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Veröffentlichungsdatum dieses Volltextes: 11 Feb 2026 13:13

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Background: Pediatric respiratory disorders frequently necessitate clinical evaluation, often during sleep. Traditional polysomnography (PSG), while the gold standard for sleep-related respiratory assessment, is resource-intensive and can cause discomfort, particularly in children. Therefore, in a prior published study, we designed and technically validated a video-based prototype for contactless monitoring of respiratory movements. Objective: Our present study aimed to clinically validate the contactless monitoring prototype in pediatric patients, with a primary focus on detecting respiratory rate and identifying abnormal breathing patterns. Methods: Twenty-seven pediatric patients (aged 6 months to 12 years) were recruited from a pediatric sleep laboratory. To monitor thoracoabdominal movements in real time, the prototype employed a time-of-flight camera and a 3D imaging module, coupled with artificial-intelligence-based determination of the region of interest (ROI). Respiratory rates obtained from the prototype were compared to simultaneously recorded PSG data. Data were collected under various conditions, including different sleeping positions. A total of 296 h of respiratory data were acquired, of which selected 60 s segments (54 during N3 sleep and 27 during REM sleep) were analyzed using the prototype and compared with PSG-derived respiratory parameters. Conclusion: The contactless prototype demonstrates that reliable and non-invasive respiratory monitoring is feasible in pediatric patients. It enables accurate detection of respiratory rate as well as abnormal breathing patterns under routine clinical conditions, while reducing patient burden compared with conventional approaches. Its usability and minimal patient discomfort suggest potential for broader clinical adoption. Future work should focus on full-night recordings across all sleep stages and the development of automated data analysis pipelines to facilitate routine clinical implementation.

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