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Clinical Evaluation of an AI-Based Prototype for Contactless Respiratory Monitoring in Children
Ludwig Maximilian Seebauer1, Marcel Geis2, Niklas Alexander Köhler2
1Department of Pediatric Pneumology and Allergy, University Children's Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, D-93049 Regensburg, Germany.
Insights
A new contactless video prototype accurately monitors pediatric respiratory rate and abnormal breathing patterns during sleep. This non-invasive method reduces patient burden compared to traditional polysomnography (PSG).
Area of Science:
- Biomedical Engineering
- Pediatric Sleep Medicine
- Respiratory Physiology
Background:
- Pediatric respiratory disorders require sleep evaluation.
- Traditional polysomnography (PSG) is resource-intensive and uncomfortable for children.
- A prior study developed and validated a contactless video prototype for respiratory monitoring.
Purpose of the Study:
- Clinically validate a contactless monitoring prototype in pediatric patients.
- Focus on accurate detection of respiratory rate.
- Identify abnormal pediatric breathing patterns during sleep.
Main Methods:
- Recruited 27 pediatric patients (6 months-12 years).
- Used a time-of-flight camera and 3D imaging with AI for contactless thoracoabdominal movement monitoring.
- Compared prototype-derived respiratory rates with simultaneous PSG data across various conditions and sleep stages.
Main Results:
- Acquired 296 hours of respiratory data.
- Analyzed 60-second segments during N3 and REM sleep.
- Demonstrated feasibility of contactless respiratory monitoring in children.
Conclusions:
- The contactless prototype enables reliable, non-invasive respiratory monitoring in pediatric patients.
- Accurately detects respiratory rate and abnormal breathing patterns under clinical conditions.
- Reduces patient burden and shows potential for clinical adoption.
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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