Understanding the Feasibility of Computer Vision in Diagnosing Respiratory Infections in Pediatric Emergency Rooms
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Objective
Respiratory infections are a leading cause of pediatric emergency visits globally, requiring timely and accurate assessment. This study evaluated the feasibility of a computer vision-based system to identify respiratory infections or distress and estimate vital signs, including respiratory rate (RR), heart rate (HR), and oxygen saturation (SpO2), in pediatric patients visiting an emergency department.
Methods
We conducted a population-based case-control study involving 100 children aged 0–5 years at the Children’s Emergency Department of Oulu University Hospital, Finland, between April and June 2022. Cases included children with respiratory symptoms (n=64), while controls had no respiratory symptoms (n=36). High-quality videos were recorded using a smartphone while the children were sitting on the lap of a parent, capturing the facial and precordium regions. Biosignals were extracted from the videos using remote photoplethysmography (rPPG) and remote ballistography (rBSG). From these 1D signals we calculated 250 features per window, including statistical, respiratory and heart-related features. These features were used to train machine learning models for classifying respiratory conditions and estimating vital signs. Ground truth data included manually measured SpO2, HR, RR, and body temperature, while nasopharyngeal samples were analyzed to determine infection etiology.
Results
The model achieved a classification accuracy of 86% in predicting SpO2 levels above 95 and 73% for levels below 95. It classified dyspnea with 63% of balanced accuracy and respiratory infections with 77% of balanced accuracy. For predicting hospitalization, the classification accuracy was 67%. Challenges were noted in video analysis due to patient movement and age-related variability, with better signal quality observed in sicker patients who remained still.
Conclusions
This study demonstrates the potential of computer vision systems to provide automated, non-intrusive assessments of respiratory conditions in acutely ill children. This could enable even remote assessment of acutely ill children at home. While the results show moderate accuracy, further research is needed to improve reliability across diverse clinical scenarios and patient populations.
