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Automated Size Recognition in Pediatric Emergencies Using Machine Learning and Augmented Reality: Within-Group
Michael Schmucker1, Martin Haag1
1GECKO Institute, Heilbronn University of Applied Sciences, Heilbronn, Germany.
Insights
This study developed an app using smartphone depth cameras to automatically measure children
Area of Science:
- Medical Technology
- Pediatric Emergency Medicine
- Computer Vision
Background:
- Pediatric emergencies are rare, leading to suboptimal outcomes due to physician inexperience.
- Anatomical variations and dosing errors in pediatric emergencies pose significant risks.
- Automated assistance for critical tasks like weight-based drug dose calculation is highly needed.
Purpose of the Study:
- To develop and evaluate an automated assistance service using smartphone depth camera technology.
- To assess if the developed service achieves measurement performance comparable to the current standard of care (emergency ruler).
- To minimize errors in pediatric emergency care through technological innovation.
Main Methods:
- Developed an AI-powered assistance service utilizing machine learning for patient recognition and size determination.
- Integrated a depth camera from smartphones for automated patient measurement.
- Conducted a within-group study comparing the app's measurements against a standard emergency ruler in 17 children.
Main Results:
- Statistical analysis (one-sample t test, P=.42) showed no significant difference in measurement accuracy between the app and the emergency ruler.
- The app demonstrated comparable measurement performance to the established emergency ruler under indoor, daylight conditions.
- The novel measurement method is technically not inferior to the current standard.
Conclusions:
- An augmented reality emergency ruler integrated into an assistance service is technically feasible.
- The study provides a foundation for further research, including usability testing.
- This technology holds promise for improving accuracy and safety in pediatric emergency medicine.
Background:
Pediatric emergencies involving children are rare events, and the experience of emergency physicians and the results of such emergencies are accordingly poor. Anatomical peculiarities and individual adjustments make treatment during pediatric emergency susceptible to error. Critical mistakes especially occur in the calculation of weight-based drug doses. Accordingly, the need for a ubiquitous assistance service that can, for example, automate dose calculation is high. However, few approaches exist due to the complexity of the problem.
Objective:
Technically, an assistance service is possible, among other approaches, with an app that uses a depth camera that is integrated in smartphones or head-mounted displays to provide a 3D understanding of the environment. The goal of this study was to automate this technology as much as possible to develop and statistically evaluate an assistance service that does not have significantly worse measurement performance than an emergency ruler (the state of the art).
Methods:
An assistance service was developed that uses machine learning to recognize patients and then automatically determines their size. Based on the size, the weight is automatically derived, and the dosages are calculated and presented to the physician. To evaluate the app, a small within-group design study was conducted with 17 children, who were each measured with the app installed on a smartphone with a built-in depth camera and a state-of-the-art emergency ruler.
Results:
According to the statistical results (one-sample t test; P=.42; α=.05), there is no significant difference between the measurement performance of the app and an emergency ruler under the test conditions (indoor, daylight). The newly developed measurement method is thus not technically inferior to the established one in terms of accuracy.
Conclusions:
An assistance service with an integrated augmented reality emergency ruler is technically possible, although some groundwork is still needed. The results of this study clear the way for further research, for example, usability testing.
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