Accepted for/Published in: JMIR Formative Research
Date Submitted: Mar 2, 2021
Date Accepted: Jun 19, 2021
Automated Size Recognition in Pediatric Emergencies Using Machine Learning and Augmented Reality: A Comparative Study
ABSTRACT
Background:
Pediatric emergencies involving children are rare events, the experience of emergency physicians and the results are accordingly poor. Anatomical peculiarities and individual adjustments make a 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 the dose calculation is high. Technically, this is possible, among other approaches, with an application that uses a depth camera which is integrated in some smartphones or head-mounted displays, to provide a three-dimensional understanding of the environment.
Objective:
In the context of this paper, 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, the dosages are calculated and presented to the physician.
Methods:
To evaluate the application, a small within-group design study was conducted with 17 children, who were measured one after the other with the application installed on a smartphone and a state-of-the-art emergency ruler.
Results:
According to the statistics (one-sample t-test; P = .422; alpha = .05) there is no significant difference between the application and an emergency ruler under the test conditions (indoor, daylight).
Conclusions:
The newly developed measurement method is thus not technically inferior to the established one in terms of accuracy. This allows further research, for example usability testing.
Citation
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