%0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 9 %P e28345 %T Automated Size Recognition in Pediatric Emergencies Using Machine Learning and Augmented Reality: Within-Group Comparative Study %A Schmucker,Michael %A Haag,Martin %+ GECKO Institute, Heilbronn University of Applied Sciences, Max-Planck-Str 39, Heilbronn, 74074, Germany, 49 1757581477, michael.schmucker@hs-heilbronn.de %K resuscitation %K emergency medicine %K mobile applications %K mobile phone %K user-computer interface %K augmented reality %K machine learning %D 2021 %7 20.9.2021 %9 Original Paper %J JMIR Form Res %G English %X 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. %M 34542416 %R 10.2196/28345 %U https://formative.jmir.org/2021/9/e28345 %U https://doi.org/10.2196/28345 %U http://www.ncbi.nlm.nih.gov/pubmed/34542416