Schmucker Michael, Haag Martin
GECKO Institute, Heilbronn University of Applied Sciences, Heilbronn, Germany.
JMIR Form Res. 2021 Sep 20;5(9):e28345. doi: 10.2196/28345.
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.
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).
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.
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.
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.
涉及儿童的儿科急诊是罕见事件，因此急诊医生的经验以及此类急诊的结果都很差。解剖学特点和个体差异使得儿科急诊治疗容易出错。严重错误尤其发生在基于体重的药物剂量计算中。因此，对一种无处不在的辅助服务（例如能够自动进行剂量计算）的需求很高。然而，由于问题的复杂性，很少有相关方法。
从技术上讲，一种辅助服务是可行的，除其他方法外，可通过一款应用程序实现，该应用程序使用集成在智能手机或头戴式显示器中的深度摄像头来提供对环境的三维理解。本研究的目标是尽可能自动化这项技术，以开发并统计评估一种辅助服务，其测量性能不比急诊标尺（现有技术水平）差很多。
开发了一种辅助服务，该服务使用机器学习来识别患者，然后自动确定其尺寸。根据尺寸自动得出体重，并计算剂量并呈现给医生。为了评估该应用程序，对17名儿童进行了一项小组内设计的小型研究，分别使用安装有内置深度摄像头的智能手机上的应用程序和最先进的急诊标尺对每名儿童进行测量。
根据统计结果（单样本t检验；P = 0.42；α = 0.05），在测试条件下（室内，日光），该应用程序和急诊标尺的测量性能之间没有显著差异。因此，就准确性而言，新开发的测量方法在技术上并不逊于现有方法。
集成增强现实急诊标尺的辅助服务在技术上是可行的，尽管仍需要一些基础工作。本研究结果为进一步研究（例如可用性测试）铺平了道路。
