Automated Size Recognition in Pediatric Emergencies Using Machine Learning and Augmented Reality: Within-Group Comparative Study.
augmented reality
emergency medicine
machine learning
mobile applications
mobile phone
resuscitation
user-computer interface
Journal
JMIR formative research
ISSN: 2561-326X
Titre abrégé: JMIR Form Res
Pays: Canada
ID NLM: 101726394
Informations de publication
Date de publication:
20 Sep 2021
20 Sep 2021
Historique:
received:
02
03
2021
accepted:
19
06
2021
revised:
25
05
2021
entrez:
20
9
2021
pubmed:
21
9
2021
medline:
21
9
2021
Statut:
epublish
Résumé
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.
Sections du résumé
BACKGROUND
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
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
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
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
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.
Identifiants
pubmed: 34542416
pii: v5i9e28345
doi: 10.2196/28345
pmc: PMC8491115
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e28345Informations de copyright
©Michael Schmucker, Martin Haag. Originally published in JMIR Formative Research (https://formative.jmir.org), 20.09.2021.
Références
Lancet. 1986 Feb 8;1(8476):307-10
pubmed: 2868172
JMIR Mhealth Uhealth. 2020 Oct 1;8(10):e19070
pubmed: 32788142
J Clin Monit Comput. 2018 Jun;32(3):571-578
pubmed: 28660564
Stud Health Technol Inform. 2019;260:17-24
pubmed: 31118314
Prehosp Emerg Care. 2012 Jan-Mar;16(1):59-66
pubmed: 21999707
Emerg Med Clin North Am. 2020 Nov;38(4):819-839
pubmed: 32981620
Pediatr Crit Care Med. 2017 Feb;18(2):120-127
pubmed: 28165347
J Med Internet Res. 2017 May 29;19(5):e183
pubmed: 28554878
Circulation. 2020 Oct 20;142(16_suppl_2):S337-S357
pubmed: 33081530
Ann Emerg Med. 2016 Oct;68(4):441-451.e10
pubmed: 27105839
Sensors (Basel). 2020 Feb 14;20(4):
pubmed: 32074980
J Med Internet Res. 2017 Feb 01;19(2):e31
pubmed: 28148473
Anaesthesist. 2016 Feb;65(2):115-21
pubmed: 26696266
Resuscitation. 2015 Oct;95:223-48
pubmed: 26477414
BMJ Open. 2019 Nov 25;9(11):e032686
pubmed: 31772103
Ann Emerg Med. 2005 Dec;46(6):512-22
pubmed: 16308066
Dtsch Arztebl Int. 2012 Sep;109(38):609-16
pubmed: 23093991
J Med Internet Res. 2020 May 27;22(5):e17792
pubmed: 32292179
Anaesthesist. 2004 Nov;53(11):1086-92
pubmed: 15490081