Accepted for/Published in: JMIR Biomedical Engineering
Date Submitted: Jun 5, 2022
Date Accepted: Sep 12, 2022
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Accuracy of Fully Automated 3D Imaging System for Child Anthropometry in a Low-resource Setting: an Effectiveness Evaluation in South Sudan
ABSTRACT
Background:
AutoAnthro, developed by Body Surface Technology Inc (BST), aims to measure child anthropometry using a 3D imaging system.
Objective:
To evaluate accuracy of child stature (height/length) and mid-upper arm circumference (MUAC) measurements produced by AutoAnthro following updates to the software algorithm to improve accuracy and support automated processing, and hardware changes aimed to reduce cost.
Methods:
A study of device accuracy was embedded within a two-stage cluster survey in Malakal Protection of Civilians site in South Sudan conducted between September and October, 2021. All children aged 6-59 months within selected households were eligible. For each child, manual measurements were obtained by two anthropometrists following the protocol used for the 2006 WHO Child Growth Standards study. Scans were then captured by a different enumerator using a Samsung Galaxy 8 phone loaded with a custom software, AutoAnthro, and an Intel RealSense 3D scanner. Scans were processed using a fully automated algorithm. A multivariate logistic regression model was fit to evaluate adjusted odds of achieving a successful scan. Accuracy of measurements were visually assessed using Bland-Altman (BA) plots and quantified using average bias, technical error of measurement (TEM), limits of agreement (LoA), and the 95% precision interval for individual differences. Key informant interviews were remotely conducted with survey enumerators and BST developers to understand challenges in beta testing, training, data acquisition, and data transmission.
Results:
Manual measurements were obtained for 539 eligible children, from which scan-derived measurements were successfully processed for 234 (43.4%) of children. Caregivers for at least 56 children (10.4%) refused consent for scan capture; additional scans were unsuccessfully transmitted to the server. Neither demographic characteristics of the children (age and sex), stature, nor MUAC were associated with availability of scan derived measurements (p>0.05); team was significantly associated (p<0.001). The average bias of scan measurements in cm was -0.5 (95% confidence interval (CI): -2.0, 1.0) for stature and +0.7 (CI: 0.4, 1.0) for MUAC. For stature, 95% LoA was -23.9 to 22.9 cm. For MUAC, the 95% LoA was -4.0 to 5.4 cm. The TEM was 8.4 cm for stature and 1.8 cm for MUAC. All metrics of accuracy varied considerably by team. Covid-19 pandemic related physical distancing and travel policies limited testing to validate the device algorithm and prevented developers from conducting in-person training and field oversight, negatively impacting quality of scan capture, processing, and transmission.
Conclusions:
Scan derived measurements were not of sufficient accuracy for widespread adoption of the current technology. While the software shows promise, further investments in the software algorithms are needed to address issues with scan transmission, extreme field contexts, as well as to revise user interface to enable improved field supervision of scan capture. Differences in accuracy by team provide evidence that investments in training may also be able to improve performance.
Citation
