(PO22-32-22) Accuracy of Fully Automated 3D Imaging System for Child Anthropometry in a Low-Resource Setting: An Effectiveness Evaluation in South Sudan

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Presenting Author(s)

Eva Leidman, MSPH
– Doctoral Candidate, Johns Hopkins Bloomberg School of Public Health, Atlanta, Georgia, United States

Co-Author(s)

MJ

Muhammad Jatoi
– International Medical Corps
IB

Iris Bollemeijer
– International Medical Corps
JM

Jennifer Majer
– International Medical Corps
SD

Shannon Doocy
– Johns Hopkins Bloomberg School of Public Health

Disclosure(s):

Eva Leidman, MSPH: No relevant financial relationship(s) with ineligible companies to disclose.

Objectives: To evaluate accuracy of child stature (height/length) and mid-upper arm circumference (MUAC) measurements produced by the AutoAnthro 3D imaging system developed by Body Surface Technology Inc following improvements to the software algorithm to improve accuracy and support automated processing, and hardware changes aimed to reduce cost.

Methods: A two-stage cluster survey in Malakal Protection of Civilians (PoC) in South Sudan between September 27 and October 2, 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 (CGS) 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 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.

Results: Manual measurements were obtained for 539 age 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 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.

Conclusions: Scan derived measurements were not of sufficient accuracy for widespread adoption. Differences in accuracy by team provide evidence that investments in training may be able to improve performance.

Funding Sources: USAID’s Bureau for Humanitarian Affairs and Grand Challenges Canada