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

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 proce...

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Autores principales: Leidman, Eva, Doocy, Shannon, Bollemeijer, Iris, Jatoi, Muhammad, Majer, Jennifer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
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Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9194222/
http://dx.doi.org/10.1093/cdn/nzac067.040
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author Leidman, Eva
Doocy, Shannon
Bollemeijer, Iris
Jatoi, Muhammad
Majer, Jennifer
author_facet Leidman, Eva
Doocy, Shannon
Bollemeijer, Iris
Jatoi, Muhammad
Majer, Jennifer
author_sort Leidman, Eva
collection PubMed
description 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.
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spelling pubmed-91942222022-06-14 Accuracy of Fully Automated 3D Imaging System for Child Anthropometry in a Low-Resource Setting: An Effectiveness Evaluation in South Sudan Leidman, Eva Doocy, Shannon Bollemeijer, Iris Jatoi, Muhammad Majer, Jennifer Curr Dev Nutr Nutritional Epidemiology 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. Oxford University Press 2022-06-14 /pmc/articles/PMC9194222/ http://dx.doi.org/10.1093/cdn/nzac067.040 Text en © The Author 2022. Published by Oxford University Press on behalf of The International Society for Human and Animal Mycology. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Nutritional Epidemiology
Leidman, Eva
Doocy, Shannon
Bollemeijer, Iris
Jatoi, Muhammad
Majer, Jennifer
Accuracy of Fully Automated 3D Imaging System for Child Anthropometry in a Low-Resource Setting: An Effectiveness Evaluation in South Sudan
title Accuracy of Fully Automated 3D Imaging System for Child Anthropometry in a Low-Resource Setting: An Effectiveness Evaluation in South Sudan
title_full Accuracy of Fully Automated 3D Imaging System for Child Anthropometry in a Low-Resource Setting: An Effectiveness Evaluation in South Sudan
title_fullStr Accuracy of Fully Automated 3D Imaging System for Child Anthropometry in a Low-Resource Setting: An Effectiveness Evaluation in South Sudan
title_full_unstemmed Accuracy of Fully Automated 3D Imaging System for Child Anthropometry in a Low-Resource Setting: An Effectiveness Evaluation in South Sudan
title_short Accuracy of Fully Automated 3D Imaging System for Child Anthropometry in a Low-Resource Setting: An Effectiveness Evaluation in South Sudan
title_sort accuracy of fully automated 3d imaging system for child anthropometry in a low-resource setting: an effectiveness evaluation in south sudan
topic Nutritional Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9194222/
http://dx.doi.org/10.1093/cdn/nzac067.040
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