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. 2018 Oct 24;13(10):e0205320.
doi: 10.1371/journal.pone.0205320. eCollection 2018.

Accuracy and reliability of a low-cost, handheld 3D imaging system for child anthropometry

Affiliations

Accuracy and reliability of a low-cost, handheld 3D imaging system for child anthropometry

Joel Conkle et al. PLoS One. .

Abstract

The usefulness of anthropometry to define childhood malnutrition is undermined by poor measurement quality, which led to calls for new measurement approaches. We evaluated the ability of a 3D imaging system to correctly measure child stature (length or height), head circumference and arm circumference. In 2016-7 we recruited and measured children at 20 facilities in and around metro Atlanta, Georgia, USA; including at daycare, higher education, religious, and medical facilities. We selected recruitment sites to reflect a generally representative population of Atlanta and to oversample newborns and children under two years of age. Using convenience sampling, a total of 474 children 0-5 years of age who were apparently healthy and who were present at the time of data collection were included in the analysis. Two anthropometrists each took repeated manual measures and repeated 3D scans of each child. We evaluated the reliability and accuracy of 3D scan-derived measurements against manual measurements. The mean child age was 26 months, and 48% of children were female. Based on reported race and ethnicity, the sample was 42% Black, 28% White, 8% Asian, 21% multiple races, other or race not reported; and 16% Hispanic. Measurement reliability of repeated 3D scans was within 1 mm of manual measurement reliability for stature, head circumference and arm circumference. We found systematic bias when analyzing accuracy-on average 3D imaging overestimated stature and head circumference by 6 mm and 3 mm respectively, and underestimated arm circumference by 2 mm. The 3D imaging system used in this study is reliable, low-cost, portable, and can handle movement; making it ideal for use in routine nutritional assessment. However, additional research, particularly on accuracy, and further development of the scanning and processing software is needed before making policy and clinical practice recommendations on the routine use of 3D imaging for child anthropometry.

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Conflict of interest statement

Dr. Eugene Alexander is employed by BST, Inc. and has a patent pending related to the study subject matter: Determining Anthropometric Measurements of a Non-Stationary Subject. All other authors do not have affiliations with or financial involvement with any organization or entity with a financial interest in the subject matter or materials discussed in the manuscript. We were able to adhere to PLOS ONE policy on sharing data, but could not share the data acquisition software code due to commercial interests of BST, Inc.

Figures

Fig 1
Fig 1. 3D scan.
Scan of child over two years of age with anthropometrist kneeling in the background. Scan as it appears to anthropometrist during data collection and before processing.
Fig 2
Fig 2. Bland-Altman plot.
Length/height best-estimate manual measurement subtracted from single-scan measurement (y-axis) plotted against average based on both measurement types (x-axis) among children 0–59 months of age.
Fig 3
Fig 3. Intra- and inter-observer technical error of measurement (TEM).
Scan-derived (light bars) versus manual measurement (dark bars) intra-observer TEM (A) and inter-observer TEM (B) for stature, head circumference and arm circumference disaggregated by age group. Inter-observer TEM based on average of repeated measures and intra-observer TEM based on single measures.
Fig 4
Fig 4. Single measure intra- and inter-observer technical error of measurement (TEM).
Inter-observer TEM (dark bars) versus intra-observer TEM (light bars) for scan-derived (right) and manual measurements (left). Both inter- and intra-observer TEM based on single measures.

References

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