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Exploring the Use of a Length AI Algorithm to Estimate Children’s Length from Smartphone Images in a Real-World Setting: Algorithm Development and Usability Study

Exploring the Use of a Length AI Algorithm to Estimate Children’s Length from Smartphone Images in a Real-World Setting: Algorithm Development and Usability Study

Most of the existing image-based approaches focus on predicting the standing height of adults [15,16], although a couple of approaches for predicting the recumbent length of young children have been proposed [17,18]. Unlike manual measurements, these automated image-based approaches do not rely on standardized positioning of the child’s body but must overcome certain challenges to produce accurate predictions.

Mei Chien Chua, Matthew Hadimaja, Jill Wong, Sankha Subhra Mukherjee, Agathe Foussat, Daniel Chan, Umesh Nandal, Fabian Yap

JMIR Pediatr Parent 2024;7:e59564

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

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

The digit preference score was calculated for height and MUAC applying the MONICA procedure, which adjusts the chi-square statistic according to the size of the sample and the df of the test. Digit preference scores values of 0 indicate a uniform distribution, and the values increase with a greater imbalance [13]. Weight-for-height or weight-for-length z score (WHZ) and height-for-age or length-for-age z score (HAZ) were calculated using the WHO growth standard [14].

Eva Leidman, Muhammad Ali Jatoi, Iris Bollemeijer, Jennifer Majer, Shannon Doocy

JMIR Biomed Eng 2022;7(2):e40066

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