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Updated: Jan 9, 2026
Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
Artificial intelligence-driven anthropometric assessment for young children: evaluating the accuracy and practicality
Daniel Chan1,2, Mei Chien Chua1,3, Matthew Hadimaja4
1Duke-NUS Medical School, Singapore.
Insights
A new artificial intelligence (AI) tool accurately predicts infant length and weight from smartphone images. This AI-powered growth monitoring offers a convenient alternative for parents and clinicians.
Area of Science:
- Pediatric Health Technology
- Artificial Intelligence in Medicine
- Child Growth Monitoring
Background:
- Early childhood growth monitoring is crucial for identifying potential health and nutritional issues.
- Growth faltering requires prompt intervention to ensure optimal child development.
- Smartphone-based tools offer a novel approach to accessible health monitoring.
Purpose of the Study:
- To evaluate the performance of a length-weight artificial intelligence (LWAI) tool.
- To assess the LWAI tool's accuracy in predicting children's length and weight using smartphone images.
- To determine the usability and potential impact of the LWAI tool on routine growth monitoring.
Main Methods:
- Observational study involving children aged 0-18 months.
- Comparison of LWAI predictions (from smartphone images) against clinical measurements (length and weight).
- Assessment of user experience via parent questionnaires.
Main Results:
- LWAI successfully generated predictions for 98% of images.
- Mean absolute errors for length and weight predictions were within acceptable clinical ranges.
- 97% of parents expressed willingness to use a digital tool for regular growth tracking.
Conclusions:
- The LWAI tool demonstrates efficacy in predicting length and weight for children up to 18 months.
- This AI-powered solution provides a practical and convenient method for growth monitoring.
- The tool has potential applications in both home and clinical settings for enhanced pediatric care.
Background:
Monitoring early childhood growth is vital, as growth faltering could indicate nutritional or health issues requiring prompt intervention. Our study's aim was to assess the performance of a length-weight artificial intelligence (LWAI) tool for predicting children's length and weight from smartphone images.
Methods:
This observational, single-centre study recruited children aged 0-18 months. Investigators measured length and weight in clinic using WHO standard recommendations and captured six images per child in a supine position, while parents took six similar images at home. Within each image, LWAI identifies specific body landmarks and a reference object, then extracts and uses image features to predict the child's length and weight. The LWAI's performance was assessed by comparing length/weight prediction versus actual measurements. User experience was collected through questionnaires.
Results:
A total of 215 participants (mean age 6.1 months) were included, and length/weight predictions were generated for 98% (2184/2224) of the images. The mean absolute error (MAE) and mean absolute percentage error (MAPE) for length were 2.47 cm (4.04%) for individual images and 1.89 cm (3.18%) for grouped images (participants with ≥9 images). The corresponding MAE/MAPE for weight were 0.69 kg (11.68%) and 0.56 kg (9.02%), respectively. Regarding usability, 97% of parents who reported not routinely measuring their child's growth indicated that they would start doing so regularly if a digital tool was available to them.
Conclusions:
The LWAI tool can predict length and weight in children ≤18 months, offering a practical, convenient, artificial intelligence-powered alternative for growth monitoring in home and clinical settings.
Trial Registration Number:
NCT05079776.
