Artificial intelligence-driven anthropometric assessment for young children: evaluating the accuracy and practicality of a digital image-based length and weight prediction tool.

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.