NutriScreener: AI to scale early malnutrition screening

Kari Jaaskelainen

21 Nov 2025 — 1 min read

NutriScreener is a new AI method to help screen for child malnutrition quickly and at scale—using ordinary photos captured from multiple poses.

How it works: it combines CLIP-based visual features, a retrieval “knowledge base” that surfaces context from similar cases, and a multi-pose graph attention network that reasons across views. This boosts robustness across populations and helps with class imbalance.

Clinician ratings: 4.3/5 for accuracy and 4.6/5 for efficiency, suggesting suitability for low-resource settings.
Performance: 0.79 recall, 0.82 AUC, and significantly lower errors when estimating anthropometric measures in unconstrained pediatric environments.
Generalization: trained/tested on 2,141 children (AnthroVision) and evaluated on ARAN plus a new CampusPose dataset. Using demographically matched knowledge bases yielded up to +25% recall and up to 3.5 cm RMSE reduction across datasets.

Why it matters: earlier, scalable screening can enable faster intervention where tools and skilled staff are scarce.

Paper: https://arxiv.org/abs/2511.16566v1

Paper: https://arxiv.org/abs/2511.16566v1

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AI Healthcare GlobalHealth ComputerVision Malnutrition LowResource PublicHealth MachineLearning

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