NutriScreener: Retrieval-Augmented Multi-Pose Graph Attention Network for Malnourishment Screening

• URL: http://arxiv.org/abs/2511.16566v1
• Date: Thu, 20 Nov 2025 17:20:42 GMT
• Title: NutriScreener: Retrieval-Augmented Multi-Pose Graph Attention Network for Malnourishment Screening
• Authors: Misaal Khan, Mayank Vatsa, Kuldeep Singh, Richa Singh,
• Abstract summary: NutriScreener is a retrieval-augmented, multi-pose graph attention network.<n>It combines CLIP-based visual embeddings, class-boosted knowledge retrieval, and context awareness.<n>It achieves 0.79 recall, 0.82 AUC, and significantly lower anthropometric RMSEs.
• Score: 33.31396710382974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Child malnutrition remains a global crisis, yet existing screening methods are laborious and poorly scalable, hindering early intervention. In this work, we present NutriScreener, a retrieval-augmented, multi-pose graph attention network that combines CLIP-based visual embeddings, class-boosted knowledge retrieval, and context awareness to enable robust malnutrition detection and anthropometric prediction from children's images, simultaneously addressing generalizability and class imbalance. In a clinical study, doctors rated it 4.3/5 for accuracy and 4.6/5 for efficiency, confirming its deployment readiness in low-resource settings. Trained and tested on 2,141 children from AnthroVision and additionally evaluated on diverse cross-continent populations, including ARAN and an in-house collected CampusPose dataset, it achieves 0.79 recall, 0.82 AUC, and significantly lower anthropometric RMSEs, demonstrating reliable measurement in unconstrained pediatric settings. Cross-dataset results show up to 25% recall gain and up to 3.5 cm RMSE reduction using demographically matched knowledge bases. NutriScreener offers a scalable and accurate solution for early malnutrition detection in low-resource environments.

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