Accelerating progress toward Sustainable Development Goal 2 — ending global hunger and undernutrition — requires access to granular, low-cost, and near-real-time data on children’s nutritional status. However, current data collection efforts are often fragmented, infrequent, costly, and inaccessible, particularly during crises or in remote areas, limiting their effectiveness in driving timely intervention and targeted support.
The Artificial Intelligence for Monitoring Malnutrition (AIMM) project introduces a low-cost, household-operated tool for monitoring undernutrition using image-based AI for real-time nutrition classification, without the need for physical scales or measuring tapes.
Implemented in the Indian state of Maharashtra in collaboration with the Center for Artificial Intelligence at FLAME University in Pune, AIMM aims to refine deep learning models that estimate key anthropometric indicators, including weight, height, MUAC, and weight-for-height z-scores, from smartphone-based images. The project seeks to provide a low-cost, real-time alternative to traditional pen-and-paper surveys and existing AI tools, while enabling caregivers in rural and underserved areas to self-collect and monitor children’s nutritional status using a non-invasive, user-friendly mobile application.
By harnessing AI to democratise the collection of nutrition data, AIMM advances more effective and inclusive global health governance. It also offers a blueprint for integrating granular, near-real-time data into international early-warning systems, nutrition programming, food aid allocation, and Sustainable Development Goal monitoring.
More information is available at ai-mm.ch.
AIMM builds on previous SNSF- and FCDO-funded projects that resulted in the following publications and tools:
- Bhavnani, R. and N. S. Link. (2026). “D2A: a community-led smartphone tool for malnutrition screening in Kenya.” Frontiers in Public Health, 13:1695850.
- Bhavnani et al. (2023). “Trajectories of resilience to acute malnutrition in the Kenyan drylands.” Frontiers in Sustainable Food Systems, 7:1091346.
- Bhavnani et al. (2023). “Household behavior and vulnerability to acute malnutrition in Kenya.” Humanities and Social Sciences Communications, 10:63.
- MERIAM. (2021). “Simulating Acute Malnutrition Toolkit (SAMT).” Interactive online dashboard for the MERIAM project.
Timeline: January 2026 - December 2029.
Funding organisations:
