India Builds AI Tools to Predict Preterm Births Using Population-Specific Data
India's government-backed GARBH-INi programme has enrolled 12,000 pregnant women and generated over 1.6 million biospecimens and a million ultrasound images to train AI models for predicting preterm births, a leading cause of neonatal death in the country.
The dataset represents one of South Asia's largest pregnancy cohorts. Researchers are using it to develop AI models tailored to the Indian population rather than relying on algorithms trained on data from other regions.
What the Programme Has Produced
Early outputs include AI-based pregnancy dating models, microbiome-based predictors of preterm birth, rapid diagnostic tools, and genetic markers for earlier risk assessment. The programme also built a national biorepository and a data-sharing platform called GARBH-INi-DRISHTI to enable broader research access.
Dr Jitendra Singh, Science and Technology Minister of State, said India carries a significant share of the global burden of preterm births, making locally developed solutions necessary.
Moving Research Into Clinical Practice
Three partnerships have formalised the transition from research to clinical tools. Sundyota Numandis Probioceuticals received technology transfer rights for microbiome-based biotherapeutics. Doto Health and medical imaging AI company Qure.ai will develop AI-enabled ultrasound reporting and risk stratification systems.
The next phase focuses on deploying these models in real-world settings to improve maternal and child health outcomes.
Broader Government Strategy
India has invested in multiple medical datasets representing the local population over recent years. These include a national repository for life science data and a cancer multi-omics database, all aimed at accelerating AI development in healthcare that reflects India's genetic and demographic diversity.
Doto Health separately partnered with FHI 360 last year to expand digital health solutions across Southeast Asia, including work in Vietnam and Laos on maternal and infant mortality reduction.
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