The Shishu Maapan Newborn Anthropometry AI tool developed by Wadhwani AI which gives anthropometric measurements of children up to 42 days of age by capturing short (15 seconds) video using basic smartphone. This innovative AI tool is being adopted and rolled out in partnership with the Union Territory of Dadra and Nagar Haveli and Daman and Diu government to enhance newborn care under the Home-Based Newborn Care (HBNC) program. This initiative empowers ASHA workers with an AI-enabled tool to accurately measure and monitor newborn anthropometry at the community level, enabling early detection of growth concerns and timely interventions while eliminating the need for conventional weighing methods (Salter/digital weighing scales).
Results Achieved and Impact Generated
Training & Adoption: By February 2025, all ASHA workers have been trained to use the Shishu Maapan AI tool.
Implementation: Over 455 ASHA workers and frontline health workers (FLWs) are actively using the tool, except for 5-10% of ASHAs collecting ground truth data alongside AI measurements.
Service Delivery: Since May 2024, a total of 11,500 HBNC visits have been conducted, registering and providing services to 3,700+ newborns using Shishu Maapan.
Population Impacted: The project has directly impacted 7,850 individuals, serving a total projected population of ~800,000 (as per the 2011 census projections for 2025).
By integrating digital health solutions into routine newborn care, this initiative is strengthening HBNC program, healthcare services, improving access, and ensuring better health outcomes for newborns in the region.

https://www.wadhwaniai.org/programs/newborn-anthropometry/

• Union Territory (UT) Government Dadara Nagar and Haveli, Diu & Daman (DNH DD), Government of India (GoI), India

• Women
• The poor
• Migrants
• Remote and rural communities
• New born
• Frontline Workers (FLWs), ASHA's, Medical Officers, Government stakeholders, Health system

The Shishu Maapan Newborn Anthropometry AI tool is designed to be highly scalable and replicable across different geographies in India and beyond, making it a transformative digital health solution for newborn care.
Key Factors Enabling Replication:
Ease of Use for Frontline Health Workers:
The AI tool is designed for ASHA workers and FLWs, requiring minimal training.
Eliminates the need for complex or expensive measuring devices like Infantometer, weighing scales.
Seamless Integration with Existing Health Systems:
Can be integrated into HBNC, ICDS, and other health programs.
Works in alignment with national and state-level health programs such as Home Based Newborn Care (HBNC) program, POSHAN Abhiyaan and RBSK etc.
Adaptability to Different Geographies:
The AI tool can function in both rural and urban settings where newborn anthropometry data collection is essential.
Supports multiple languages, making it applicable across different linguistic and cultural contexts.
Real-Time Data & Decision Support:
Provides real-time analytics and a dashboard for health administrators, allowing for data-driven policy decisions.
Can be integrated into state and national platforms like the RCH, Techo+, eKavach etc.
Proven Success and Expansion Potential:
The successful rollout in Dadra and Nagar Haveli and Daman and Diu demonstrates its scalability in other states of India, including high-burden neonatal morbidity regions.
The approach can be adapted for LMICs aiming reducing neonatal morbidity and malnutrition.
Potential for Global Adoption
The AI tool aligns with global health initiatives such as WHO’s Every Newborn Action Plan (ENAP) and UNICEF’s efforts on child growth monitoring.
Can support health programs in South Asia, Africa, and Southeast Asia, where newborn malnutrition and growth monitoring are major public health priorities.
By leveraging AI and digital health, the Shishu Maapan AI tool presents an affordable, scalable, and impactful solution for improving newborn care.

The Shishu Maapan Newborn Anthropometry AI tool is designed to be a sustainable, long-term solution for improving newborn care by integrating with existing healthcare systems, fostering local ownership, and ensuring cost-effectiveness.
Key Elements of Sustainability
1. Integration with Existing Health Systems
The AI tool integrates directly into existing programs like HBNC and ICDS, ensuring it becomes part of regular healthcare workflows. This minimizes the need for separate infrastructure, promoting sustainability.
2. Capacity Building for Local Ownership
Sustainability is achieved by building local capacity through training programs for ASHAs and FLWs. ASHAs play key role in ensuring the tool’s continued use and effectiveness.
3. Continuous Monitoring and Feedback
The AI tool enables real-time data collection, providing insights that help health administrators improve care.
4. Cost-Effectiveness
The tool is a low-cost alternative to traditional weighing devices, ensuring affordability for both local governments and communities, which is essential for long-term sustainability.
5. Government Support
The Union Territory government provides the necessary health infrastructure and human resources, ensuring the AI tool’s maintenance and scaling without relying on external funding.
6. Integration with National Health Platforms
By connecting to national health data platforms like RCH and Techo+, the tool ensures ongoing support and integration into broader health monitoring systems.
7. Expansion and Replication
The tool’s success in Union Territory DNH DD highlights its scalability. Its adaptability across regions ensures broader, long-term sustainability by expanding its reach to more areas.
Conclusion
The Shishu Maapan AI tool offers a sustainable and scalable solution to enhance newborn care, integrating into health systems, providing real-time data, and promoting local ownership. Its potential for replication ensures lasting impact in both India and globally.

The Shishu Maapan Newborn Anthropometry AI tool promotes WSIS values by leveraging digital health solutions to address maternal and child health challenges, fostering inclusivity, accessibility, and equity in the community. 1 Access to Information The tool democratizes access to accurate newborn anthropometry data, empowering ASHA workers with the ability to measure and monitor newborn growth in underserved areas, and facilitating informed healthcare decisions at the community level. 2 Building Inclusive Digital Solutions By providing an affordable and scalable AI solution, the project promotes digital inclusivity, ensuring that even economically disadvantaged communities can access high-quality newborn care without needing expensive equipment. 3 Strengthening Human Capacity The project trains ASHA workers and frontline health workers, building local capacity in digital health technologies and promoting skills development within the community. 4 Enhancing Data for Policy Support The AI tool provides real-time data collection, supporting evidence-based decision-making for public health policies and contributing to data-driven health strategies at the national level. 5 Promoting Health as a Human Right By integrating the tool into the Home-Based Newborn Care (HBNC) program, the project ensures timely newborn care and improved health outcomes, reinforcing health as a human right. 6 Support for SDGs Aligned with SDG 3: Good Health and Well-Being, this project focuses on reducing neonatal mortality and improving maternal and child health. By improving data collection and decision-making through AI-enabled solutions, it promotes sustainable healthcare delivery and equitable access to quality services for vulnerable populations. Conclusion The Shishu Maapan AI tool aligns with WSIS values by promoting inclusive, sustainable digital health solutions, improving health access, capacity building, and supporting data-driven policy to enhance community health outcomes.