MAHILA: Smartphone-Based AI Screening for Maternal Anemia Using Eye and Tongue Images

MAHILA: Smartphone-Based AI Screening for Maternal Anemia Using Eye and Tongue Images

India flag

India

Healthcare

Medium replicability and adaptation

Implementing Organisation

Khushi Baby

India, Rajasthan, Udaipur

Civil Society

Implementing Point of Contact

Saket Kumar

R&D Lead

Contributor of the Impact Story

Carnegie India

Year of implementation

2024

Problem statement

Affecting an estimated 12 million pregnant women annually in India, maternal anemia is a major contributor to adverse maternal and neonatal outcomes, yet screening at the last mile is constrained by invasive, consumable-dependent, or unreliable tools. Moderate anemia often goes undetected due to limited lab access and constraints with point-of-care devices, resulting in missed opportunities for early intervention and increased downstream health and system costs. MAHILA addresses this gap by enabling frontline workers to perform frequent, non-invasive screening using a smartphone camera, flagging higher-risk women for confirmatory testing and treatment while reducing unnecessary referrals.

Submission Overview

Khushi Baby (KB) is a non-profit organisation working as a systems enabler to public health departments in India. Established in 2016, KB’s team of 130 members includes expertise from public health, product design, engineering, field implementation, and data science. Co-created with 250,000 hours of community health workers' engagement, KB’s solutions have been used to track the health of over 50M beneficiaries across Rajasthan, Maharashtra, and Karnataka. Through enabling high quality data, timely insights, and effective actions, KB aims to close the loop for the public health system at the last mile.

AI Technology Used

Computer Vision

Key Outcomes

Accuracy & Quality Improvement

Access & Reach

Efficiency & Productivity

Resource Efficiency

Inclusion & Equity

Maternal anemia affects millions of pregnant women in India annually, yet screening at the last mile remains constrained by invasive or unreliable tools. MAHILA enables community health workers to screen for moderate anemia using smartphone images of the eye and tongue. In validation studies, the model demonstrates a high level of accuracy, achieved without blood draws or expensive equipment, making it possible to detect anemia earlier and intervene before outcomes worsen. This approach is most beneficial in environments that face a shortage of primary healthcare workers and lab facilities.

Impact Metrics

Diagnostic performance of MAHILA’s conjunctival smartphone vision model to detect moderate-or-worse anemia in pregnancy (Hb < 9 g/dL) — sensitivity, specificity, and AUC

Baseline Value

NA

Post-Implementation

NA

Academic Study

Implementation Context

Pilot

Indian states including Rajasthan, Maharashtra, Karnataka

The current study scale is over 3000 pregnant women. Target scale in phase 2 will be over 30000 participants.

Key Partnerships

State health departments of Rajasthan, Maharashtra, Karnataka, Kasturba Medical College (KMC), Mangalore, and Nandurbar District Administration

Replicability & Adaptation

Moderate
  1. Calibrate for local device types, lighting conditions, and population diversity, maintain ongoing bias monitoring across subgroups
  2. Ensure a clear pathway for confirmatory testing and treatment so that AI screening supports—rather than replaces—clinical decision-making

Supporting Materials

* The data presented is self-reported by the respective organisations. Readers should consult the original sources for further details.