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Grand Challenges is a family of initiatives fostering innovation to solve key global health and development problems. Each initiative is an experiment in the use of challenges to focus innovation on making an impact. Individual challenges address some of the same problems, but from differing perspectives.


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Initiatives: Grand Challenges India
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Development of AI-Enabled Field Portable Microscope for Detection of Filariasis

Satish Kumar Dubey, Indian Institute of Technology Delhi (New Delhi, Delhi, India)
Sep 11, 2023

Project intends to capture the images of blood samples for diagnosis of Lymphatic Filariasis and applying customized machine learning algorithms for quantification and classification of Lymphatic Filariasis.

A Novel Total Nucleic Acid Based Reverse Transcription Loop-Mediated Isothermal Amplification Detection of Lymphatic Filariasis

Radha Krishan Shandil, Foundation for Neglected Disease Research (Bangalore, Karnataka, India)
Jun 27, 2023

Project intends to identify highly specific, and tandem repeats in the genomes of W. bancrofti, B. malayii, B. timori, and other human filarial agents, to develop a field-deployable, diagnostically robust molecular Point-of-Care test for Lymphatic Filariasis detection.

Exploring Risk Factors of Adverse Maternal and Child Health Outcomes Using Machine Learning and Other Advanced Data Analytical Approaches

Sourabh Paul, Department of Humanities and Social Sciences (New Delhi, Delhi, India)
Nov 27, 2020

Combining multiple data sets from HBGDKi using ML tools for prediction, classification and topic discovery may yield new insights for adverse birth outcomes and intermediate outcomes of interest. The study is based on a set of epidemiological, clinical and biochemical variables risk stratification algorithms for various adverse outcomes with practical applicability in health programme, and clinical settings may be feasible to develop using ML tools.ML can be used to suitably impute/bin missing values within datasets and merge variables from multiple datasets using robust data triangulation algorithms.

High Throughput Technology to Monitor Environmental Antibiotic Dispersion and Their Impact On AMR

Thirumurthy Velpandian, AIIMS Delhi (New Delhi, Delhi, India)
Nov 24, 2020

Environmental exposure to antibiotics is correlated well with AMR. The present study proposes the development of high throughput screening (HTS) technique of 125 antibiotics from environmental samples like water (from river, aquifers and food sources like egg and raw meat). A novel in-vitro method will be adopted to correlate AMR with the environmental levels of antibiotics found in Delhi-NCR region and derive safe levels of antibiotics that should be permissible in the environment.

Preterm Birth Risk in Pregnant Women – Prediction Using Machine Learning Models

Shinjini Bhatnagar, Translational Health Science and Technology Institute (Faridabad, Haryana, India)
Nov 16, 2020

The study proposes on pregnant women (Garbh-ini) cohort, a multidimensional longitudinal dataset purposely designed to study preterm birth. The study will apply data-driven machine learning approaches to develop an accurate and clinically useful model to predict the risk of preterm births. It will use multiple models for classification, with better objective functions and misclassification penalties that will aid in a higher rate of accurate predictions, and resampling of the data to avert biases arising from class imbalance. The primary deliverable will be dynamic prediction models that can predict, at different periods of gestation, the PTB risk using the clinical, epidemiological and imaging data.

Inali Arm – A Low-Cost Affordable Prosthetic Arm

Prashant Gade, Inali Foundation (Prashanth) (Khandwa, Madhya Pradesh, India)
Nov 10, 2020

The Inali Arm solves a problem that derails the lives of millions of people across the world. India alone is home to more than 22 million people with disabilities, with children making up nearly 8 million of that number. Due to cost and lack of accessibility, most of these people never get the help they need. The proposal intends to develop indigenous low-cost, affordable arm to for thousands of people who are living without any prosthetic care.

A Data Science Approach to Develop Growth Cutoffs for Graded Care of Malnutrition

Uma Chandra Mouli, St. John's Research Institute (Bangalore, Karnataka, India)
Nov 2, 2020

The study aims to calculate cut-offs using data provided by HBGDki and datasets with SAS, SJRI where weight, height, and age are available for children below five years in combination with other outcomes such as death, morbidity or hospitalization. Using WHO standards, weight for height, height for age and weight for age will be calculated, and these metrics will be used as determinants for risk of death, morbidity/hospitalization. A finer categorization of malnutrition based on the risk of mortality or significant morbidity can be used to develop and then deliver tailored optimized therapeutic options for what is essentially a far more eclectic group than what is captured by a three-category classification MAM, SAM and others.

Powdered Egg: Improving Egg Consumption in India

Srujith Lingala, Nutrition for Development Foundation (Kolkata, West Bengal, India)
Sep 28, 2020

Studies promoting egg consumption among women and children in lower middle-income countries (LMICs) show that growth indicators are significantly improved in children who consume eggs consistently. However, many states in India are chronically egg deficit, unable to fulfil their daily egg demand. The study is exploring the use of powdered-eggs in place of fresh eggs as they are cheaper and have a longer shelf life. Furthermore, the product is convenient to use, easy to transport, store and can be incorporated as an ingredient into Indian recipes and daily diets.

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View the Grand Challenges partnership network

The Bill & Melinda Gates Foundation is part of the Grand Challenges partnership network. Visit www.grandchallenges.org to view the map of awarded grants across this network and grant opportunities from partners.