A socio-technological approach for AI-augmented AMR stewardship and surveillance. AMRSense integrates diverse data sources including antibiotic consumption and sales data, to build a unified ecosystem that supports evidence-based decision-making. Won the joint second prize in the prestigious Trinity Challenge on AMR (2024) and featured as a case study in the World Economic Forum report on AMR in Asia.
An AI-powered tool for automated analysis of peripheral blood smear images, enabling differential diagnosis. This project leverages deep learning for cell detection and classification to assist pathologists and clinicians with faster, more accurate blood-based diagnostics.
A comprehensive computational gastronomy platform featuring multiple databases and AI tools for food, nutrition, and health research. Includes RecipeDB, FlavorDB, DietRx, SpiceRx, and the commercial API platform Foodoscope offering computational gastronomy data for recipes, flavors, nutrition, health, and sustainability.
An open big data and reproducible AI resource for early prediction of intensive care outcomes. Uses data from a large number of ICU stays of adult patients and deep learning models to generate useful clinical insights for prognostic decision-making in critical care and emergency settings.
An interpretable AI model that identifies changes in ECG signals of post-COVID patients compared to normal individuals. It provides explainability for both patient and population-level differences, aiding in understanding and managing cardiac complications in post-COVID individuals.
Making and validating indicators for prognostic outcomes in critical care and emergency settings. Using data from a large number of ICU stays of adult patients and deep learning models to generate useful clinical insights.
Design and development of a district-level early warning system for COVID-19 outbreak detection and its national implementation. This system enables timely identification of potential outbreaks at the district level across India.
A predictive and real-time spatio-temporal crisis response dashboard that enables rapid, data-driven responses to health emergencies and crises using advanced visualization and predictive analytics.
Developing affordable detection and prognosis methods using blood. Building cognitive computing-based methods to identify and use markers for detection of disorders using blood samples. Additionally, developing methods to characterize diseases like cancer to guide therapeutics.
A multi-omics study and development of a prognostic computational model for COVID-19 to correlate clinical outcomes and disease sequelae with the differential immunological response, mutational and vaccination status in India.
Development of a semi-supervised instance segmentation model for Multiple Myeloma blood cancer images, enabling more accurate and automated analysis of blood cancer samples with limited labeled data.
A novel diagnostic approach using histone acetylation-based immune-precipitation of nucleosome-bound cell-free DNA (cfDNA) in blood plasma for disease detection and characterization.
This study focuses on leveraging protein structure readouts to predict cancer drivers for precision medicine applications. By analyzing genetic mutations and their impact on protein structures, researchers aim to identify key drivers of cancer development for targeted therapies tailored to individual patients.
Development of an in-silico AI model capable of predicting the properties of new drug compounds, accelerating the drug discovery pipeline through computational approaches.
Building a reliable druggability prediction toolbox by integrating Quantum Mechanics (QM) and Machine Learning methods to assess the therapeutic potential of molecular targets.
Identification of therapeutically actionable mutations in ATP7B protein in Wilson's disease through computational approaches and cell culture systems, in collaboration with AIST, Tsukuba, Japan.
Two-pronged research: (1) Decoding neurodevelopmental pathways by unravelling the WDR:HCF-1 Nexus, and (2) Developing therapeutics for non-alcohol associated fatty liver/steatohepatitis (NAFL/NASH).
Developing interactive training and mentoring sessions for community health workers in India (ASHAs) using mobile phones and interactive voice response systems for better public healthcare in rural communities.
Strengthening managerial workflows with data-driven decision support in Sanjeevani Clinics in Madhya Pradesh, enabling better health service delivery through digital tools and analytics.
Developing a scalable, Ayushman Bharat Digital Mission (ABDM) compliant antimicrobial resistance tracker to monitor and combat AMR across healthcare facilities in India.
Investigating the role of digital interventions in supporting patients with Obsessive-Compulsive Disorder (OCD), exploring how technology can improve mental health outcomes.
Leveraging social media data and AI to sense public health trends, misinformation, and societal well-being indicators for evidence-based public health interventions.
Design and evaluation of a wearable arm band for measuring Heart Rate Variability (HRV) for organ function assessment. This device enables continuous, non-invasive monitoring of physiological parameters.
A national-level network project under the Dr. B.R. Ambedkar Centre for Biometric Research, focusing on advancing biometric technologies for healthcare and identity applications.
Grand Challenges India funded project addressing the intersection of climate change and health by tackling vector-borne diseases in Uttar Pradesh through data-driven approaches and predictive analytics.
Grand Challenges India funded project leveraging AI in healthcare to tackle antimicrobial resistance (AMR) in tertiary care settings at AIIMS and Max Hospitals, combining clinical data with advanced analytics.