Research Projects

The project focuses on developing a scalable, AI/ML-driven mobile/tablet-based hearing and cognitive screening system for the underserved and the rural population in India. The research has been driven by the high prevalence of unnoticed and undetected hearing loss and cognitive health issues in such populations. Despite government programs such as the National Program for the Prevention and Control of Deafness (NPPCD), Rashtriya Bal Swasthya Karyakram (RBSK), and the National Program for Health Care of the Elderly (NCHCE), access to screening and diagnostic services remains limited. This proposal aims to fill this knowledge gap by arming community health workers with a technology that combines semi-automated screening, teleconsultation, and data analysis for early detection and intervention. The research aims to design software with AI/ML-based algorithms designed in a way that they classify a person into categories of risk, which could be normal, at-risk, or require a referral. This will include in-built screening tools for auditory and cognitive assessments, teleconsultation modules for real-time collaboration with audiologists, and a health data analytics dashboard for decision-making. It also addresses the system's compatibility with low-cost hardware- its input is specifically designed to work with the system’s loudspeakers and microphones, which may be used by grassroots workers with minimal training. This integration will identify high-risk areas and accurately calibrate interventions according to prioritized needs, thus optimizing the usage of resources. We hypothesize that integrating AI-based diagnostics with community-level interventions will increase early identification and referral rates in underserved and rural populations, leading to better hearing and cognitive health outcomes. These primary experiments include 1. Developing AI/ML algorithms based on existing audiological datasets for hearing and cognitive screening. 2. Testing the system on various populations, comparing AI-generated results with conventional diagnostics. 3. Validation in the field using community health workers followed by large-scale deployment for real-world applicability and operational feasibility. 4. Analysis of teleconsultation impact and effectiveness of the AI-based screening system for detecting high-risk individuals. In addition, by incorporating AI/ML-driven diagnostics and teleconsultation, the project envisions a system that improves health outcomes in rural communities and creates scalable, low-cost solutions applicable to other underserved regions worldwide. It will correctly align with Indian health initiatives using digital health, contribute to a more robust and integrated healthcare ecosystem, and possibly provide a model for similar implementations in other settings characterized by resource constraints.