Developed a non-invasive method for diagnosing chronic illnesses using fundus images with the latest state-of-the-art data analytics tools and artificial intelligence techniques funded by the Ministry of Electronics and Information Technology (MeitY). The project has three major modules:

• Development of Artificial Intelligence framework using data analysis techniques for detection of diseases through retinal images.

• State-of-art development of data acquisition framework for expert grading of retinal images.

• Development of web/mobile interface for predictive analytics and automated disease diagnosis.

The project has following major components: 

• State-of-art Research & Innovation study in the field of object detection and classification from stream data for the various fruits and crops detection.

• Real-time field data capturing from the required sensors using low-flying aerial drones, and in-field soil/ climatic parameters.

• Development of a correlated data analytics framework based upon the computation of environmental parameters for –

  • Detection of fruit/crops along with production estimate.

  • Development of Plant growth Model-based water management system.

• Development of web application/mobile application for real-time production estimate statistics, advisory to farmers for irrigation, and early detection of diseases.

I have worked as a CO-PI  in this project that aims to develop an AI speech processing framework that leverages acoustic biomarker feature extractors to identify cough signatures for Covid-19, Tuberculosis, and other pulmonary diseases using cough recordings that can provide real-time results. 

The solution will be delivered in the form of a low-cost AI-driven speech processing handheld device to enable mass "do it yourself" without intervention from overburdened health professionals.