Computer Sciences and Information Technology
Title : | Design and Development of Anti-VEGF response prediction model of DME patients with FPGA-Accelerated Deep Learning tools |
Area of research : | Computer Sciences and Information Technology |
Focus area : | Digital technologies, Health |
Principal Investigator : | Ms. Tamilselvi S, Vellore Institute Of Technology (VIT) Chennai, Tamilnadu (600127) |
Timeline Start Year : | 2024 |
Timeline End Year : | 2028 |
Contact info : | tamilselvi.s@vit.ac.in |
Details
Executive Summary : | Diabetic Macular Edema is a major healthcare issue in rural areas, primarily due to factors like prolonged diabetes, reduced physical activity, impaired family function, and diabetes-related complications. The primary treatment is Anti-Vascular Endothelial Growth Factor therapy, but its effectiveness varies among patients. The complex, time-consuming, and costly process of screening is limited in developing countries like India. Deep learning, a technology that mimics the human brain structure, has been used to automate pattern recognition tasks and streamline the prediction process. This technology can predict the response to anti-Vascular Endothelial Growth Factor injections based on pre-treatment values, providing a promising avenue for improving management in resource-constrained settings. However, no study has implemented Deep Learning algorithms for eye screening applications on an Field Programmable Gate Array, which could revolutionize the efficiency and accessibility of these critical diagnostic procedures. |
Total Budget (INR): | 28,55,520 |
Organizations involved