Projects

Prediction of risk of stroke using retinal images 

Currently there is no efficient. sensitive and specific screening methodology for the huge population of India for picking up the population at risk for coronary artery disease and stroke, which are a significant cause of mortality, morbidity and financial burden to family and the nation. An efficient, sensitive, specific screening method for picking up the people at risk for coronary artery disease and stroke will be a boon for the nation.

The development of an Artificial intelligence based algorithm to detect the patients at risk of coronary artery diseases and stroke from retinal images which is based on the idea of using the eyes as a window to the human body and the retina is the only place where a trained ophthalmologist can visualise the vessels and the nerve. Retinal Images provide am important factors about the health of cardiovascular system which includes the presence of risk factors like hypertension and atherosclerosis.

Imagology in ophthalmology is the non-invasive and cost effective model that can be easily performed in a primary healthcare settings, population based activities ,making it an affordable option for large-scale screening and triaging models.By analysing retinal images with AI algorithms, this will enable the healthcare professionals to provide accurately and efficiently identify the people who are at risk of developing cardiovascular conditions and benefit the community to do early diagnosis and treatment.

In India, cardiovascular disease is a leading cause of death and permanent disability. Early detection and the intervention can improve the outcomes also reduce the economic burden. Development of an AI based algorithm helps to detect individuals at risk of cardiovascular conditions and stroke by analysing retinal images. This would help in a population based model and show the public health  implications and save many lives. Gaps in the current methodology Currently, studies show that there are huge gap in screening patients at risk of cardiovascular conditions and stroke in India. Lack of awareness among the public about the importance of screening and prevention.

Picking up cardiovascular conditions through a cost effective method will improve the awareness. Limited access to multispeciality hospitals are another gap in the system. Lack of trained healthcare professionals , especially in rural settings where we can’t perform the screening methods very often. Fragmentation of healthcare services are another gap in Indian healthcare system. Through this model any primary care providers can screen and triage the people are at risk. India has a significant gap in the number of cardiologists and neurologists needed to screen and manage patients at risk of developing cardiovascular diseases like heart attack and stroke.

According to a study published in the Indian Journal of Public Health, there is only one cardiologist per 100,000 population and one neurologist per 250,000 population in India. This is significantly lower than the recommended ratio of one cardiologist and neurologist per 10,000 population in high-income countries. The shortage of cardiologists and neurologists in India creates a significant gap in the healthcare system’s ability to screen and manage patients at risk of developing cardiovascular diseases. This shortage makes it difficult for patients in rural and remote areas to access specialized care, and delays in diagnosis and treatment can lead to adverse health outcomes, including death.

Additionally, this shortage also results in a high workload for the limited number of specialists available, leading to burnout and decreased quality of care. Developing an AI/ML algorithm for screening and triaging patients at risk of developing heart attack and stroke from retinal images could be a potential solution to bridge this gap by providing a cost-effective and accessible screening tool to identify individuals at high risk of cardiovascular disease. This could help prioritize patients for further evaluation and treatment and reduce the workload on specialists, improving the overall quality of care for patients.