Revolutionizing Anaemia Detection: Leveraging Eye Condition Data and Machine Learning
Naresh Kumar Mangalapu, Lakshmi Prasanth Thangavelu, J. Gayathri, Gedela Kalyani, K S Balamurugan · 2025
Anemia, which is characterized by a low level of hemoglobin in the blood, can cause major damage to vital organs including the heart and kidneys. As per the records of 2021, 1.92 billion people are effected globally with anemia. Traditional diagnostic methods sometimes include invasive procedures, which makes patients anxious and delays treatment. This study offers a novel, non-invasive technique for identifying anemia by using pictures of the palpebral conjunctiva, which is known to exhibit pallor in anemic individuals. Our strategy is to offer a simple method for early identification of at-risk individuals by conjunctival paleness measurement, enabling timely interventions. Our novel approach ensures a rapid, simple, and accessible diagnosis of anemia for everyone by utilizing machine learning and integrating data on eye conditions. This technological breakthrough has the power to improve patient outcomes, enhance mental health globally, and alter the way healthcare is provided. We think that with more research and development, our approach will significantly impact the diagnosis of anemia and pave the way for better health outcomes everywhere.