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IIT-G Creates AI-Driven Model for Predicting Knee Osteoarthritis Severity Using X-ray Images

Written by : Jayati Dubey

August 9, 2023

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Utilising machine learning algorithms, the AI model crafted by the IIT-G researchers assesses numerous X-ray images, effectively gauging and predicting the extent of knee osteoarthritis with high precision.

Indian Institute of Technology Guwahati (IIT-G) has developed a new artificial intelligence (AI) model that can predict the severity of knee osteoarthritis by analysing X-ray images. This breakthrough will help doctors diagnose and treat the disease more accurately and efficiently.

Knee osteoarthritis, a prevalent form of arthritis affecting millions across the globe, inflicts pain, inflammation, and stiffness upon the knee joint. This can subsequently hinder mobility and lead to a loss of functionality. Presently, doctors rely on X-ray images for diagnosing the condition and gauging its intensity. However, this method's subjective nature can lead to diagnostic errors.

The newly developed AI-based model by the IIT-G team leverages machine learning algorithms. Further, the model effectively scrutinises thousands of X-ray images to predict the severity of knee osteoarthritis. The model considers a range of factors, including bone density, joint space width, and bone spurs, to provide a precise diagnosis.

The study titled "AI-based Prediction of Knee Osteoarthritis Severity from X-ray Images" finds its place in the journal Medical Image Analysis. It marks a significant stride in the fusion of cutting-edge technology and medical science.

India is experiencing a surge of AI-based innovations within medical diagnostics. One notable example is the AI-powered diabetic retinopathy diagnosis system introduced by Aravind Eye Hospital. This system employs deep learning algorithms to analyse retinal images and accurately diagnose diabetic retinopathy.

Another example in this area includes AI-driven cardiac risk assessment system launched by Apollo Hospitals. This system employs machine learning algorithms to scrutinise patient data and predict the likelihood of developing cardiovascular disease. These advancements carry the potential to remarkably enhance the precision and efficiency of medical diagnostics across the nation.

The team's work continues further; they strive to further enhance the model by delving into a more extensive set of X-ray images and incorporating novel features. Their aspiration is that this research will ultimately lead to improved diagnostics and treatment strategies for knee osteoarthritis, thereby enhancing the quality of life for millions worldwide.


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