revolutionizing osteoarthritis evaluation in conventional radiography with AI powered grading.

The study aims to improve the accuracy and

consistency of osteoarthritis (OA) diagnosis using an AI-based algorithm. Traditional

radiographic assessments are subjective and variable, often leading to delayed or

inappropriate treatments. This research will develop an AI model trained on a diverse,

annotated dataset of radiographic images to detect and grade OA features such as

joint space narrowing, osteophytes, subchondral sclerosis, and cysts. Employing a

stratified random sampling technique, the study ensures a representative dataset. The

AI’s performance will be validated against traditional assessments, aiming to achieve

high accuracy, sensitivity, and specificity. The expected outcome is a robust AI tool

that enhances diagnostic reliability, improves patient outcomes, and optimizes radiology

workflows.