DeepOA: Clinical Decision Support System for Early Detection and Severity Grading of Knee Osteoarthritis

Abstract

Knee Osteoarthritis (OA) is a medical condition affecting the knee joint that causes pain due to the cartilage wear-And-Tear. The severity of the impairment is graded by experienced radiologists as per standardized grading systems like the Kellgren-Lawrence(KL) grading scheme. Early detection and classification of knee OA in a patient before it increases in severity can significantly aid in corrective measures and benefit humankind. In this work, we propose a DL model to automatically segment the knee region and predict onset of Knee OA with X-ray scans. A comparative study using an ensemble model consisting of a YOLOv5 object detection algorithm for knee joint segmentation is also proposed. Various classification models such as VGG16, Resnet etc., are experimented with for the KL grade classification. The detailed experiments are conducted to understand the need for the region of interest segmentation step in KL grade classification. The proposed Clinical Decision Support System (CDSS) can help the medical practitioners perform preemptive screening based on X-ray scans for detecting onset earlier and for enabling required treatment. © 2021 IEEE.