Knee Radiograph

Knee radiograph analysis is crucial for diagnosing and managing various knee conditions, but manual interpretation is time-consuming and prone to subjectivity. Current research focuses on applying deep learning, particularly convolutional neural networks (CNNs) and attention mechanisms, to automate tasks such as identifying the knee joint area, classifying abnormalities (like osteoarthritis and post-arthroplasty states), and even predicting disease progression. These automated methods aim to improve diagnostic accuracy, efficiency, and consistency, ultimately enhancing patient care and facilitating large-scale data analysis in knee radiology.