Prediction of Disease-Related Femur Shape Changes Using Geometric Encoding and Clinical Context on a Hip Disease CT Database

The accurate prediction of femur shape changes due to hip diseases is potentially useful for early diagnosis, treatment planning, and the assessment of disease progression. This study proposes a novel pipeline that leverages geometry encoding and context-awareness mechanisms to predict disease-related femur shape changes. Our method exploits the inherent geometric properties of femurs in CT scans to model and predict alterations in bone structure associated with various hip diseases, such as osteoarthritis (OA). We constructed a database of 367 CT scans from patients with hip OA, annotated using a previously developed bone segmentation model and an automated OA grading system. By combining geometry encoding and clinical context, our model achieves femur surface deformation prediction through implicit geometric and clinical insights, allowing for the detailed modeling of bone geometry variations due to disease progression. Our model demonstrated moderate accuracy in a cross-validation study, with a point-to-face distance (P2F) of 1.545 mm on the femoral head, aligning with other advanced predictive methods. This work marks a significant step toward personalized hip disease treatment, offering a valuable tool for clinicians and researchers and aiming to enhance patient care outcomes.