Abstract. Objectives. Knee alignment affects both the development and surgical treatment of knee osteoarthritis. Automating femorotibial angle (FTA) and hip-knee-ankle angle (HKA) measurement from radiographs could improve reliability and save time. Further, if the gold-standard HKA from full-limb radiographs could be accurately predicted from knee-only radiographs then the need for more expensive equipment and radiation exposure could be reduced. The aim of this research is to assess if deep learning methods can predict FTA and HKA angle from posteroanterior (PA) knee radiographs. Methods. Convolutional neural networks with densely connected final layers were trained to analyse PA knee radiographs from the Osteoarthritis Initiative (OAI) database with corresponding angle measurements. The FTA dataset with 6149 radiographs and HKA dataset with 2351 radiographs were split into training, validation and test datasets in a 70:15:15 ratio. Separate models were learnt for the prediction of FTA and HKA, which were trained using mean squared error as a loss function. Heat maps were used to identify the anatomical features within each image that most contributed to the predicted angles. Results. FTA could be predicted with errors less than 3° for 99.8% of images, and less than 1° for 89.5%. HKA prediction was less accurate than FTA but still high: 95.7% within 3°, and 68.0 % within 1°. Heat maps for both models were generally concentrated on the
Preservation of both anterior and posterior cruciate ligaments in total knee arthroplasty (TKA) can lead to near-normal post-operative joint mechanics and improved knee function. We hypothesised that a patient-specific bicruciate-retaining prosthesis preserves near-normal kinematics better than standard off-the-shelf posterior cruciate-retaining and bicruciate-retaining prostheses in TKA. We developed the validated models to evaluate the post-operative kinematics in patient-specific bicruciate-retaining, standard off-the-shelf bicruciate-retaining and posterior cruciate-retaining TKA under gait and deep knee bend loading conditions using numerical simulation.Objectives
Methods