Comparison of the Performances of Deep Learning Methods in Kellgren-Lawrence Classification of Knee Osteoarthritis: A Multicenter Study.

Knee osteoarthritis (OA) causes symptoms such as pain, stiffness, and functional limitation in the knees; plain knee radiographs are used in addition to physical examination in its diagnosis. This study aimed to compare the performance of pre-trained deep learning models in knee OA classification and to examine the effects of lateral knee radiographs and patient clinical data on the performance of these models. Eight centers participated in the study; radiographs from six centers were used for training, validation, and testing of the models, and radiographs from two centers were used for external validation. In addition to anteroposterior (AP) and lateral knee radiographs, clinical information such as age, gender, height, and weight was collected from the patients. A total of 1550 patients were included in the study. The performance of deep learning models VGG-16, ResNet-50, DenseNet-121, Inception_v3, and EfficientNetb0 was compared in the classification of knee OA. The VGG-16 model yielded significantly higher performance metrics than competing architectures (p < 0.001), with an accuracy of 0.73, sensitivity of 0.72, specificity of 0.93, F1 score of 0.72, positive predictive value (PPV) of 0.72, quadratic weighted Kappa of 0.91, and AUC of 0.93. Feature extraction was performed using the VGG-16 model, and the patient's age, gender, and BMI clinical characteristics were added to the image feature set and classified using machine learning algorithms. The XGBoost classifier showed statistically insignificant improvements of 1-2% in performance metrics. Similar performance results were obtained with the VGG-16 model in a study using only AP knee radiographs. Radiographs from two centers were used for external validation, and the VGG-16 model yielded accuracy of 0.65, sensitivity of 0.65, specificity of 0.91, PPV of 0.67, F1 score of 0.65, quadratic weighted kappa of 0.88, and AUC of 0.90. Adding age, gender, and BMI clinical characteristics to the knee radiograph image feature set did not significantly improve model performance. Furthermore, adding lateral radiographs to the AP knee radiograph set did not provide any additional contribution to KL classification performance.