Diagnosing the Severity of Knee Osteoarthritis Using Regression Scores From Artificial Intelligence Convolution Neural Networks

Abstract

Background: This study focused on using deep learning neural networks to classify the severity of osteoarthritis in the knee. A continuous regression score of osteoarthritis severity has yet to be explored using artificial intelligence machine learning, which could offer a more nuanced assessment of osteoarthritis. Materials and Methods: This study used 8260 radiographic images from The Osteoarthritis Initiative to develop and assess four neural network models (VGG16, EfficientNetV2 small, ResNet34, and DenseNet196). Each model generated a regressor score of the osteoarthritis severity based on Kellgren-Lawrence grading scale criteria. Primary performance outcomes assessed were area under the curve (AUC), accuracy, and mean absolute error (MAE) for each model. Secondary outcomes evaluated were precision, recall, and F-1 score. Results: The EfficientNet model architecture yielded the strongest AUC (0.83), accuracy (71%), and MAE (0.42) compared with VGG16 (AUC: 0.74; accuracy: 57%; MAE: 0.54), ResNet34 (AUC: 0.76; accuracy: 60%; MAE: 0.53), and DenseNet196 (AUC: 0.78; accuracy: 62%; MAE: 0.49). Conclusion: Convolutional neural networks offer an automated and accurate way to quickly assess and diagnose knee radiographs for osteoarthritis. The regression score models evaluated in this study demonstrated superior AUC, accuracy, and MAE compared with standard convolutional neural network models. The EfficientNet model exhibited the best overall performance, including the highest AUC (0.83) noted in the literature. The artificial intelligence-generated regressor exhibits a finer progression of knee osteoarthritis by quantifying severity of various hallmark features. Potential applications for this technology include its use as a screening tool in determining patient suitability for orthopedic referral. [ Orthopedics . 202x;4x(x):xx–xx.]