Deep learning-based assessment of knee septic arthritis using transformer features in sonographic modalities.

PURPOSE

Septic arthritis is an infectious disease. Conventionally, the diagnosis of septic arthritis can only be based on the identification of causal pathogens taken from synovial fluid, synovium or blood samples. However, the cultures require several days for the isolation of pathogens. A rapid assessment performed through computer-aided diagnosis (CAD) would bring timely treatment.

METHODS

A total of 214 non-septic arthritis and 64 septic arthritis images generated by gray-scale (GS) and Power Doppler (PD) ultrasound modalities were collected for the experiment. A deep learning-based vision transformer (ViT) with pre-trained parameters were used for image feature extraction. The extracted features were then combined in machine learning classifiers with ten-fold cross validation in order to evaluate the abilities of septic arthritis classification.

RESULTS

Using a support vector machine, GS and PD features can achieve an accuracy rate of 86% and 91%, with the area under the receiver operating characteristic curves (AUCs) being 0.90 and 0.92, respectively. The best accuracy (92%) and best AUC (0.92) was obtained by combining both feature sets.

CONCLUSIONS

This is the first CAD system based on a deep learning approach for the diagnosis of septic arthritis as seen on knee ultrasound images. Using pre-trained ViT, both the accuracy and computation costs improved more than they had through convolutional neural networks. Additionally, automatically combining GS and PD generates a higher accuracy to better assist the physician's observations, thus providing a timely evaluation of septic arthritis.