Deep learning-based automated detection and diagnosis of gouty arthritis in ultrasound images of the first metatarsophalangeal joint.
Authors
Affiliations (3)
Affiliations (3)
- Department of Ultrasound, the Affiliated Hospital of Qingdao University, Qingdao.
- The School of Electronic Information and Electrical Engineering, MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai.
- Department of Ultrasound, the Affiliated Hospital of Qingdao University, Qingdao. [email protected].
Abstract
This study aimed to develop a deep learning (DL) model for automatic detection and diagnosis of gouty arthritis (GA) in the first metatarsophalangeal joint (MTPJ) using ultrasound (US) images. A retrospective study included individuals who underwent first MTPJ ultrasonography between February and July 2023. A five-fold cross-validation method (training set = 4:1) was employed. A deep residual convolutional neural network (CNN) was trained, and Gradient-weighted Class Activation Mapping (Grad-CAM) was used for visualization. Different ResNet18 models with varying residual blocks (2, 3, 4, 6) were compared to select the optimal model for image classification. Diagnostic decisions were based on a threshold proportion of abnormal images, determined from the training set. A total of 2401 US images from 260 patients (149 gout, 111 control) were analyzed. The model with 3 residual blocks performed best, achieving an AUC of 0.904 (95% CI: 0.887~0.927). Visualization results aligned with radiologist opinions in 2000 images. The diagnostic model attained an accuracy of 91.1% (95% CI: 90.4%~91.8%) on the testing set, with a diagnostic threshold of 0.328. The DL model demonstrated excellent performance in automatically detecting and diagnosing GA in the first MTPJ.