深度学习识别痛风患者第一跖趾关节异常超声图像的方法初探

Objective To explore the feasibility of deep learning method in identifying abnormal ultrasound images of the first metatarsophalangeal joint (MTP1) in gout patients. Methods A total of 351 patients who underwent MTP1 ultrasound examination in the Affiliated Hospital of Qingdao University from February to October 2023 were prospectively enrolled. A total of 4 032 ultrasound images were collected, including 2 040 positive images and 1 992 negative images. All the images were divided into dorsal image set, medial image set and plantar image set according to the anatomic characters. All image sets were divided into training set and test set by 6∶4. ResNet-50 network was used in different training sets to establish deep learning models, including a single model and a multiple model. Efficiencies of different models were tested on each test set, ROC curves of the two methods for identifying abnormal MTP1 ultrasonic images were plotted. The area under the curve (AUC), accuracy, sensitivity and specificity were calculated, and the De-long test was used to compare the AUC. Results Two deep learning methods were successfully established, and both methods performed well on the whole test set. The AUC of the two methods in identifying abnormal MTP1 ultrasound images was 0.92 and 0.92, the accuracy was 85.95% and 86.57%, the sensitivity was 83.60% and 82.37%, and the specificity was 88.36% and 90.86%, respectively. There was no significant difference between the two methods (Z=-0.50, P=0.62). Compared with the multiple model, the AUC of single model was slightly lower in the dorsal and plantar test sets (0.95 vs 0.96; 0.83 vs 0.86), and slightly higher in the medial test set (AUC: 0.90 vs 0.89), but there were no significant differences (P>0.05). Conclusions Deep learning method can effectively distinguish abnormal MTP1 ultrasonic images from normal ones, and the tolerance for different anatomical views quite high. Multiple model is not necessary when the training sample size is large enough.