Objective To establish an automatic quality control system for wrist joint direct digital flat panel X-ray imaging system(DR)based on deep learning methods and conduct preliminary studies on quality control performance.Methods This study employed artificial intelligence deep learning techniques to develop a quality control system model for anteroposterior and lateral wrist joint DR images.A retrospective collection of 1 315 images from patients clinically suspected of having wrist joint lesions from Central Hospital Affilia-ted to Chongqing University was performed.The dataset was divided into a training set and a validation set at a ratio of 6∶4.Training was conducted on the MobileNet V2 classification model and the Global Universal U-Net(GU2Net)keypoint detection model,followed by evaluation using model accuracy,precision,recall rate,area under the curve(AUC),mean radial error(MRE),and successful detection rate(SDR).Results Experi-mental results on the validation dataset showed that the artefact classification model achieved high perform-ance in artefact recognition,with an AUC=0.970 1,95％confidence interval(95％CI)0.970 0-0.970 3,and its accuracy,precision and recall rate were 0.93,0.88 and 0.97,respectively.The MRE of the keypoint detec-tion model in anteroposterior and lateral images was also within a reasonable range,with MRE values of(0.794 4±3.253 5)mm and(3.813 4±7.408 7)mm,respectively.The SDRs of the forward and lateral key point detection models at a distance of 10.0 mm were 99.64％and 92.51％,respectively.Conclusion The fully automatic wrist joint DR quality control system model,developed based on deep convolutional neural net-works,can automatically generate image quality control reports for anteroposterior and lateral wrist joint ima-ges,with favourable results.
维普
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