Is the diagnostic model based on convolutional neural network superior to pediatric radiologists in the ultrasonic diagnosis of biliary atresia? - Duan, Xingxing; Yang, Liu; Zhu, Weihong; Yuan, Hongxia; Xu, Xiangfen; Wen, Huan; Liu, Wengang; Chen, Meiyan (Frontiers in Medicine, 2024)
Background: Many screening and diagnostic methods are currently available for biliary atresia (BA), but the early and accurate diagnosis of BA remains a challenge with existing methods. This study aimed to use deep learning algorithms to intelligently analyze the ultrasound image data, build a BA ultrasound intelligent diagnostic model based on convolutional neural network, and realize an intelligent diagnosis of BA. Methods: A total of 4887 gallbladder ultrasound images of infants with BA, non-BA hyperbilirubinemia, and healthy infants were collected. Two mask region convolutional neural network (Mask R-CNN) models based on different backbone feature extraction networks were constructed. The diagnostic performance between the two models was compared through good quality images at image level and patient level. The diagnostic performance between the two models was compared through poor quality images. The diagnostic performance of BA between the model and 4 pediatric radiologists was compared at image level and patient level. Results: The classification performance of BA of model 2 was slightly higher than that of model 1 in the test set, both at the image level and at the patient level, with significant difference of P = 0.0365 and P = 0.0459, respectively. The classification accuracy of model 2 was slightly higher than that of model 1 in poor quality images (88.3% vs 86.4%), and the difference was not statistically significant (P = 0.560). The diagnostic performance of model 2 was similar to that of the two radiology experts at the image level, and the differences were not statistically significant. The diagnostic performance of model 2 in the test set was higher than that of the two radiology experts at the patient level (all P < 0.05). Conclusion: The performance of the model 2 based on Mask R-CNN in the diagnosis of BA reached or even exceeded the level of pediatric radiology experts.