A deep learning-based linear system for liver station in EUS (with video)
Bing Xiao, Wei Tan, Liwen Yao, Xiao Tao, Chenxia Zhang, Jing Wang, Huiling Wu, Wen Wang, Qinxuan Zu, Xueying Wang, Jialing Li, Xun Li, Chaijie Luo, Ruiqing Jiang, Shuzhe Tan, Jiamin Wang, Mei Deng, Lianlian Wu, Jun Liu, Wei Zhou, Honggang YuBackground and Objectives:
With the widespread adoption of endoscopic techniques, the integration of EUS into hepatology has become an inevitable trend. This study aimed to develop a system that can conduct a comprehensive inspection of liver segments through scanning imaging at 8 stations.
Methods:
A standardized EUS examination protocol for the liver comprises 8 stations. We developed a deep learning-based station classification model using a dataset of 9403 EUS liver images. Internal validation was performed on 2780 EUS images and 26 EUS video clips. To further evaluate the model’s clinical utility, a human–machine competition involving 340 liver images was conducted. The same set of 26 video clips in internal video validation was also employed for a crossover study to evaluate the system’s effectiveness in reducing the difficulty of interpreting liver EUS images.
Results:
For station classification, the model achieved an accuracy of 92.52% on image validation and a per-frame accuracy of 85.98% on video validation. In the human–machine comparison, the overall classification accuracy reached 88.53%, which was comparable to the average accuracy of the EUS experts (84.56%,
Conclusions:
The system demonstrates high accuracy in liver station localization and achieves a classification performance comparable to EUS experts. It shows promise for facilitating standardized hepatic EUS examinations and may contribute to shortening the learning curve for trainees in the future.