Zhang Jingjie, Zhang Jun, Chen Hongsuo, Tian Feilong, Zhang Yu, Zhou Yi, Jiang Zhenyu
Department of Gastroenterology, The Second Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, No. 30, Hudemulin Street, Qingshan District, Baotou, 014030, Inner Mongolia Autonomous Region, China.
Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
Surg Endosc. 2025 Jun 23. doi: 10.1007/s00464-025-11858-3.
Endoscopic ultrasound (EUS) is a vital tool for diagnosing biliopancreatic disease, offering detailed imaging to identify key abnormalities. Its interpretation demands expertise, which limits its accessibility for less trained practitioners. Thus, the creation of tools or systems to assist in interpreting EUS images is crucial for improving diagnostic accuracy and efficiency.
To develop an AI-assisted EUS system for accurate pancreatic and biliopancreatic duct segmentation, and evaluate its impact on endoscopists' ability to identify biliary-pancreatic diseases during segmentation and anatomical localization.
The EUS-AI system was designed to perform station positioning and anatomical structure segmentation. A total of 45,737 EUS images from 1852 patients were used for model training. Among them, 2881 images were for internal testing, and 2747 images from 208 patients were for external validation. Additionally, 340 images formed a man-machine competition test set. During the research process, various newer state-of-the-art (SOTA) deep learning algorithms were also compared.
In classification, in the station recognition task, compared to the ResNet-50 and YOLOv8-CLS algorithms, the Mean Teacher algorithm achieved the highest accuracy, with an average of 95.60% (92.07%-99.12%) in the internal test set and 92.72% (88.30%-97.15%) in the external test set. For segmentation, compared to the UNet ++ and YOLOv8 algorithms, the U-Net v2 algorithm was optimal. Ultimately, the EUS-AI system was constructed using the optimal models from two tasks, and a man-machine competition experiment was conducted. The results demonstrated that the performance of the EUS-AI system significantly outperformed that of mid-level endoscopists, both in terms of position recognition (p < 0.001) and pancreas and biliopancreatic duct segmentation tasks (p < 0.001, p = 0.004).
The EUS-AI system is expected to significantly shorten the learning curve for the pancreatic EUS examination and enhance procedural standardization.
内镜超声（EUS）是诊断胆胰疾病的重要工具，可提供详细成像以识别关键异常。其解读需要专业知识，这限制了经验较少的从业者使用该技术。因此，创建辅助解读EUS图像的工具或系统对于提高诊断准确性和效率至关重要。
开发一种人工智能辅助的EUS系统，用于准确分割胰腺和胆胰管，并评估其对内镜医师在分割和解剖定位过程中识别胆胰疾病能力的影响。
EUS-AI系统旨在进行体位定位和解剖结构分割。共使用来自1852例患者的45737张EUS图像进行模型训练。其中，2881张图像用于内部测试，来自208例患者的2747张图像用于外部验证。此外，340张图像构成人机竞赛测试集。在研究过程中，还比较了各种更新的先进（SOTA）深度学习算法。
在分类方面，在体位识别任务中，与ResNet-50和YOLOv8-CLS算法相比，均值教师算法准确率最高，内部测试集平均为95.60%（92.07%-99.12%），外部测试集为92.72%（88.30%-97.15%）。在分割方面，与UNet ++和YOLOv8算法相比，U-Net v2算法最佳。最终，使用两项任务的最优模型构建了EUS-AI系统，并进行了人机竞赛实验。结果表明，EUS-AI系统在位置识别（p < 0.001）以及胰腺和胆胰管分割任务（p < 0.001，p = 0.004）方面的表现均显著优于中级内镜医师。
EUS-AI系统有望显著缩短胰腺EUS检查的学习曲线并提高操作标准化程度。
