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Background <p>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.</p> Objective <p>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.</p> Methods <p>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.</p> Results <p>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 (<i>p</i> &lt; 0.001) and pancreas and biliopancreatic duct segmentation tasks (<i>p</i> &lt; 0.001, <i>p</i> = 0.004).</p> Conclusion <p>The EUS-AI system is expected to significantly shorten the&#xa0;learning curve for&#xa0;the pancreatic EUS&#xa0;examination and enhance&#xa0;procedural standardization.</p>

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Development and validation of a SOTA-based system for biliopancreatic segmentation and station recognition system in EUS

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月23日

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  • Jingjie Zhang,
  • Jun Zhang,
  • Hongsuo Chen,
  • Feilong Tian,
  • Yu Zhang,
  • Yi Zhou,
  • Zhenyu Jiang

摘要

Background

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.

Objective

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.

Methods

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.

Results

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).

Conclusion

The EUS-AI system is expected to significantly shorten the learning curve for the pancreatic EUS examination and enhance procedural standardization.