在EUS中开发和验证基于SOTA的胆腺细分和站点识别系统的系统
Jingjie Zhang1, Jun Zhang2, Hongsuo Chen1
1Department 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.
Surgical endoscopy
|June 23, 2025
概括
一个人工智能辅助的内镜超声波 (EUS) 系统准确地细分胰腺和胆胰腺管道. 这种人工智能工具显著提高了内镜师的诊断能力,并缩短了EUS考试的学习曲线.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 胃肠病学 胃肠病学
背景情况:
- 内镜超声波 (EUS) 对于诊断胆胰腺疾病至关重要.
- 对于EUS图像解释,需要专业知识,限制了可访问性.
- 需要人工智能协助来提高EUS诊断的准确性和效率.
研究的目的:
- 开发一个人工智能辅助的EUS系统,用于精确的胰腺和胆胰腺管道细分.
- 评估人工智能系统对内镜医生的疾病识别和解剖学定位能力的影响.
主要方法:
- EUS-AI系统是为站位定位和解剖结构细分而开发的.
- 深度学习算法在一个大数据集 (45,737张图像) 上进行了训练和验证.
- 性能与最先进的算法和中级内镜师进行了比较.
主要成果:
- 平均教师算法实现了高精度 (95.60%内部,92.72%外部) 的站点识别.
- 对于细分任务,U-Net v2 是最优的.
- 在位置识别和管道细分方面,EUS-AI系统显著超过中级内镜师 (p < 0.001).
结论:
- 开发的EUS-AI系统在EUS图像分析方面表现出卓越的性能.
- 这种人工智能工具预计将缩短胰腺EUS考试的学习曲线.
- 该系统促进了EUS中程序标准化的加强.
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