Li Xun, Yao Liwen, Wu Huiling, Tan Wei, Zhou Wei, Zhang Jun, Dong Zehua, Ding Xiangwu, Yu Honggang
Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.
Digestive Endoscopy Center, Wuhan Fourth Hospital, Wuhan, China.
Gastrointest Endosc. 2025 Jun;101(6):1166-1173.e11. doi: 10.1016/j.gie.2024.10.030. Epub 2024 Oct 19.
The integrity of image acquisition is critical for biliopancreatic EUS reporting, significantly affecting the quality of EUS examinations and disease-related decision-making. However, the quality of EUS reports varies among endoscopists. To address this issue, we developed a deep learning-based EUS automatic image report system (EUS-AIRS), aiming to achieve automatic photodocumentation in real-time during EUS, including capturing standard stations, lesions, and puncture procedures.
Eight deep learning models trained and tested using 235,784 images were integrated to construct the EUS-AIRS. The performance of EUS-AIRS was tested through man-machine comparisons at 2 levels: a retrospective test (include internal and external testing) and a prospective test. From May 2023 to October 2023, a total of 114 patients undergoing EUS at Renmin Hospital of Wuhan University were consecutively recruited for prospective testing. The primary outcome was the completeness of the EUS-AIRS for capturing standard stations.
In terms of completeness in capturing biliopancreatic standard stations, EUS-AIRS exceeded the capabilities of endoscopists at all levels of expertise in retrospective internal testing (90.8% [95% confidence interval (CI), 88.7%-92.9%] vs 70.5% [95% CI, 67.2%-73.8%]; P < .001) and external testing (91.4% [95% CI, 88.4%-94.4%] vs 68.2% [95% CI, 63.3%-73.2%]; P < .001). EUS-AIRS exhibited high accuracy and completeness in capturing standard station images. The completeness of the EUS-AIRS significantly outperformed manual endoscopist reports (91.4% [95% CI, 89.4%-93.4%] vs 78.1% [95% CI, 75.1%-81.0%); P < .001).
EUS-AIRS exhibits exceptional capabilities in real-time, capturing high-quality and high-integrity biliopancreatic EUS images. This showcases the potential of applying an artificial intelligence image report system in the EUS field.
图像采集的完整性对于胆胰内镜超声（EUS）报告至关重要，会显著影响EUS检查的质量以及与疾病相关的决策。然而，不同内镜医师的EUS报告质量存在差异。为解决这一问题，我们开发了一种基于深度学习的EUS自动图像报告系统（EUS - AIRS），旨在在EUS检查期间实时实现自动图像记录，包括捕捉标准部位、病变和穿刺操作。
整合使用235,784张图像训练和测试的8个深度学习模型，构建EUS - AIRS。通过两个层面的人机比较来测试EUS - AIRS的性能：回顾性测试（包括内部和外部测试）和前瞻性测试。2023年5月至2023年10月，连续招募了114例在武汉大学人民医院接受EUS检查的患者进行前瞻性测试。主要结局是EUS - AIRS捕捉标准部位的完整性。
在捕捉胆胰标准部位的完整性方面，EUS - AIRS在回顾性内部测试中超过了各级专业水平的内镜医师（90.8% [95%置信区间（CI），88.7% - 92.9%] 对 70.5% [95% CI，67.2% - 73.8%]；P <.001）以及外部测试（91.4% [95% CI，88.4% - 94.4%] 对 68.2% [95% CI，63.3% - 73.2%]；P <.001）。EUS - AIRS在捕捉标准部位图像方面表现出高准确性和完整性。EUS - AIRS的完整性显著优于内镜医师的手动报告（91.4% [95% CI，89.4% - 93.4%] 对 78.1% [95% CI，75.1% - 81.0%]；P <.001）。
EUS - AIRS在实时捕捉高质量和高完整性的胆胰EUS图像方面表现出卓越能力。这展示了在EUS领域应用人工智能图像报告系统的潜力。
