PDF(Pubmed)
Abstract：
UNASSIGNED: Artificial intelligence (AI), when applied to computer vision using a convolutional neural network (CNN), is a promising tool in \"difficult-to-diagnose\" conditions such as malignant biliary strictures and cholangiocarcinoma (CCA). The aim of this systematic review is to summarize and review the available data on the diagnostic utility of endoscopic AI-based imaging for malignant biliary strictures and CCA.
UNASSIGNED: In this systematic review, PubMed, Scopus and Web of Science databases were reviewed for studies published from January 2000 to June 2022. Extracted data included type of endoscopic imaging modality, AI classifiers, and performance measures.
UNASSIGNED: The search yielded 5 studies involving 1465 patients. Of the 5 included studies, 4 (n=934; 3,775,819 images) used CNN in combination with cholangioscopy, while one study (n=531; 13,210 images) used CNN with endoscopic ultrasound (EUS). The average image processing speed of CNN with cholangioscopy was 7-15 msec per frame while that of CNN with EUS was 200-300 msec per frame. The highest performance metrics were observed with CNN-cholangioscopy (accuracy 94.9%, sensitivity 94.7%, and specificity 92.1%). CNN-EUS was associated with the greatest clinical performance application, providing station recognition and bile duct segmentation; thus reducing procedure length and providing real-time feedback to the endoscopist.
UNASSIGNED: Our results suggest that there is increasing evidence to support a role for AI in the diagnosis of malignant biliary strictures and CCA. CNN-based machine leaning of cholangioscopy images appears to be the most promising, while CNN-EUS has the best clinical performance application.
UNASSIGNED: In this systematic review, PubMed, Scopus and Web of Science databases were reviewed for studies published from January 2000 to June 2022. Extracted data included type of endoscopic imaging modality, AI classifiers, and performance measures.
UNASSIGNED: The search yielded 5 studies involving 1465 patients. Of the 5 included studies, 4 (n=934; 3,775,819 images) used CNN in combination with cholangioscopy, while one study (n=531; 13,210 images) used CNN with endoscopic ultrasound (EUS). The average image processing speed of CNN with cholangioscopy was 7-15 msec per frame while that of CNN with EUS was 200-300 msec per frame. The highest performance metrics were observed with CNN-cholangioscopy (accuracy 94.9%, sensitivity 94.7%, and specificity 92.1%). CNN-EUS was associated with the greatest clinical performance application, providing station recognition and bile duct segmentation; thus reducing procedure length and providing real-time feedback to the endoscopist.
UNASSIGNED: Our results suggest that there is increasing evidence to support a role for AI in the diagnosis of malignant biliary strictures and CCA. CNN-based machine leaning of cholangioscopy images appears to be the most promising, while CNN-EUS has the best clinical performance application.
摘要：
未经评估：人工智能（AI），当应用于使用卷积神经网络(CNN)的计算机视觉时，是“难以诊断”的条件，如恶性胆管狭窄和胆管癌（CCA）的一个有前途的工具。本系统综述的目的是总结和回顾有关基于内窥镜AI的成像对恶性胆道狭窄和CCA的诊断实用性的可用数据。
未经评估：在本系统综述中，PubMed,Scopus和WebofScience数据库对2000年1月至2022年6月发表的研究进行了审查。提取的数据包括内窥镜成像模式的类型，AI分类器，和绩效指标。
UNASSIGNED：搜索产生了5项研究，涉及1465名患者。在纳入的5项研究中，4（n=934；3,775,819张图像）将CNN与胆道镜检查结合使用，而一项研究（n=531;13,210图像）使用CNN和内窥镜超声（EUS）。具有胆管镜检查的CNN的平均图像处理速度为每帧7-15毫秒，而具有EUS的CNN的平均图像处理速度为每帧200-300毫秒。使用CNN-胆道镜检查观察到最高的性能指标(准确率94.9%，灵敏度94.7%，和特异性92.1%)。CNN-EUS与最大的临床表现应用相关，提供站识别和胆管分割;从而减少程序的长度，并提供实时反馈给内窥镜医师。
UNASSIGNED：我们的研究结果表明，越来越多的证据支持AI在恶性胆道狭窄和CCA诊断中的作用。基于CNN的胆道镜检查图像的机器倾斜似乎是最有前途的，而CNN-EUS具有最佳的临床性能应用。
未经评估：在本系统综述中，PubMed,Scopus和WebofScience数据库对2000年1月至2022年6月发表的研究进行了审查。提取的数据包括内窥镜成像模式的类型，AI分类器，和绩效指标。
UNASSIGNED：搜索产生了5项研究，涉及1465名患者。在纳入的5项研究中，4（n=934；3,775,819张图像）将CNN与胆道镜检查结合使用，而一项研究（n=531;13,210图像）使用CNN和内窥镜超声（EUS）。具有胆管镜检查的CNN的平均图像处理速度为每帧7-15毫秒，而具有EUS的CNN的平均图像处理速度为每帧200-300毫秒。使用CNN-胆道镜检查观察到最高的性能指标(准确率94.9%，灵敏度94.7%，和特异性92.1%)。CNN-EUS与最大的临床表现应用相关，提供站识别和胆管分割;从而减少程序的长度，并提供实时反馈给内窥镜医师。
UNASSIGNED：我们的研究结果表明，越来越多的证据支持AI在恶性胆道狭窄和CCA诊断中的作用。基于CNN的胆道镜检查图像的机器倾斜似乎是最有前途的，而CNN-EUS具有最佳的临床性能应用。
