Yuan Peng, Ma Zhong-Hua, Yan Yan, Li Shi-Jie, Wang Jing, Wu Qi
State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Endoscopy, Peking University Cancer Hospital & Institute, Beijing, 100142, People's Republic of China.
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Endoscopy, Peking University Cancer Hospital & Institute, Beijing, 100142, People's Republic of China.
Int J Gen Med. 2024 Dec 12;17:6127-6138. doi: 10.2147/IJGM.S481127. eCollection 2024.
A full examination of gastrointestinal tract is an essential prerequisite for effectively detecting gastrointestinal lesions. However, there is a lack of efficient tools to analyze and recognize gastric anatomy locations, preventing the complete portrayal of entire stomach. This study aimed to evaluate the effectiveness of artificial intelligence in identifying gastric anatomy sites by analyzing esophagogastroduodenoscopy images.
Using endoscopic images, we proposed a system called the Artificial Intelligence of Medicine (AIMED) through convolutional neural networks and MobileNetV3-large. The performance of artificial intelligence in the recognition of anatomic sites in esophagogastroduodenoscopy images was evaluated by considering many cases. Primary outcomes included diagnostic accuracy, sensitivity, and specificity.
A total of 160,308 images from 27 categories of the upper endoscopy anatomy classification were included in this retrospective research. As a test group, 16031 esophagogastroduodenoscopy images with 27 categories were used to evaluate AIMED's performance in identifying gastric anatomy sites. The convolutional neural network's accuracy, sensitivity, and specificity were determined to be 99.40%, 91.85%, and 99.69%, respectively.
The AIMED system achieved high accuracy with regard to recognizing gastric anatomy sites, and it could assist the operator in enhancing the quality control of the used endoscope. Moreover, it could contribute to a more standardized endoscopic performance. Overall, our findings prove that artificial-intelligence-based systems can be indispensable to the endoscopic revolution (Clinical trial registration number: NCT04384575 (12/05/2020)).
全面检查胃肠道是有效检测胃肠道病变的必要前提。然而，目前缺乏有效的工具来分析和识别胃的解剖位置，这阻碍了对整个胃部的完整描绘。本研究旨在通过分析食管胃十二指肠镜检查图像来评估人工智能在识别胃解剖部位方面的有效性。
利用内镜图像，我们通过卷积神经网络和MobileNetV3-large提出了一个名为医学人工智能（AIMED）的系统。通过考虑多个病例来评估人工智能在食管胃十二指肠镜检查图像中识别解剖部位的性能。主要结果包括诊断准确性、敏感性和特异性。
这项回顾性研究共纳入了来自27类上消化道内镜解剖分类的160308张图像。作为测试组，使用了16031张27类的食管胃十二指肠镜检查图像来评估AIMED在识别胃解剖部位方面的性能。卷积神经网络的准确性、敏感性和特异性分别确定为99.40%、91.85%和99.69%。
AIMED系统在识别胃解剖部位方面取得了很高的准确性，它可以帮助操作人员加强所用内镜的质量控制。此外，它有助于使内镜操作更加标准化。总体而言，我们的研究结果证明基于人工智能的系统对于内镜革命不可或缺（临床试验注册号：NCT04384575（2020年5月12日））。
