Rizzatti Gianenrico, Tripodi Giulia, De Lucia Sara Sofia, Pellegrino Antonio, Boskoski Ivo, Larghi Alberto, Spada Cristiano
Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
VideoGIE. 2025 Mar 22;10(7):358-363. doi: 10.1016/j.vgie.2025.03.027. eCollection 2025 Jul.
The use of artificial intelligence (AI) has been introduced in several medical fields with promising results, including endoscopy. In the field of EUS, studies using AI are still limited and have mostly focused on the identification and characterization of pancreatic masses. Recently, AI systems based on deep learning have been developed to identify anatomical landmarks during diagnostic EUS.
The Endoangel system (Wuhan ENDOANGEL Medical Technology, Wuhan, China), built using deep convolutional neural networks (DCNNs), is able to provide navigation hints and identify anatomical landmarks in real time during diagnostic EUS. The system was trained with more than 550 EUS procedures and uses a DCNN that processes images through multiple layers by extracting features, introducing nonlinearity, reducing complexity, and making predictions via fully connected layers.
The AI EUS system was tested in 3 patients undergoing diagnostic EUS. In each case, the correct recognition of anatomical landmarks by the AI EUS system was judged by a single expert performing the EUS examination. The system did not recognize pathologic alterations such as pancreatic masses or cystic lesions.
The AI EUS DCNN-based system is able to correctly identify EUS anatomical landmarks. In the near future, this system might play an important role in EUS training and quality control. In addition, many other features might progressively be added, with the next ideal step being the identification of pathologic alterations.
人工智能（AI）已被引入多个医学领域并取得了令人鼓舞的成果，包括内镜检查。在超声内镜（EUS）领域，使用AI的研究仍然有限，且大多集中于胰腺肿块的识别与特征描述。最近，基于深度学习的AI系统已被开发用于在诊断性EUS过程中识别解剖标志。
Endoangel系统（武汉安翰医疗技术有限公司，中国武汉）采用深度卷积神经网络（DCNN）构建，能够在诊断性EUS过程中实时提供导航提示并识别解剖标志。该系统使用超过550例EUS检查进行训练，并使用一个通过提取特征、引入非线性、降低复杂度以及经由全连接层进行预测来多层处理图像的DCNN。
AI EUS系统在3例接受诊断性EUS的患者中进行了测试。在每种情况下，由进行EUS检查的一位专家判断AI EUS系统对解剖标志的正确识别情况。该系统未识别出诸如胰腺肿块或囊性病变等病理改变。
基于AI EUS DCNN的系统能够正确识别EUS解剖标志。在不久的将来，该系统可能在EUS培训和质量控制中发挥重要作用。此外，可能会逐步添加许多其他功能，下一个理想步骤是识别病理改变。
