Artificial intelligence system for EUS navigation and anatomical landmark recognition.

Background and Aims: 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. Methods: 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. Results: 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. Conclusions: 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.