Contrast‐enhanced harmonic endoscopic ultrasound (CH‐EUS) MASTER: A novel deep learning‐based system in pancreatic mass diagnosis

Abstract

AbstractBackground and AimsDistinguishing pancreatic cancer from nonneoplastic masses is critical and remains a clinical challenge. The study aims to construct a deep learning‐based artificial intelligence system to facilitate pancreatic mass diagnosis, and to guide EUS‐guided fine‐needle aspiration (EUS‐FNA) in real time.MethodsThis is a prospective study. The CH‐EUS MASTER system is composed of Model 1 (real‐time capture and segmentation) and Model 2 (benign and malignant identification). It was developed using deep convolutional neural networks and Random Forest algorithm. Patients with pancreatic masses undergoing CH‐EUS examinations followed by EUS‐FNA were recruited. All patients underwent CH‐EUS and were diagnosed both by endoscopists and CH‐EUS MASTER. After diagnosis, they were randomly assigned to undergo EUS‐FNA with or without CH‐EUS MASTER guidance.ResultsCompared with manual labeling by experts, the average overlap rate of Model 1 was 0.708. In the independent CH‐EUS video testing set, Model 2 generated an accuracy of 88.9% in identifying malignant tumors. In clinical trial, the accuracy, sensitivity, and specificity for diagnosing pancreatic masses by CH‐EUS MASTER were significantly better than that of endoscopists. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were respectively 93.8%, 90.9%, 100%, 100%, and 83.3% by CH‐EUS MASTER guided EUS‐FNA, and were not significantly different compared to the control group. CH‐EUS MASTER‐guided EUS‐FNA significantly improved the first‐pass diagnostic yield.ConclusionCH‐EUS MASTER is a promising artificial intelligence system diagnosing malignant and benign pancreatic masses and may guide FNA in real time.Trial registration number: NCT04607720.