In the early stage, the investigators successfully constructed an artificial intelligence model-based ultrasonic endoscopy-assisted film reading system and named the modified system biliopancreatic Master. The system can realize real-time ultrasonic station recognition and anatomical mark recognition and provide doctors with corresponding operation techniques. This study aimed to verify the feasibility and effectiveness of the biliopancreatic master system developed by our project team in shortening the training period of ultrasound endoscopists through a single-center clinical study.

Endoscopic ultrasonography is essential for diagnosing and treating biliary and pancreatic diseases. The total incidence of biliary and pancreatic diseases is as high as 121/100,000, causing about 14.3/100,000 deaths worldwide every year, posing a severe threat to the health and safety of all humanity. The biliopancreatic system is located deep in the abdominal cavity, which is relatively difficult to examine and lacks specific symptoms at the early stage of the disease. Hence, the prognosis is poor, and the five-year survival rate is low. Biliopancreatic ultrasound endoscopy can obtain the cross cross-section of the biliopancreatic system through ultrasonic detection, collect tissue samples for pathological biopsy through the puncture, and carry out operations such as flushing and drainage, radiofrequency ablation, abdominal ganglion block, and gastrojejunostomy. The device integrates detection and treatment. In terms of detection, endoscopic ultrasonography is close to the lesion tissue and can avoid influencing the abdominal cavity wall, gastrointestinal gas, and other organs. Compared with X-ray, CT, MRI, and different in vitro detection methods, endoscopic ultrasonography has technical advantages of high resolution, no ionizing radiation, and solid real-time performance. In terms of treatment, endoscopic ultrasound-guided puncture through natural lumen has the characteristics of minimally invasive, lower surgical risk and cost compared with surgical treatment, which can significantly improve the prognosis of patients with biliary and pancreatic diseases. Biliopancreatic endoscopic ultrasonography is essential for diagnosing and treating biliopancreatic system diseases.

The basis of endoscopic ultrasound diagnosis and treatment lies in the accurate identification and localization of lesions by ultrasound images. Ultrasound images are cross-section images of human tissues, mainly containing texture information that is difficult to be recognized by naked human eyes; for endoscopy, doctors lacking professional training and long-term practice, difficult to accurately identify the anatomical signs in the images, which significantly affects the accuracy of identification and localization of biliopancreatic lesions. Due to the low image recognition of ultrasonic endoscopy, the operation of ultrasonic endoscopy is difficult, and the training cycle is very long. Conventional European and American endoscopic ultrasound training programs lasted for three years, but in a clinical study, two-thirds of the physicians who participated in the standard training program failed to pass the ability assessment of endoscopic ultrasound. Although ultrasound endoscopy is one of the most sensitive methods for the diagnosis of pancreatic cancer, sensitivity (87.8%) is still significantly inadequate compared with optical endoscopy for the diagnosis of gastric cancer (96%) and colon cancer (97.1%). The existence of this problem means that the existing training in ultrasonic endoscopy needs to be optimized. In recent years, Artificial intelligence (AI) machine vision technology has developed rapidly and has been widely used in transportation, finance, medicine and other fields. Several studies have shown that the diagnostic ability of AI has surpassed that of human experts in some diseases, and some studies have confirmed the feasibility of the application of AI in endoscopic ultrasound image recognition. In a previous study, an artificial intelligence model was used to assist ultrasonic endoscopists in ultrasound film reading, which could significantly improve the accuracy of physicians in image positioning and anatomical marker segmentation. In the early stage, the investigators successfully constructed an artificial intelligence model-based ultrasonic endoscopy-assisted film reading system and named the modified system biliopancreatic Master. The system can realize real-time ultrasonic station recognition and anatomical mark recognition and provide doctors with corresponding operation techniques.

Endoscopist:

Inclusion Criteria:

Endoscopists with experience in performing more than 300 gastroscopies; Lack of experience in independently operating an endoscopic ultrasound (EUS); No experience in EUS-guided fine-needle aspiration biopsy (FNA).

Patient:

Inclusion Criteria:

Patient age ≥18 years; Patients who consecutively undergo sedated EUS procedures; Ability to read, understand, and sign the informed consent; Patients suspected of having biliary (both pancreatic and biliary) lesions based on clinical symptoms and/or radiological findings and/or laboratory test results; High-risk patients for pancreatic cancer: known genetic mutations associated with the risk of pancreatic cancer (BRCA2, BRCA1, PALB2, ATM, CDKNA/p16); familial pancreatic ductal adenocarcinoma with no known lineage mutations; Peutz-Jeghers syndrome (STK11); Lynch syndrome (MLH1/MSH2/MSH6, EPCAM, PMS2); familial adenomatous polyposis (APC) etc. Based on the preoperative cholangiopancreatography report, patients are classified into those with or without radiological findings. Patients with radiological findings are categorized by lesion location, such as the pancreas, PD, CBD, and other lesions (like gallbladder, duodenal papilla suspected of biliary invasion).

Exclusion Criteria:

Patients with absolute contraindications to EUS examination; Previous gastric surgery; Pregnancy; Severe internal medical diseases; History of allergy to anesthesia drugs; Esophageal narrowing or obstruction; Upper gastrointestinal anatomical abnormalities caused by advanced tumors.