• Artificial intelligence
• Cancer
• Endoscopic ultrasound
• Training

• Humans
• Endosonography/methods
• Artificial Intelligence
• Deep Learning
• Neural Networks, Computer
• Algorithms

• endoscopic ultrasound
• extra trees
• radiomics
• submucosal tumor
• tumor classification

• Natural Science Foundation of China

• Artificial intelligence (AI)
• Body composition assessment
• Sarcopenia diagnosis
• Skeletal muscle mass

• Sarcopenia/diagnosis
• Sarcopenia/diagnostic imaging
• Humans
• Artificial Intelligence
• Body Composition
• Aged
• Female
• Male
• Tomography, X-Ray Computed/methods
• Middle Aged
• Muscle, Skeletal/diagnostic imaging
• Reproducibility of Results
• Aged, 80 and over
• Adult

• Deep learning
• Endobronchial ultrasound-guided transbronchial needle aspiration
• Lung cancer
• Mediastinal lesions
• Vision transformer

• Deep Learning
• Image Interpretation, Computer-Assisted/methods
• Bronchoscopy/methods
• Endosonography/methods
• Mediastinal Neoplasms/diagnosis
• Mediastinal Neoplasms/pathology
• Mediastinal Neoplasms/secondary
• Diagnosis, Differential
• Area Under Curve
• Mediastinum/diagnostic imaging
• Mediastinum/pathology
• Humans

• Deep learning
• Diagnostic imaging
• Endosonography
• Rectal cancer

• Humans
• Deep Learning
• Endosonography/methods
• Colorectal Neoplasms/diagnostic imaging
• Colorectal Neoplasms/pathology
• Prospective Studies
• Image Interpretation, Computer-Assisted/methods
• Female
• Male
• Intestinal Mucosa/diagnostic imaging
• Intestinal Mucosa/pathology

• a Tempo study Pioneers in Health Care Innovation Fund, University of Twente
• a Tempo study Pioneers in Health Care Innovation Fund, University of Twente
• a Tempo study Pioneers in Health Care Innovation Fund, University of Twente

• Contrastive learning
• Endoscopic ultrasonography
• Multi-task learning
• Neural network
• Pancreatic diseases
• Semi-supervised learning

• Humans
• Pancreatic Neoplasms/diagnostic imaging
• Pancreatic Neoplasms/pathology
• Endosonography/methods
• Algorithms
• Neural Networks, Computer
• Pancreas/diagnostic imaging
• Pancreas/pathology
• Image Interpretation, Computer-Assisted/methods
• Supervised Machine Learning

• deep learning
• histological sampling
• macroscopically visible core
• stereomicroscopy on‐site evaluation
• visual on‐site evaluation

• ERCP
• EUS
• artificial intelligence
• deep learning
• pancreas

• Humans
• Female
• Ultrasonography
• Endoscopy
• Pancreas
• Pancreatic Neoplasms/diagnostic imaging
• Breast Neoplasms/diagnostic imaging
• Supervised Machine Learning

• anesthesia
• art of anesthesia
• artificial intelligence
• technology

• Humans
• Artificial Intelligence
• Anesthesia
• Anesthesiology

• Deep learning
• EUS
• Mediastinum
• Training

• artificial intelligence
• deep learning
• digestive system diseases
• endoscopic ultrasound
• machine learning

• 22ZYYLCCG09 Tianjin Medical University General Hospital Clinical Research Program

• artificial intelligence
• biopsy
• endoscopic ultrasound
• pathological diagnosis

• artificial intelligence
• endoscopic ultrasound
• pancreatic cancer
• tissue acquisition

• Humans
• Endosonography/methods
• Deep Learning
• Diagnosis, Differential
• Retrospective Studies
• Artificial Intelligence
• Pancreatic Neoplasms/pathology
• Carcinoma, Pancreatic Ductal/diagnosis
• Pancreatitis, Chronic/diagnostic imaging
• Pancreatic Neoplasms

• JP 21K15938 Japan Society for the Promotion of Science

• Endosonography
• Muscularis propria
• Perforation
• Stomach
• Submucosal tumors

• deep learning
• endoscopic ultrasonography
• pancreatic cancer
• segmentation
• surgical therapy

• 202016B01 Korea Medical Device Development Fund

Endoscopic ultrasound (EUS) morphology can aid in the discrimination between mucinous and non-mucinous pancreatic cystic lesions (PCLs) but has several limitations that can be overcome by artificial intelligence. We developed a convolutional neural network (CNN) algorithm for the automatic diagnosis of mucinous PCLs. Images retrieved from videos of EUS examinations for PCL characterization were used for the development, training, and validation of a CNN for mucinous cyst diagnosis. The performance of the CNN was measured calculating the area under the receiving operator characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values. A total of 5505 images from 28 pancreatic cysts were used (3725 from mucinous lesions and 1780 from non-mucinous cysts). The model had an overall accuracy of 98.5%, sensitivity of 98.3%, specificity of 98.9% and AUC of 1. The image processing speed of the CNN was 7.2 ms per frame. We developed a deep learning algorithm that differentiated mucinous and non-mucinous cysts with high accuracy. The present CNN may constitute an important tool to help risk stratify PCLs.

Early detection of pancreatic cancer has long eluded clinicians because of its insidious nature and onset. Often metastatic or locally invasive when symptomatic, most patients are deemed inoperable. In those who are symptomatic, multi-modal imaging modalities evaluate and confirm pancreatic ductal adenocarcinoma. In asymptomatic patients, detected pancreatic lesions can be either solid or cystic. The clinical implications of identifying small asymptomatic solid pancreatic lesions (SPLs) of < 2 cm are tantamount to a better outcome. The accurate detection of SPLs undoubtedly promotes higher life expectancy when resected early, driving the development of existing imaging tools while promoting more comprehensive screening programs. An imaging tool that has matured in its reiterations and received many image-enhancing adjuncts is endoscopic ultrasound (EUS). It carries significant importance when risk stratifying cystic lesions and has substantial diagnostic value when combined with fine needle aspiration/biopsy (FNA/FNB). Adjuncts to EUS imaging include contrast-enhanced harmonic EUS and EUS-elastography, both having improved the specificity of FNA and FNB. This review intends to compile all existing enhancement modalities and explore ongoing research around the most promising of all adjuncts in the field of EUS imaging, artificial intelligence.

• Pancreatic ductal adenocarcinoma
• Pancreatic cancer
• Endoscopic ultrasound
• Contrast-enhanced endoscopic ultrasound
• Endoscopic ultrasound contrast agents
• Endoscopic ultrasound elastography
• Artificial intelligence
• Fractal analysis
• Endoscopy
• Imaging

• Artificial Intelligence
• Elasticity Imaging Techniques
• Endoscopic Ultrasound-Guided Fine Needle Aspiration/methods
• Humans
• Pancreas/pathology
• Pancreatic Neoplasms/pathology

• Adenocarcinoma
• Artificial intelligence
• Detection
• Endoscopic ultrasonography
• Pancreatic cancer

• Endosonography/methods
• Humans
• Pancreas/diagnostic imaging
• Pancreas/pathology
• Pancreatic Neoplasms/diagnostic imaging
• Pancreatic Neoplasms/pathology
• Support Vector Machine
• Pancreatic Neoplasms

• artificial intelligences
• contrast-enhanced harmonic
• endoscopic ultrasonography
• gastrointestinal stromal tumor
• neural network
• submucosal tumor

• Humans
• Pancreatic Neoplasms/diagnosis
• Pancreatic Neoplasms

Pancreatic diseases have a substantial burden on society which is predicted to increase further over the next decades. Endoscopic ultrasonography (EUS) remains the best available diagnostic method to assess the pancreas, however, there remains room for improvement. Artificial intelligence (AI) approaches have been adopted to assess pancreatic diseases for over a decade, but this methodology has recently reached a new era with the innovative machine learning algorithms which can process, recognize, and label endosonographic images. Our review provides a targeted summary of AI in EUS for pancreatic diseases. Included studies cover a wide spectrum of pancreatic diseases from pancreatic cystic lesions to pancreatic masses and diagnosis of pancreatic cancer, chronic pancreatitis, and autoimmune pancreatitis. For these, AI models seemed highly successful, although the results should be evaluated carefully as the tasks, datasets and models were greatly heterogenous. In addition to use in diagnostics, AI was also tested as a procedural real-time assistant for EUS-guided biopsy as well as recognition of standard pancreatic stations and labeling anatomical landmarks during routine examination. Studies thus far have suggested that the adoption of AI in pancreatic EUS is highly promising and further opportunities should be explored in the field.

• Artificial intelligence
• Pancreas
• Endoscopic ultrasonography
• Pancreatic cancer
• Autoimmune pancreatitis
• Pancreatic cystic lesions

EUS is an important diagnostic tool in pancreatic lesions. Performance of single-center and/or single study artificial intelligence (AI) in the analysis of EUS-images of pancreatic lesions has been reported. The aim of this study was to quantitatively study the pooled rates of diagnostic performance of AI in EUS image analysis of pancreas using rigorous systematic review and meta-analysis methodology. Multiple databases were searched (from inception to December 2020) and studies that reported on the performance of AI in EUS analysis of pancreatic adenocarcinoma were selected. The random-effects model was used to calculate the pooled rates. In cases where multiple 2 × 2 contingency tables were provided for different thresholds, we assumed the data tables as independent from each other. Heterogeneity was assessed by I²% and 95% prediction intervals. Eleven studies were analyzed. The pooled overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 86% (95% confidence interval [82.8–88.6]), 90.4% (88.1–92.3), 84% (79.3–87.8), 90.2% (87.4–92.3) and 89.8% (86–92.7), respectively. On subgroup analysis, the corresponding pooled parameters in studies that used neural networks were 85.5% (80–89.8), 91.8% (87.8–94.6), 84.6% (73–91.7), 87.4% (82–91.3), and 91.4% (83.7–95.6)], respectively. Based on our meta-analysis, AI seems to perform well in the EUS-image analysis of pancreatic lesions.

• artificial intelligence
• endoscopic ultrasound
• meta-analysis

Endoscopic ultrasound (EUS) has emerged as an invaluable tool for the diagnosis, staging and treatment of pancreatic ductal adenocarcinoma (PDAC). EUS is currently the most sensitive imaging tool for the detection of solid pancreatic tumors. Conventional EUS has evolved, and new imaging techniques, such as contrast-enhanced harmonics and elastography, have been developed to improve diagnostic accuracy during the evaluation of focal pancreatic lesions. More recently, evaluation with artificial intelligence has shown promising results to overcome operator-related flaws during EUS imaging evaluation. Currently, an appropriate diagnosis is based on a proper histological assessment, and EUS-guided tissue acquisition is the standard procedure for pancreatic sampling. Newly developed cutting needles with core tissue procurement provide the possibility of molecular evaluation for personalized oncological treatment. Interventional EUS has modified the therapeutic approach, primarily for advanced pancreatic cancer. EUS-guided fiducial placement for local targeted radiotherapy treatment or EUS-guided radiofrequency ablation has been developed for local treatment, especially for patients with pancreatic cancer not suitable for surgical resection. Additionally, EUS-guided therapeutic procedures, such as celiac plexus neurolysis for pain control and EUS-guided biliary drainage for biliary obstruction, have dramatically improved in recent years toward a more effective and less invasive procedure to palliate complications related to PDAC. All the current benefits of EUS in the diagnosis and management of PDAC will be thoroughly discussed.

• Endoscopic ultrasound
• Contrast-enhanced harmonic
• Elastography
• Artificial intelligence
• Radiofrequency ablation
• Celiac plexus neurolysis
• Biliary drainage

The role of endoscopic ultrasound (EUS) as a diagnostic and therapeutic modality for the management of various gastrointestinal diseases has been expanding. The imaging or intervention for various liver diseases has primarily been the domain of radiologists. With the advances in EUS, the domain of endosonologists is rapidly expanding in the field of hepatology. The ability to combine endoscopy and sonography in one hybrid device is a unique property of EUS, together with the ability to bring its probe/transducer near the liver, the area of interest. Its excellent spatial resolution and ability to provide real-time images coupled with several enhancement techniques, such as contrast-enhanced (CE) EUS, have facilitated the growth of EUS. The concept of “Endo-hepatology” encompasses the wide range of diagnostic and therapeutic procedures that are now gradually becoming feasible for managing various liver diseases. Diagnostic advancements can enable a wide array of techniques from elastography and liver biopsy for liver parenchymal diseases, to CE-EUS for focal liver lesions to portal pressure measurements for managing various liver conditions. Similarly, therapeutic advancements range from EUS-guided eradication of varices, drainage of bilomas and abscesses to various EUS-guided modalities of liver tumor management. We provide a comprehensive review of all the different diagnostic and therapeutic EUS modalities available for the management of various liver diseases. A synopsis of all the technical details involving each procedure and the available data has been tabulated, and the future trends in this area have been highlighted.

• Endoscopic ultrasound
• Liver disease
• Elastography
• Varices
• Liver tumor
• Liver biopsy

The application of artificial intelligence (AI) using deep learning and machine learning approaches in modern medicine is rapidly expanding. Within the field of Gastroenterology, AI is being evaluated across a breadth of clinical and diagnostic applications including identification of pathology, differentiation of disease processes, and even automated procedure report generation. Many pancreatic pathologies can have overlapping features creating a diagnostic dilemma that provides a window for AI-assisted improvement in current evaluation and diagnosis, particularly using endoscopic ultrasound. This topic highlight will review the basics of AI, history of AI in gastrointestinal endoscopy, and prospects for AI in the evaluation of autoimmune pancreatitis, pancreatic ductal adenocarcinoma, chronic pancreatitis and intraductal papillary mucinous neoplasm.

• Artificial intelligence
• Deep learning
• Machine learning
• Convolutional neural network
• Endoscopic ultrasound
• Autoimmune pancreatitis
• Pancreatic ductal adenocarcinoma
• Chronic pancreatitis
• Intraductal papillary mucinous neoplasm

Artificial intelligence (AI) has been increasingly utilized in medical applications, especially in the field of gastroenterology. AI can assist gastroenterologists in imaging-based testing and prediction of clinical diagnosis, for examples, detecting polyps during colonoscopy, identifying small bowel lesions using capsule endoscopy images, and predicting liver diseases based on clinical parameters. With its high mortality rate, pancreatic cancer can highly benefit from AI since the early detection of small lesion is difficult with conventional imaging techniques and current biomarkers. Endoscopic ultrasound (EUS) is a main diagnostic tool with high sensitivity for pancreatic adenocarcinoma and pancreatic cystic lesion. The standard tumor markers have not been effective for diagnosis. There have been recent research studies in AI application in EUS and novel biomarkers to early detect and differentiate malignant pancreatic lesions. The findings are impressive compared to the available traditional methods. Herein, we aim to explore the utility of AI in EUS and novel serum and cyst fluid biomarkers for pancreatic cancer detection.

• Artificial intelligence
• Machine learning
• Deep learning
• Endoscopic ultrasound
• microRNA
• Pancreatic cancer
• Pancreatic cyst

Although pancreatic neuroendocrine neoplasms (PNENs) are relatively rare tumors, their number is increasing with advances in diagnostic imaging modalities. Even small lesions that are difficult to detect using computed tomography or magnetic resonance imaging can now be detected with endoscopic ultrasound (EUS). Contrast-enhanced EUS is useful, and not only diagnosis but also malignancy detection has become possible by evaluating the vascularity of tumors. Pathological diagnosis using EUS with fine-needle aspiration (EUS-FNA) is useful when diagnostic imaging is difficult. EUS-FNA can also be used to evaluate the grade of malignancy. Pooling the data of the studies that compared the PNENs grading between EUS-FNA samples and surgical specimens showed a concordance rate of 77.5% (κ-statistic = 0.65, 95% confidence interval = 0.59–0.71, p < 0.01). Stratified analysis for small tumor size (2 cm) showed that the concordance rate was 84.5% and the kappa correlation index was 0.59 (95% confidence interval = 0.43–0.74, p < 0.01). The evolution of ultrasound imaging technologies such as contrast-enhanced and elastography and the artificial intelligence that analyzes them, the evolution of needles, and genetic analysis, will further develop the diagnosis and treatment of PNENs in the future.

• endoscopic ultrasound
• pancreatic neuroendocrine neoplasms
• pancreatic tumor

Chronic pancreatitis (CP) is associated with a risk of pancreatic cancer and is characterized by irreversible morphological changes, fibrosis, calcification, and exocrine and endocrine insufficiency. CP is a progressive disease with a poor prognosis and is typically diagnosed at an advanced stage. The Japan Pancreas Society proposed criteria for early CP in 2009, and their usefulness has been reported. Recently, a mechanism definition was proposed by the International Consensus Guidelines and early CP was defined as a disease state that is not based on disease duration. CP is diagnosed by computed tomography, magnetic resonance imaging, and endoscopic cholangiopancreatography, which can detect calcification and dilation of the pancreatic ducts; however, detecting early CP with these modalities is difficult because subtle changes in early CP occur before established CP or end-stage CP. Endoscopic ultrasonography (EUS) is useful in the diagnosis of early CP because it allows high-resolution, close-up observation of the pancreas. In addition to imaging findings, EUS with elastography enables measurement of the stiffness of the pancreas, an objective diagnostic measure. Understanding the EUS findings of early CP is important because a histological diagnosis is problematic, and other modalities are not capable of detecting subtle changes in early CP.

• EUS
• early CP
• rosemont criteria

• endoscopic injection sclerotherapy
• endoscopic ultrasound
• endoscopic variceal ligation
• esophageal varices
• hemodynamics

• chronic pancreatitis
• endoscopic ultrasonography
• gallbladder wall thickening
• interobserver reliability
• intrapancreatic accessory spleen
• lymphadenopathy
• pancreatic cystic lesion
• solid pancreatic lesion
• subepithelial lesion