• Humans
• Artificial Intelligence
• Endosonography/methods
• Cholangiopancreatography, Endoscopic Retrograde/methods
• Deep Learning
• Pancreatic Diseases/diagnostic imaging

• Diagnosis and imaging (inc chromoendoscopy, NBI, iSCAN, FICE, CLE)
• Endoscopic ultrasonography
• Endoscopy Upper GI Tract
• Pancreas
• Precancerous conditions & cancerous lesions (displasia and cancer) stomach

Endoscopy is the cornerstone in the management of digestive diseases. Over the last few decades, technology has played an important role in the development of this field, helping endoscopists in better detecting and characterizing luminal lesions. However, despite ongoing advancements in endoscopic technology, the incidence of missed pre-neoplastic and neoplastic lesions remains high due to the operator-dependent nature of endoscopy and the challenging learning curve associated with new technologies. Artificial intelligence (AI), an operator-independent field, could be an invaluable solution. AI can serve as a “second observer”, enhancing the performance of endoscopists in detecting and characterizing luminal lesions. By utilizing deep learning (DL), an innovation within machine learning, AI automatically extracts input features from targeted endoscopic images. DL encompasses both computer-aided detection and computer-aided diagnosis, assisting endoscopists in reducing missed detection rates and predicting the histology of luminal digestive lesions. AI applications in clinical gastrointestinal diseases are continuously expanding and evolving the entire digestive tract. In all published studies, real-time AI assists endoscopists in improving the performance of non-expert gastroenterologists, bringing it to a level comparable to that of experts. The development of DL may be affected by selection biases. Studies have utilized different AI-assisted models, which are heterogeneous. In the future, algorithms need validation through large, randomized trials. Theoretically, AI has no limit to assist endoscopists in increasing the accuracy and the quality of endoscopic exams. However, practically, we still have a long way to go before standardizing our AI models to be accepted and applied by all gastroenterologists.

• Artificial intelligence
• Digestive tract
• Gastroenterology
• Gastroscopy
• Coloscopy

• AI-assisted systems
• Artificial intelligence
• Endoscopic ultrasound
• Pancreas

• Humans
• Endosonography/methods
• Artificial Intelligence
• Pancreas/diagnostic imaging
• Machine Learning
• Deep Learning
• Pancreatic Neoplasms/diagnostic imaging
• Pancreatic Diseases/diagnostic imaging
• Image Interpretation, Computer-Assisted/methods
• Image Processing, Computer-Assisted/methods

• celiac plexus neurolysis
• endoscopic ultrasound
• endoscopic ultrasound-guided interventions
• pancreatic ductal adenocarcinoma

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

• Male
• Humans
• Adult
• Middle Aged
• Retrospective Studies
• Pancreas/pathology
• Pancreatitis, Chronic/diagnosis
• Pancreatitis, Chronic/diagnostic imaging
• Pancreatic Neoplasms/diagnosis
• Pancreatic Neoplasms/diagnostic imaging
• Endosonography
• Carcinoma/pathology

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

Artificial intelligence (AI) has been used in various fields of day-to-day life and its role in medicine is immense. Understanding of oncology has been improved with the introduction of AI which helps in diagnosis, treatment planning, management, prognosis, and follow-up. It also helps to identify high-risk groups who can be subjected to timely screening for early detection of malignant conditions. It is more important in pancreatic cancer as it is one of the major causes of cancer-related deaths worldwide and there are no specific early features (clinical and radiological) for diagnosis. With improvement in imaging modalities (computed tomography, magnetic resonance imaging, endoscopic ultrasound), most often clinicians were being challenged with lesions that were difficult to diagnose with human competence. AI has been used in various other branches of medicine to differentiate such indeterminate lesions including the thyroid gland, breast, lungs, liver, adrenal gland, kidney, etc. In the case of pancreatic cancer, the role of AI has been explored and is still ongoing. This review article will focus on how AI can be used to diagnose pancreatic cancer early or differentiate it from benign pancreatic lesions, therefore, management can be planned at an earlier stage.

• Artificial intelligence
• Indeterminate pancreatic lesion
• Imaging
• Biomarkers
• Diagnosis

• artificial intelligence
• deep learning
• machine learning
• pancreatic cancer
• screening

• CT
• MRI
• cardiac hypertrophy
• cardiac sensors
• cardiomyopathy
• early detection
• heart failure
• heart remodeling
• pancreatic cancer
• ultrasound imaging

• artificial intelligence
• clinical implementation
• computer-aided detection
• diagnostics
• pancreatic ductal adenocarcinoma
• radiological imaging

• RVO characteristic TKI2112P08 Eindhoven AI Systems Institute (EAISI)

• Acute pancreatitis
• Machine learning
• Pancreaticobiliary maljunction

• CT
• artificial intelligence
• endoscopic ultrasound
• endoscopy
• gastrointestinal diseases
• radiology

• 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

• artificial intelligence
• cholangiocarcinoma
• radiomics
• risk assessment
• surveillance

• artificial intelligence
• pancreatic cancer
• radiomics
• risk assessment
• surveillance

• AI
• EUS
• accuracy
• artificial intelligence
• pancreatic cancer
• predicting pancreatic ductal adenocarcinoma

• artificial intelligence
• autoimmune pancreatitis
• chronic pancreatitis
• endoscopic ultrasound
• pancreatic cancer

• artificial intelligence
• cancer imaging
• deep learning
• pancreatic cancer
• risk prediction

• SR/NM/NS-1095/2012 Department of Science and Technology
• SR/FST/LSI-622/2014 Department of Science and Technology

Artificial intelligence (AI), especially deep learning, is gaining extensive attention for its excellent performance in medical image analysis. It can automatically make a quantitative assessment of complex medical images and help doctors to make more accurate diagnoses. In recent years, AI based on ultrasound has been shown to be very helpful in diffuse liver diseases and focal liver lesions, such as analyzing the severity of nonalcoholic fatty liver and the stage of liver fibrosis, identifying benign and malignant liver lesions, predicting the microvascular invasion of hepatocellular carcinoma, curative transarterial chemoembolization effect, and prognoses after thermal ablation. Moreover, AI based on endoscopic ultrasonography has been applied in some gastrointestinal diseases, such as distinguishing gastric mesenchymal tumors, detection of pancreatic cancer and intraductal papillary mucinous neoplasms, and predicting the preoperative tumor deposits in rectal cancer. This review focused on the basic technical knowledge about AI and the clinical application of AI in ultrasound of liver and gastroenterology diseases. Lastly, we discuss the challenges and future perspectives of AI.

• Artificial intelligence
• Ultrasound
• Liver
• Gastroenterology
• Deep learning

• Humans
• Artificial Intelligence
• Gastroenterology/methods
• Carcinoma, Hepatocellular/therapy
• Chemoembolization, Therapeutic/methods
• Liver Neoplasms/therapy

• Artificial intelligence
• early detection
• machine learning
• pancreatic cancer
• prognosis prediction

• Humans
• Artificial Intelligence
• Algorithms
• Pancreatic Neoplasms/diagnosis
• Biomarkers, Tumor

• Artificial intelligence
• Early diagnosis
• Liquid biopsy
• Pancreatic cancer
• Pancreatic cancer biomarkers
• Pancreatic cancer screening
• Pancreatic ductal adenocarcinoma

• Artificial Intelligence
• Biomarkers, Tumor
• Carcinoma, Pancreatic Ductal/diagnosis
• Carcinoma, Pancreatic Ductal/therapy
• Early Detection of Cancer
• Humans
• Pancreatic Neoplasms/diagnosis
• Pancreatic Neoplasms/pathology
• Pancreatic Neoplasms/therapy

Multiple tools have been applied to radiomics evaluation, while evidence rating tools for this field are still lacking. This study aims to assess the quality of pancreatitis radiomics research and test the feasibility of the evidence level rating tool.

Thirty studies were included after a systematic search of pancreatitis radiomics studies until February 28, 2022, via five databases. Twenty-four studies employed radiomics for diagnostic purposes. The mean ± standard deviation of the adherence rate was 38.3 ± 13.3%, 61.3 ± 11.9%, and 37.1 ± 27.2% for the Radiomics Quality Score (RQS), the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, and the Image Biomarker Standardization Initiative (IBSI) guideline for preprocessing steps, respectively. The median (range) of RQS was 7.0 (− 3.0 to 18.0). The risk of bias and application concerns were mainly related to the index test according to the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The meta-analysis on differential diagnosis of autoimmune pancreatitis versus pancreatic cancer by CT and mass-forming pancreatitis versus pancreatic cancer by MRI showed diagnostic odds ratios (95% confidence intervals) of, respectively, 189.63 (79.65–451.48) and 135.70 (36.17–509.13), both rated as weak evidence mainly due to the insufficient sample size.

More research on prognosis of acute pancreatitis is encouraged. The current pancreatitis radiomics studies have insufficient quality and share common scientific disadvantages. The evidence level rating is feasible and necessary for bringing the field of radiomics from preclinical research area to clinical stage.

The online version contains supplementary material available at 10.1186/s13244-022-01279-4.

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

We evaluated the related research on artificial intelligence (AI) in pancreatic cancer (PC) through bibliometrics analysis and explored the research hotspots and current status from 1997 to 2021.

Publications related to AI in PC were retrieved from the Web of Science Core Collection (WoSCC) during 1997-2021. Bibliometrix package of R software 4.0.3 and VOSviewer were used to bibliometrics analysis.

A total of 587 publications in this field were retrieved from WoSCC database. After 2018, the number of publications grew rapidly. The United States and Johns Hopkins University were the most influential country and institution, respectively. A total of 2805 keywords were investigated, 81 of which appeared more than 10 times. Co-occurrence analysis categorized these keywords into five types of clusters: (1) AI in biology of PC, (2) AI in pathology and radiology of PC, (3) AI in the therapy of PC, (4) AI in risk assessment of PC and (5) AI in endoscopic ultrasonography (EUS) of PC. Trend topics and thematic maps show that keywords " diagnosis ", “survival”, “classification”, and “management” are the research hotspots in this field.

The research related to AI in pancreatic cancer is still in the initial stage. Currently, AI is widely studied in biology, diagnosis, treatment, risk assessment, and EUS of pancreatic cancer. This bibliometrics study provided an insight into AI in PC research and helped researchers identify new research orientations.

• 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

Pancreatic cancer is the most lethal type of malignancy and is characterized by high invasiveness without severe symptoms. It is difficult to detect PC at an early stage because of the low diagnostic accuracy of existing routine methods, such as abdominal ultrasound, CT, MRI, and endoscopic ultrasound (EUS). Therefore, it is of value to develop new diagnostic techniques for early detection with high accuracy. In this review, we aim to highlight research progress on novel biomarkers, artificial intelligence, and nanomaterial applications on the diagnostic accuracy of pancreatic cancer.

Artificial intelligence (AI)-based tools have ushered in a new era of innovation in the field of gastrointestinal (GI) endoscopy. Despite vast improvements in endoscopic techniques and equipment, diagnostic endoscopy remains heavily operator-dependent, in particular, colonoscopy and endoscopic ultrasound (EUS). Recent reports have shown that as much as 25% of colonic adenomas may be missed at colonoscopy. This can result in an increased incidence of interval colon cancer. Similarly, EUS has been shown to have high inter-observer variability, overlap in diagnoses with a relatively low specificity for pancreatic lesions. Our understanding of Machine-learning (ML) techniques in AI have evolved over the last decade and its application in AI–based tools for endoscopic detection and diagnosis is being actively investigated at several centers. ML is an aspect of AI that is based on neural networks, and is widely used for image classification, object detection, and semantic segmentation which are key functional aspects of AI-related computer aided diagnostic systems. In this review, current status and limitations of ML, specifically for adenoma detection and endosonographic diagnosis of pancreatic lesions, will be summarized from existing literature. This will help to better understand its role as viewed through the prism of real world application in the field of GI endoscopy.

• Artificial intelligence
• Artificial
• Machine
• Colonoscopy
• Polyp
• Endosonography
• Pancreas

Artificial intelligence (AI) is a quickly expanding field in gastrointestinal endoscopy. Although there are a myriad of applications of AI ranging from identification of bleeding to predicting outcomes in patients with inflammatory bowel disease, a great deal of research has focused on the identification and classification of gastrointestinal malignancies. Several of the initial randomized, prospective trials utilizing AI in clinical medicine have centered on polyp detection during screening colonoscopy. In addition to work focused on colorectal cancer, AI systems have also been applied to gastric, esophageal, pancreatic, and liver cancers. Despite promising results in initial studies, the generalizability of most of these AI systems have not yet been evaluated. In this article we review recent developments in the field of AI applied to gastrointestinal oncology.

• Artificial intelligence
• Oncology
• Gastroenterology
• Endoscopy
• Machine learning
• Computer-assisted decision making
• Computer-aided detection
• Computer-aided diagnosis

• artificial intelligence
• artificial neural network
• convolutional neural network
• endoscopic ultrasonography
• pancreatic cancer
• support vector machine

Pancreatic adenocarcinoma remains to be one of the deadliest malignancies in the world despite treatment advancement over the past few decades. Its low survival rates and poor prognosis can be attributed to ambiguity in recommendations for screening and late symptom onset, contributing to its late presentation. In the recent years, artificial intelligence (AI) as emerged as a field to aid in the process of clinical decision making. Considerable efforts have been made in the realm of AI to screen for and predict future development of pancreatic ductal adenocarcinoma. This review discusses the use of AI in early detection and screening for pancreatic adenocarcinoma, and factors which may limit its use in a clinical setting.

• Artificial intelligence
• Pancreatic cancer
• Pancreatic adenocarcinoma
• screening
• Early detection

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

With a five-year survival rate of only 3% for the majority of patients, pancreatic cancer is a global healthcare challenge. Radiomics and deep learning, two novel quantitative imaging methods that treat medical images as minable data instead of just pictures, have shown promise in advancing personalized management of pancreatic cancer through diagnosing precursor diseases, early detection, accurate diagnosis, and treatment personalization. Radiomics and deep learning methods aim to collect hidden information in medical images that is missed by conventional radiology practices through expanding the data search and comparing information across different patients. Both methods have been studied and applied in pancreatic cancer. In this review, we focus on the current progress of these two methods in pancreatic cancer and provide a comprehensive narrative review on the topic. With better regulation, enhanced workflow, and larger prospective patient datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through personalized precision medicine.

As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization.

• deep learning
• machine learning
• pancreatic cancer
• quantitative imaging
• radiomics

• 2P50CA127297-06A1 NIH HHS

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

• artificial intelligence
• computer-aided diagnosis
• deep learning
• endoscopic ultrasound
• pancreatic cancer

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

• computer-aided diagnosis
• deep learning
• endoscopic ultrasonography
• pancreatic cyst lesion
• segmentation

• Cross-Over Studies
• Deep Learning
• Endosonography
• Humans
• Image Processing, Computer-Assisted
• Pancreatic Cyst

• EUS
• artificial intelligence
• computer-assisted diagnosis
• computer-assisted image analysis
• machine learning
• pancreatic cancer

• Artificial intelligence
• Machine learning
• Pancreatic cancer
• Pancreatic ductal adenocarcinoma
• Precision medicine

• Artificial Intelligence
• Carcinoma, Pancreatic Ductal/genetics
• Carcinoma, Pancreatic Ductal/therapy
• Genomics
• Humans
• Pancreatic Neoplasms/genetics
• Precision Medicine

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.

• Artificial intelligence
• Machine learning
• Deep learning
• Clinical applications
• Gastroenterology

• Artificial Intelligence
• Barrett Esophagus
• Diagnostic Imaging
• Endoscopy
• Gastroenterology
• Humans

Chronic pancreatitis (CP) is a progressive condition caused by several factors and characterised by pancreatic fibrosis and dysfunction. However, CP is difficult to diagnose at an early stage. Various advanced methods including endoscopic ultrasound based elastography and confocal laser endomicroscopy have been used to diagnose early CP, although no unified diagnostic standards have been established. In the past, the diagnosis was mainly based on imaging, and no comprehensive evaluations were performed. This review describes and compares the advantages and limitations of the traditional and latest diagnostic modalities and suggests guidelines for the standardisation of the methods used to diagnose early CP.

• Chronic pancreatitis
• Pancreatic fibrosis
• Early diagnosis
• Ultrasound endoscopy
• Endosonography
• Elastography
• Confocal laser endomicroscopy

• Elasticity Imaging Techniques
• Endosonography
• Humans
• Pancreatic Diseases
• Pancreatitis, Chronic/diagnostic imaging

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