• early diagnosis of cancer
• early treatment of cancer
• esophageal cancer
• gastrointestinal endoscopy
• screening

• Humans
• Esophageal Neoplasms/diagnosis
• Esophageal Neoplasms/therapy
• Esophageal Neoplasms/surgery
• Esophageal Neoplasms/epidemiology
• Early Detection of Cancer/methods
• Esophagoscopy/methods
• Esophageal Squamous Cell Carcinoma/diagnosis
• Global Health
• Carcinoma, Squamous Cell/diagnosis
• Carcinoma, Squamous Cell/therapy

• AI study bias
• AI tool development
• Artificial intelligence
• Cancer screening
• Deep learning
• Upper endoscopy

• Humans
• Esophageal Neoplasms/diagnosis
• Esophageal Neoplasms/diagnostic imaging
• Esophageal Neoplasms/therapy
• Artificial Intelligence
• Stomach Neoplasms/diagnosis
• Stomach Neoplasms/therapy
• Stomach Neoplasms/diagnostic imaging
• Precancerous Conditions/diagnosis
• Precancerous Conditions/diagnostic imaging
• Diagnosis, Computer-Assisted/methods
• Early Detection of Cancer/methods
• Deep Learning
• Esophageal Squamous Cell Carcinoma/diagnosis

• randomized controlled trials
• oesophageal cancer

• SHDC2023CRT006 Shanghai Shen Kang Hospital Development Center
• 82273025 National Natural Science Foundation of China (National Science Foundation of China)
• 82203460 National Natural Science Foundation of China (National Science Foundation of China)
• 22XD1402200 Science and Technology Commission of Shanghai Municipality (Shanghai Municipal Science and Technology Commission)
• Shanghai Xuhui District Central Hospital (Xuhui Central Hospital)
• the Science and Technology Commission Foundation of Xuhui District, Shanghai (grant no. 23XHYD-11);Shanghai Oriental Talent Program (grant no. 2023-168).
• the Outstanding Clinical Postdoctoral Program of Zhongshan Hospital Affiliated to Fudan University (grant no. 2023ZYYS-002)

• Artificial intelligence
• Deep learning
• Esophageal squamous cell carcinoma
• Machine learning
• Precision tumor therapy

• Humans
• Esophageal Squamous Cell Carcinoma/therapy
• Esophageal Squamous Cell Carcinoma/pathology
• Esophageal Squamous Cell Carcinoma/mortality
• Esophageal Squamous Cell Carcinoma/diagnosis
• Artificial Intelligence
• Esophageal Neoplasms/therapy
• Esophageal Neoplasms/pathology
• Esophageal Neoplasms/mortality
• Esophageal Neoplasms/diagnosis
• Precision Medicine/methods
• Prognosis
• Clinical Decision-Making/methods
• Early Detection of Cancer/methods
• Medical Oncology/methods
• Medical Oncology/trends

• Barrett’s esophagus
• computer-aided diagnosis
• convolutional neural networks
• deep learning
• esophageal cancer
• machine learning

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

The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate. Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality. Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes. Artificial intelligence (AI)-assisted diagnostic, prognostic, and therapeutic tools can assist in expeditious diagnosis, treatment planning/response prediction, and post-surgical prognostication. AI can intercept neoplastic lesions in their primordial stages, accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic, histopathological, and/or endoscopic analyses, and eliminate over-dependence on clinicians. AI-based models have shown to be on par, and sometimes even outperformed experienced gastroenterologists and radiologists. Convolutional neural networks (state-of-the-art deep learning models) are powerful computational models, invaluable to the field of precision oncology. These models not only reliably classify images, but also accurately predict response to chemotherapy, tumor recurrence, metastasis, and survival rates post-treatment. In this systematic review, we analyze the available evidence about the diagnostic, prognostic, and therapeutic utility of artificial intelligence in gastrointestinal oncology.

• Artificial intelligence
• Gastrointestinal malignancies
• Machine learning
• Helicobacter pylori
• State-of-the-art deep learning models

• AI-assisted gastroscopy
• artificial intelligence
• diagnosis
• endoscopists
• gastric neoplasm

• collaborative care
• financial toxicity
• gastrointestinal cancer
• geriatric oncology
• hospital-at-home
• palliative oncology
• patient-reported outcomes
• supportive care

• Humans
• Esophageal Squamous Cell Carcinoma/surgery
• Adenocarcinoma/drug therapy
• Adenocarcinoma/surgery
• Adenocarcinoma/pathology
• Esophageal Neoplasms/surgery
• Esophageal Neoplasms/pathology
• Endoscopic Mucosal Resection/adverse effects
• Pancreatic Neoplasms/surgery
• Treatment Outcome
• Retrospective Studies

• cancer screening
• endoscopic imaging
• esophageal cancers
• robotics

• Humans
• Esophageal Neoplasms/diagnosis
• Esophageal Neoplasms/pathology
• Esophageal Squamous Cell Carcinoma
• Artificial Intelligence
• Endoscopy
• Adenocarcinoma

Esophageal cancer is the seventh-most common cancer type worldwide, accounting for 5% of death from malignancy. Development of novel diagnostic techniques has facilitated screening, early detection, and improved prognosis. Convolutional neural network (CNN)-based image analysis promises great potential for diagnosing and determining the prognosis of esophageal cancer, enabling even early detection of dysplasia.

To conduct a meta-analysis of the diagnostic accuracy of CNN models for the diagnosis of esophageal cancer and high-grade dysplasia (HGD).

PubMed, EMBASE, Web of Science and Cochrane Library databases were searched for articles published up to November 30, 2022. We evaluated the diagnostic accuracy of using the CNN model with still image-based analysis and with video-based analysis for esophageal cancer or HGD, as well as for the invasion depth of esophageal cancer. The pooled sensitivity, pooled specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and area under the curve (AUC) were estimated, together with the 95% confidence intervals (CI). A bivariate method and hierarchical summary receiver operating characteristic method were used to calculate the diagnostic test accuracy of the CNN model. Meta-regression and subgroup analyses were used to identify sources of heterogeneity.

A total of 28 studies were included in this systematic review and meta-analysis. Using still image-based analysis for the diagnosis of esophageal cancer or HGD provided a pooled sensitivity of 0.95 (95%CI: 0.92-0.97), pooled specificity of 0.92 (0.89-0.94), PLR of 11.5 (8.3-16.0), NLR of 0.06 (0.04-0.09), DOR of 205 (115-365), and AUC of 0.98 (0.96-0.99). When video-based analysis was used, a pooled sensitivity of 0.85 (0.77-0.91), pooled specificity of 0.73 (0.59-0.83), PLR of 3.1 (1.9-5.0), NLR of 0.20 (0.12-0.34), DOR of 15 (6-38) and AUC of 0.87 (0.84-0.90) were found. Prediction of invasion depth resulted in a pooled sensitivity of 0.90 (0.87-0.92), pooled specificity of 0.83 (95%CI: 0.76-0.88), PLR of 7.8 (1.9-32.0), NLR of 0.10 (0.41-0.25), DOR of 118 (11-1305), and AUC of 0.95 (0.92-0.96).

CNN-based image analysis in diagnosing esophageal cancer and HGD is an excellent diagnostic method with high sensitivity and specificity that merits further investigation in large, multicenter clinical trials.

• Esophageal cancer
• High-grade dysplasia
• Convolutional neural network
• Deep learning
• Systematic review
• Meta-analysis

• Artificial intelligence
• Barrett's
• IBD
• capsule endoscopy
• endoscopy
• polyp

• Humans
• Artificial Intelligence
• Colonic Neoplasms/diagnostic imaging
• Crohn Disease/diagnostic imaging
• Endoscopy
• Endoscopy, Gastrointestinal
• Gastroenterology
• Barrett Esophagus/diagnostic imaging

• artificial intelligence
• convolutional neural network
• diagnosis
• endoscopy
• esophageal squamous cell carcinoma

• Histogram of Oriented Gradients
• Multi-task learning
• Self-supervised learning
• Semantic segmentation

• Humans
• Deep Learning
• Semantics
• Hand
• Image Processing, Computer-Assisted

• Wellcome Trust
• 203145/Z/16/Z Wellcome Trust

• cancer diagnosis
• deep learning
• meta-analysis
• systematic review

• Humans
• Deep Learning
• Neoplasms/diagnostic imaging
• Algorithms
• Data Science

• Artificial intelligence-assisted diagnosis
• Deep learning
• Early-stage esophageal squamous cell carcinoma
• Faster R-CNN
• HRNetV2p
• Magnification endoscopy images

• anesthesia assistance
• detection rate
• esophageal squamous cell cancer
• esophagogastroduodenoscopy
• retrospective; propensity score matching

Artificial intelligence (AI) is a complex concept, broadly defined in medicine as the development of computer systems to perform tasks that require human intelligence. It has the capacity to revolutionize medicine by increasing efficiency, expediting data and image analysis and identifying patterns, trends and associations in large datasets. Within gastroenterology, recent research efforts have focused on using AI in esophagogastroduodenoscopy, wireless capsule endoscopy (WCE) and colonoscopy to assist in diagnosis, disease monitoring, lesion detection and therapeutic intervention. The main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy, WCE and colonoscopy.

• Artificial intelligence
• Colonoscopy
• Computer-aided detection
• Deep learning
• Endoscopy
• Machine learning

• artificial intelligence
• automated diagnosis
• convolutional neural network
• esophageal cancer
• gastrointestinal endoscopy

• Artificial intelligence
• Esophageal squamous cell carcinoma
• Intrapapillary capillary loops

• Humans
• Artificial Intelligence
• Esophageal Neoplasms/diagnostic imaging
• Esophageal Squamous Cell Carcinoma/pathology
• Esophagoscopy/methods
• Microvessels/pathology
• Narrow Band Imaging/methods
• Precancerous Conditions/diagnostic imaging
• Retrospective Studies

• National Natural Science Foundation of China
• 1·3·5 project for disciplines of excellence, West China Hospital, Sichuan University

The artificial intelligence (AI)-assisted endoscopic detection of early gastric cancer (EGC) has been preliminarily developed. The currently used algorithms still exhibit limitations of large calculation and low-precision expression. The present study aimed to develop an endoscopic automatic detection system in EGC based on a mask region-based convolutional neural network (Mask R-CNN) and to evaluate the performance in controlled trials. For this purpose, a total of 4,471 white light images (WLIs) and 2,662 narrow band images (NBIs) of EGC were obtained for training and testing. In total, 10 of the WLIs (videos) were obtained prospectively to examine the performance of the RCNN system. Furthermore, 400 WLIs were randomly selected for comparison between the Mask R-CNN system and doctors. The evaluation criteria included accuracy, sensitivity, specificity, positive predictive value and negative predictive value. The results revealed that there were no significant differences between the pathological diagnosis with the Mask R-CNN system in the WLI test (χ² = 0.189, P=0.664; accuracy, 90.25%; sensitivity, 91.06%; specificity, 89.01%) and in the NBI test (χ² = 0.063, P=0.802; accuracy, 95.12%; sensitivity, 97.59%). Among 10 WLI real-time videos, the speed of the test videos was up to 35 frames/sec, with an accuracy of 90.27%. In a controlled experiment of 400 WLIs, the sensitivity of the Mask R-CNN system was significantly higher than that of experts (χ² = 7.059, P=0.000; 93.00% VS 80.20%), and the specificity was higher than that of the juniors (χ² = 9.955, P=0.000, 82.67% VS 71.87%), and the overall accuracy rate was higher than that of the seniors (χ² = 7.009, P=0.000, 85.25% VS 78.00%). On the whole, the present study demonstrates that the Mask R-CNN system exhibited an excellent performance status for the detection of EGC, particularly for the real-time analysis of WLIs. It may thus be effectively applied to clinical settings.

Upper gastrointestinal endoscopy is critical for esophageal squamous cell carcinoma (ESCC) detection; however, endoscopists require long-term training to avoid missing superficial lesions.

To develop a deep learning computer-assisted diagnosis (CAD) system for endoscopic detection of superficial ESCC and investigate its application value.

We configured the CAD system for white-light and narrow-band imaging modes based on the YOLO v5 algorithm. A total of 4447 images from 837 patients and 1695 images from 323 patients were included in the training and testing datasets, respectively. Two experts and two non-expert endoscopists reviewed the testing dataset independently and with computer assistance. The diagnostic performance was evaluated in terms of the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity.

The area under the receiver operating characteristics curve, accuracy, sensitivity, and specificity of the CAD system were 0.982 [95% confidence interval (CI): 0.969-0.994], 92.9% (95%CI: 89.5%-95.2%), 91.9% (95%CI: 87.4%-94.9%), and 94.7% (95%CI: 89.0%-97.6%), respectively. The accuracy of CAD was significantly higher than that of non-expert endoscopists (78.3%, P < 0.001 compared with CAD) and comparable to that of expert endoscopists (91.0%, P = 0.129 compared with CAD). After referring to the CAD results, the accuracy of the non-expert endoscopists significantly improved (88.2% vs 78.3%, P < 0.001). Lesions with Paris classification type 0-IIb were more likely to be inaccurately identified by the CAD system.

The diagnostic performance of the CAD system is promising and may assist in improving detectability, particularly for inexperienced endoscopists.

• Computer-aided diagnosis
• Artificial intelligence
• Deep learning
• Esophageal squamous cell carcinoma
• Early detection of cancer
• Upper gastrointestinal endoscopy

• Humans
• Esophageal Squamous Cell Carcinoma/diagnostic imaging
• Artificial Intelligence
• Esophageal Neoplasms/diagnostic imaging
• Esophageal Neoplasms/pathology
• Narrow Band Imaging
• Endoscopy, Gastrointestinal

• National Natural Science Foundation of China

• ResNet150V2
• U-Net
• encoder–decoder model
• esophageal cancer
• narrowband imaging
• semantic segmentation
• small data
• white light imaging

• MOST 109-2622-E-194-007 Ministry of Science and Technology
• CCU-NTUHYB-2022-01 National Chung Cheng University-National Taiwan University Hospital Yunlin Branch Joint Re-search Program
• 111-010 Kaohsiung Armed Forces General Hospital

The aim of this study was to assess the diagnostic ability of artificial intelligence (AI) in the detection of early upper gastrointestinal cancer (EUGIC) using endoscopic images.

Databases were searched for studies on AI-assisted diagnosis of EUGIC using endoscopic images. The pooled area under the curve (AUC), sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) with 95% confidence interval (CI) were calculated.

Overall, 34 studies were included in our final analysis. Among the 17 image-based studies investigating early esophageal cancer (EEC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.95 (95% CI, 0.95–0.96), 0.95 (95% CI, 0.94–0.95), 10.76 (95% CI, 7.33–15.79), 0.07 (95% CI, 0.04–0.11), and 173.93 (95% CI, 81.79–369.83), respectively. Among the seven patient-based studies investigating EEC detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.94 (95% CI, 0.91–0.96), 0.90 (95% CI, 0.88–0.92), 6.14 (95% CI, 2.06–18.30), 0.07 (95% CI, 0.04–0.11), and 69.13 (95% CI, 14.73–324.45), respectively. Among the 15 image-based studies investigating early gastric cancer (EGC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.94, 0.87 (95% CI, 0.87–0.88), 0.88 (95% CI, 0.87–0.88), 7.20 (95% CI, 4.32–12.00), 0.14 (95% CI, 0.09–0.23), and 48.77 (95% CI, 24.98–95.19), respectively.

On the basis of our meta-analysis, AI exhibited high accuracy in diagnosis of EUGIC.

https://www.crd.york.ac.uk/PROSPERO/, identifier PROSPERO (CRD42021270443).

• artificial intelligence
• early detection of cancer
• endoscopy
• systematic review
• upper gastrointestinal tract

• Esophageal squamous cell carcinoma
• Immunophenotyping
• Prognosis
• Single-sample GSEA
• Tumor microenvironment

• Biomarkers, Tumor/genetics
• Esophageal Neoplasms/pathology
• Esophageal Squamous Cell Carcinoma/genetics
• Esophageal Squamous Cell Carcinoma/pathology
• Gene Expression Regulation, Neoplastic
• Humans
• Immunophenotyping
• Prognosis
• Tumor Microenvironment

• Artificial intelligence
• Classification
• Computer-assisted diagnosis
• Deep learning
• Detection
• Endoscopy

• Artificial Intelligence
• Barrett's esophagus
• Convoluted Neural Networks
• Deep Learning
• Esophageal Cancer
• Gastric Cancer
• Upper GI Endoscopy

• Convolutional neural network
• Deep learning
• Pouchitis
• Prediction
• Ulcerative colitis

• Barrett’s oesophagus
• IPCL
• artificial intelligence
• gastroesophageal reflux disease
• gastrointestinal endoscopy
• oesophageal cancer

• Artificial Intelligence
• Endoscopy
• Gastroesophageal Reflux/diagnosis
• Humans
• Odds Ratio
• ROC Curve

• classification
• deep learning
• esophageal lesions
• gastrointestinal endoscopy
• multi-task
• segmentation

• Deep Learning
• Endoscopy
• Humans
• Image Processing, Computer-Assisted

• Artificial Intelligence
• Endoscopy
• Esophageal Neoplasms/diagnosis
• Esophageal Neoplasms/pathology
• Esophageal Squamous Cell Carcinoma/diagnosis
• Humans
• Margins of Excision

• artificial intelligence
• gastrointestinal endoscopy
• upper gastrointestinal cancer
• gerd
• helicobacter pylori infection

• Artificial Intelligence
• Endoscopy, Gastrointestinal
• Gastritis
• Gastroenterology
• Humans
• Upper Gastrointestinal Tract

In recent years, artificial intelligence (AI) combined with endoscopy has made an appearance in the diagnosis of early esophageal cancer (EC) and achieved satisfactory results. Due to the rapid progression and poor prognosis of EC, the early detection and diagnosis of EC are of great value for patient prognosis improvement. AI has been applied in the screening of early EC and has shown advantages; notably, it is more accurate than less-experienced endoscopists. In China, the detection of early EC depends on endoscopist expertise and is inevitably subject to interobserver variability. The excellent imaging recognition ability of AI is very suitable for the diagnosis and recognition of EC, thereby reducing the missed diagnosis and helping physicians to perform endoscopy better. This paper reviews the application and relevant progress of AI in the field of endoscopic detection of early EC (including squamous cell carcinoma and adenocarcinoma), with a focus on diagnostic performance of AI to identify different types of endoscopic images, such as sensitivity and specificity.

• Artificial intelligence
• Early esophageal cancer
• Esophageal squamous cell carcinoma
• Endoscopic diagnosis

• artificial intelligence in gastroenterology
• artificial intelligence in medicine
• artificial neural networks
• clinica gastroenterology
• deep learning artificial intelligence