• Disposable electronic endoscope with Early-stage cancer auxiliary diagnosis
• Endoscopists’ gaze attention
• Polyp segmentation
• Prompt

• Humans
• Colonic Polyps/diagnostic imaging
• Artificial Intelligence
• Image Interpretation, Computer-Assisted/methods
• Algorithms

• Artificial intelligence in gastroenterology
• Capsule endoscopy
• Obscure gastrointestinal bleeding
• Therapeutic capsule endoscopy
• Wireless capsule endoscopy

• Humans
• Capsule Endoscopy/methods
• Capsule Endoscopy/trends
• Deep Learning
• Gastrointestinal Hemorrhage/diagnosis
• Gastrointestinal Diseases/diagnosis

• Colorectal cancer
• Integrated CNNs
• K-Means clustering
• Support vector machines
• Transformers

• Humans
• Colorectal Neoplasms/diagnosis
• Colorectal Neoplasms/diagnostic imaging
• Unsupervised Machine Learning
• Colonoscopy/methods
• Supervised Machine Learning
• Support Vector Machine
• Cluster Analysis
• Neural Networks, Computer
• Algorithms
• Colonic Polyps/diagnostic imaging
• Colonic Polyps/diagnosis

• (TU-DSPP-2024-14) Taif University, Taif, Saudi Arabia

• CKHK-22 datasets
• Colorectal Cancer
• Discrete Wavelet transform (DWT)
• Integrated CNNs
• Support Vector machines

• Colorectal Neoplasms/diagnosis
• Colorectal Neoplasms/pathology
• Humans
• Neural Networks, Computer
• Algorithms
• Support Vector Machine

• Deanship of Graduate Studies and Scientific Research, Taif University

• ConvMixer
• Gastrointestinal endoscopy images
• Kvasir dataset
• Spatial attention mechanism

• Dicle University

• capsule endoscopy
• detection
• diagnosis
• machine learning

• Humans
• Deep Learning
• Capsule Endoscopy/methods
• Pilot Projects
• Singapore
• Neural Networks, Computer

• artificial intelligence
• capsule endoscopy
• convolutional neural network
• deep learning
• device-assisted enteroscopy
• small bowel

• artificial intelligence
• enteroscopy
• small bowel
• video capsule endoscopy

• Humans
• Artificial Intelligence
• Gastrointestinal Tract
• Endoscopy, Gastrointestinal
• Support Vector Machine

• FPN
• Linknet
• MAnet
• PAN
• gastrointestinal tract
• semantic segmentation

• NU /RCP/SERC/12/15 The authors are thankful to the Deanship of Scientific Research and under the supervision of the Research Centre Funding program at Najran University for funding this work under the grant code

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

• Humans
• Neural Networks, Computer
• Support Vector Machine
• Diagnosis, Computer-Assisted/methods
• Colonoscopy
• Polyps
• Colorectal Neoplasms/diagnostic imaging

• Humans
• Semantics
• Neural Networks, Computer
• Wavelet Analysis
• Carcinoma

The proliferation of video capsule endoscopy (VCE) would not have been possible without continued technological improvements in imaging and locomotion. Advancements in imaging include both software and hardware improvements but perhaps the greatest software advancement in imaging comes in the form of artificial intelligence (AI). Current research into AI in VCE includes the diagnosis of tumors, gastrointestinal bleeding, Crohn’s disease, and celiac disease. Other advancements have focused on the improvement of both camera technologies and alternative forms of imaging. Comparatively, advancements in locomotion have just started to approach clinical use and include onboard controlled locomotion, which involves miniaturizing a motor to incorporate into the video capsule, and externally controlled locomotion, which involves using an outside power source to maneuver the capsule itself. Advancements in locomotion hold promise to remove one of the major disadvantages of VCE, namely, its inability to obtain targeted diagnoses. Active capsule control could in turn unlock additional diagnostic and therapeutic potential, such as the ability to obtain targeted tissue biopsies or drug delivery. With both advancements in imaging and locomotion has come a corresponding need to be better able to process generated images and localize the capsule’s position within the gastrointestinal tract. Technological advancements in computation performance have led to improvements in image compression and transfer, as well as advancements in sensor detection and alternative methods of capsule localization. Together, these advancements have led to the expansion of VCE across a number of indications, including the evaluation of esophageal and colon pathologies including esophagitis, esophageal varices, Crohn’s disease, and polyps after incomplete colonoscopy. Current research has also suggested a role for VCE in acute gastrointestinal bleeding throughout the gastrointestinal tract, as well as in urgent settings such as the emergency department, and in resource-constrained settings, such as during the COVID-19 pandemic. VCE has solidified its role in the evaluation of small bowel bleeding and earned an important place in the practicing gastroenterologist’s armamentarium. In the next few decades, further improvements in imaging and locomotion promise to open up even more clinical roles for the video capsule as a tool for non-invasive diagnosis of lumenal gastrointestinal pathologies.

• artificial intelligence
• capsule endoscopy
• computer-aided diagnosis
• hemorrhage
• lesion capsule endoscopy
• polyp
• protruded
• tumor
• ulcer

• This research was supported by Hallym University Research Fund Hallym University

• deep features
• disease classification
• features optimization
• stomach disease

• Algorithms
• Humans
• Machine Learning
• Stomach Diseases/diagnosis

• RSP-2021/206 King Saud University

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

• childhood medulloblastoma
• computer-aided diagnosis
• convolutional neural network
• histopathology
• long short term memory

• Artificial intelligence
• Convolutional neural networks
• Deep learning
• Machine learning
• Video capsule endoscopy

• Computer aided diagnosis
• Convolution neural network
• Deep learning
• Gastrointestinal (GI) diseases

• Endoscopy
• Gastrointestinal Tract/diagnostic imaging
• Humans
• Image Processing, Computer-Assisted
• Neural Networks, Computer

• Biological systems
• Cancer treatment
• Cardiac ailments
• Closed-loop control
• Control system
• Drug delivery
• GI tract
• Insulin therapy
• Neurological disorders

• Computer Aided Design (CAD)
• Convolutional Neural Network (CNN)
• Gastrointestinal Tract (GIT)
• Wireless Capsule Endoscopy (WCE)
• handcrafted features.
• machine learning

• Algorithms
• Capsule Endoscopy
• Diagnosis, Computer-Assisted
• Gastrointestinal Diseases/diagnosis
• Humans
• Machine Learning

The early diagnosis of various gastrointestinal diseases can lead to effective treatment and reduce the risk of many life-threatening conditions. Unfortunately, various small gastrointestinal lesions are undetectable during early-stage examination by medical experts. In previous studies, various deep learning–based computer-aided diagnosis tools have been used to make a significant contribution to the effective diagnosis and treatment of gastrointestinal diseases. However, most of these methods were designed to detect a limited number of gastrointestinal diseases, such as polyps, tumors, or cancers, in a specific part of the human gastrointestinal tract.

This study aimed to develop a comprehensive computer-aided diagnosis tool to assist medical experts in diagnosing various types of gastrointestinal diseases.

Our proposed framework comprises a deep learning–based classification network followed by a retrieval method. In the first step, the classification network predicts the disease type for the current medical condition. Then, the retrieval part of the framework shows the relevant cases (endoscopic images) from the previous database. These past cases help the medical expert validate the current computer prediction subjectively, which ultimately results in better diagnosis and treatment.

All the experiments were performed using 2 endoscopic data sets with a total of 52,471 frames and 37 different classes. The optimal performances obtained by our proposed method in accuracy, F1 score, mean average precision, and mean average recall were 96.19%, 96.99%, 98.18%, and 95.86%, respectively. The overall performance of our proposed diagnostic framework substantially outperformed state-of-the-art methods.

This study provides a comprehensive computer-aided diagnosis framework for identifying various types of gastrointestinal diseases. The results show the superiority of our proposed method over various other recent methods and illustrate its potential for clinical diagnosis and treatment. Our proposed network can be applicable to other classification domains in medical imaging, such as computed tomography scans, magnetic resonance imaging, and ultrasound sequences.

• artificial intelligence
• computer-aided diagnosis
• content-based medical image retrieval
• deep learning
• endoscopic video retrieval
• polyp detection

• CE
• automated abnormality detection
• gastroenterologist
• image-analysis
• medical image analysis
• non-invasive

• Artificial Intelligence
• Capsule Endoscopy
• Deep Learning
• Humans
• Neural Networks, Computer
• Retrospective Studies

Capsule endoscopy has revolutionized the management of small-bowel diseases owing to its convenience and noninvasiveness. Capsule endoscopy is a common method for the evaluation of obscure gastrointestinal bleeding, Crohn’s disease, small-bowel tumors, and polyposis syndrome. However, the laborious reading process, oversight of small-bowel lesions, and lack of locomotion are major obstacles to expanding its application. Along with recent advances in artificial intelligence, several studies have reported the promising performance of convolutional neural network systems for the diagnosis of various small-bowel lesions including erosion/ulcers, angioectasias, polyps, and bleeding lesions, which have reduced the time needed for capsule endoscopy interpretation. Furthermore, colon capsule endoscopy and capsule endoscopy locomotion driven by magnetic force have been investigated for clinical application, and various capsule endoscopy prototypes for active locomotion, biopsy, or therapeutic approaches have been introduced. In this review, we will discuss the recent advancements in artificial intelligence in the field of capsule endoscopy, as well as studies on other technological improvements in capsule endoscopy.

• Artificial intelligence
• Capsule endoscopy
• Convolutional neural network
• Locomotion

• Wireless capsule endoscopy
• classification
• feature-based techniques
• future trends
• preprocessing techniques
• segmentation techniques

• Capsule Endoscopy
• Communicable Diseases
• Computers
• Hemorrhage
• Humans
• Stomach Diseases

• Deep Learning
• Digestive System
• Machine Learning
• Neural Network

Various techniques using artificial intelligence (AI) have resulted in a significant contribution to field of medical image and video-based diagnoses, such as radiology, pathology, and endoscopy, including the classification of gastrointestinal (GI) diseases. Most previous studies on the classification of GI diseases use only spatial features, which demonstrate low performance in the classification of multiple GI diseases. Although there are a few previous studies using temporal features based on a three-dimensional convolutional neural network, only a specific part of the GI tract was involved with the limited number of classes. To overcome these problems, we propose a comprehensive AI-based framework for the classification of multiple GI diseases by using endoscopic videos, which can simultaneously extract both spatial and temporal features to achieve better classification performance. Two different residual networks and a long short-term memory model are integrated in a cascaded mode to extract spatial and temporal features, respectively. Experiments were conducted on a combined dataset consisting of one of the largest endoscopic videos with 52,471 frames. The results demonstrate the effectiveness of the proposed classification framework for multi-GI diseases. The experimental results of the proposed model (97.057% area under the curve) demonstrate superior performance over the state-of-the-art methods and indicate its potential for clinical applications.

• Artificial intelligence (AI)
• classification of multiple gastrointestinal (GI) diseases
• deep learning
• endoscopic video analysis
• residual network (ResNet) and long short-term memory (LSTM) model

• Advance MRI
• Artificial intelligence
• Computer
• Diagnosis