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

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

• Capsule Endoscopy/methods
• Humans
• Gastrointestinal Hemorrhage/etiology
• Gastrointestinal Hemorrhage/diagnostic imaging
• Gastrointestinal Hemorrhage/diagnosis
• Deep Learning
• Gastrointestinal Diseases/diagnosis
• Gastrointestinal Diseases/diagnostic imaging

• chemotherapy
• duodenal follicular lymphoma
• small bowel
• video capsule endoscopy

• 2022R1A2C2004517 National Research Foundation of Korea

• Capsule endoscopy
• Drug delivery capsule
• Intestinal disease
• Intestinal microbiome
• Intestinal monitoring
• Smart capsule robot

• gastrointestinal system
• biomedical engineering

• Capsule Endoscopy/methods
• Capsule Endoscopy/instrumentation
• Humans
• Wireless Technology/instrumentation
• Capsule Endoscopes
• Robotics/instrumentation

• 52105072 National Natural Science Foundation of China (National Science Foundation of China)

• 3D convolutional neural network
• Deep learning
• Image classification
• Wireless capsule endoscopy

• Humans
• Capsule Endoscopy/methods
• Neural Networks, Computer
• Diagnosis, Computer-Assisted

• Endoscopes
• Imaging navigation
• Nanorobots
• Surgical robots

• Humans
• Surgery, Computer-Assisted
• Endoscopy
• Imaging, Three-Dimensional

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 (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
• Classification
• Computer-assisted diagnosis
• Deep learning
• Detection
• Endoscopy

The anatomical landmarks contain the characteristics that are used to guide the gastroenterologists during the endoscopy. The expert can also ensure the completion of examination with the help of the anatomical landmarks. Automatic detection of anatomical landmarks in endoscopic video frames can be helpful for guiding the physicians during screening the gastrointestinal tract (GI).

This study presents an automatic novel method for anatomical landmark detection of GI tract from endoscopic video frames based on semisupervised deep convolutional neural network (CNN) and compares the results with supervised CNN model. We consider the anatomical landmarks from Kvasir dataset that includes 500 images for each class of Z-line, pylorus, and cecum. The resolution of these images varies from 750 × 576 up to 1920 × 1072 pixels.

Experimental results show that the supervised CNN has highly desirable performance with accuracy of 100%. Also, our proposed semisupervised CNN can compete with a slight difference similar to the CNN model. Our proposed semisupervised model trained using 1, 5, 10, and 20 percent of training data records as labeled training dataset has the average accuracy of 83%, 98%, 99%, and 99%, respectively.

The main advantage of our proposed method is achieving the high accuracy with small amount of labeled data without spending time for labeling more data. The strength of our proposed method saves the required labor, cost, and time for data labeling.

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

• Capsule Endoscopy
• Gastroscopy
• Humans
• Stomach Neoplasms

• Artificial intelligence
• Deep learning
• Machine learning
• Prognosis prediction
• Small intestinal diseases

• Artificial Intelligence
• Capsule Endoscopy
• Gastrointestinal Diseases/diagnosis
• Humans
• Intestinal Diseases/diagnosis
• Intestine, Small

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

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

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

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

• Attention
• Semi-supervised learning
• Synergic network
• WCE Image classification

• compliance rate
• conventional gastroscopy
• gastric disease
• gastric examination
• standing-type magnetically guided capsule

• Adult
• Capsule Endoscopes
• Capsule Endoscopy/instrumentation
• Feasibility Studies
• Female
• Gastroscopy
• Humans
• Magnetics
• Male
• Patient Preference
• Single-Blind Method
• Stomach Diseases/diagnosis

Background: Computer-aided disease detection schemes from wireless capsule endoscopy (WCE) videos have received great attention by the researchers for reducing physicians’ burden due to the time-consuming and risky manual review process. While single disease classification schemes are greatly dealt by the researchers in the past, developing a unified scheme which is capable of detecting multiple gastrointestinal (GI) diseases is very challenging due to the highly irregular behavior of diseased images in terms of color patterns. Method: In this paper, a computer-aided method is developed to detect multiple GI diseases from WCE videos utilizing linear discriminant analysis (LDA) based region of interest (ROI) separation scheme followed by a probabilistic model fitting approach. Commonly in training phase, as pixel-labeled images are available in small number, only the image-level annotations are used for detecting diseases in WCE images, whereas pixel-level knowledge, although a major source for learning the disease characteristics, is left unused. In view of learning the characteristic disease patterns from pixel-labeled images, a set of LDA models are trained which are later used to extract the salient ROI from WCE images both in training and testing stages. The intensity patterns of ROI are then modeled by a suitable probability distribution and the fitted parameters of the distribution are utilized as features in a supervised cascaded classification scheme. Results: For the purpose of validation of the proposed multi-disease detection scheme, a set of pixel-labeled images of bleeding, ulcer and tumor are used to extract the LDA models and then, a large WCE dataset is used for training and testing. A high level of accuracy is achieved even with a small number of pixel-labeled images. Conclusion: Therefore, the proposed scheme is expected to help physicians in reviewing a large number of WCE images to diagnose different GI diseases.

• Capsule endoscopy
• gastrointestinal disease detection
• linear discriminant analysis
• probability density function model
• support vector machine

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

• ROI selection
• anderson acceleration algorithm
• capsule endoscopy
• ensemble learning
• fixed-point iteration
• support vector machines

• algorithm
• detection
• diagnostic yield
• drug delivery
• engineering
• therapeutic capsule endoscopy

Colonoscopy is the standard device for diagnosing colorectal cancer, which develops from little lesions on the bowel wall called polyps. The Rényi entropies-based structural entropy and spatial filling factor are two scale- and resolution-independent quantities that characterize the shape of a probability distribution with the help of characteristic curves of the structural entropy–spatial filling factor map. This alternative definition of structural entropy is easy to calculate, independent of the image resolution, and does not require the calculation of neighbor statistics, unlike the other graph-based structural entropies.The distant goal of this study was to help computer aided diagnosis in finding colorectal polyps by making the Rényi entropy based structural entropy more understood. The direct goal was to determine characteristic curves that can differentiate between polyps and other structure on the picture. After analyzing the distribution of colonoscopy picture color channels, the typical structures were modeled with simple geometrical functions and the structural entropy–spatial filling factor characteristic curves were determined for these model structures for various parameter sets. A colonoscopy image analying method, i.e., the line- or column-wise scanning of the picture, was also tested, with satisfactory matching of the characteristic curve and the image.

• Rényi entropies
• colonoscopy
• computer aided diagnostics
• spatial filling factor
• structural entropy

• Capsule endoscopy
• algorithm
• detection
• diagnostic yield
• endoscopy
• engineering
• small bowel