• 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

• Artificial intelligence
• Capsule endoscopy
• Digestive endoscopy

Gastrointestinal (GI) complications frequently necessitate intensive care unit (ICU) admission. Additionally, critically ill patients also develop GI complications requiring further diagnostic and therapeutic interventions. However, these patients form a vulnerable group, who are at risk for developing side effects and complications. Every effort must be made to reduce invasiveness and ensure safety of interventions in ICU patients. Artificial intelligence (AI) is a rapidly evolving technology with several potential applications in healthcare settings. ICUs produce a large amount of data, which may be employed for creation of AI algorithms, and provide a lucrative opportunity for application of AI. However, the current role of AI in these patients remains limited due to lack of large-scale trials comparing the efficacy of AI with the accepted standards of care.

• Artificial intelligence
• Critical care
• Gastroenterology
• Hepatology
• Intensive care unit
• Machine learning

• clinical medicine
• deep learning
• internal medicine
• machine learning
• neural networks

• bleeding classification
• bleeding detection
• bleeding recognition
• bleeding segmentation
• capsule endoscopy
• wireless capsule endoscopy

• Humans
• Capsule Endoscopy
• Computers
• Computer Systems
• Algorithms
• Hemorrhage

• MMUE/220021 (TM R&D) and FRGS/1/2022/TK0/MMU/02/13 (FRGS) Multimedia University

• HarDNet
• attention mechanism
• colon polyps
• deep learning
• image segmentation

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

• CNN
• HyperKvasir dataset
• gastrointestinal diseases
• squeeze and excitation

• 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.

• Humans
• Pneumonectomy/adverse effects
• Thoracic Surgery, Video-Assisted/adverse effects
• Lung Neoplasms/surgery
• Blood Stains
• Algorithms
• Computers
• Postoperative Complications/epidemiology
• Postoperative Complications/surgery
• Retrospective Studies
• Length of Stay

• National Key Research and Development Program
• Beijing Natural Science Foundation
• National Natural Science Foundation of China
• Strategic Research and Consulting Project of Chinese Academy of Engineering
• Peking University Baidu Fund
• Advanced Institute of Information Technology, Peking University, Zhejiang Province

In medical imaging, the detection and classification of stomach diseases are challenging due to the resemblance of different symptoms, image contrast, and complex background. Computer-aided diagnosis (CAD) plays a vital role in the medical imaging field, allowing accurate results to be obtained in minimal time. This article proposes a new hybrid method to detect and classify stomach diseases using endoscopy videos. The proposed methodology comprises seven significant steps: data acquisition, preprocessing of data, transfer learning of deep models, feature extraction, feature selection, hybridization, and classification. We selected two different CNN models (VGG19 and Alexnet) to extract features. We applied transfer learning techniques before using them as feature extractors. We used a genetic algorithm (GA) in feature selection, due to its adaptive nature. We fused selected features of both models using a serial-based approach. Finally, the best features were provided to multiple machine learning classifiers for detection and classification. The proposed approach was evaluated on a personally collected dataset of five classes, including gastritis, ulcer, esophagitis, bleeding, and healthy. We observed that the proposed technique performed superbly on Cubic SVM with 99.8% accuracy. For the authenticity of the proposed technique, we considered these statistical measures: classification accuracy, recall, precision, False Negative Rate (FNR), Area Under the Curve (AUC), and time. In addition, we provided a fair state-of-the-art comparison of our proposed technique with existing techniques that proves its worthiness.

• deep learning
• endoscopy videos
• genetic algorithm
• stomach diseases

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 has revolutionized the management of patients with obscure gastrointestinal bleeding. Nevertheless, reading capsule endoscopy images is time-consuming and prone to overlooking significant lesions, thus limiting its diagnostic yield. We aimed to create a deep learning algorithm for automatic detection of blood and hematic residues in the enteric lumen in capsule endoscopy exams.

A convolutional neural network was developed based on a total pool of 22,095 capsule endoscopy images (13,510 images containing luminal blood and 8,585 of normal mucosa or other findings). A training dataset comprising 80% of the total pool of images was defined. The performance of the network was compared to a consensus classification provided by 2 specialists in capsule endoscopy. Subsequently, we evaluated the performance of the network using an independent validation dataset (20% of total image pool), calculating its sensitivity, specificity, accuracy, and precision.

Our convolutional neural network detected blood and hematic residues in the small bowel lumen with an accuracy and precision of 98.5 and 98.7%, respectively. The sensitivity and specificity were 98.6 and 98.9%, respectively. The analysis of the testing dataset was completed in 24 s (approximately 184 frames/s).

We have developed an artificial intelligence tool capable of effectively detecting luminal blood. The development of these tools may enhance the diagnostic accuracy of capsule endoscopy when evaluating patients presenting with obscure small bowel bleeding.

• deep learning
• endoscopy
• machine learning
• radiology
• ultrasonography

• 21K15951 Japan Society for the Promotion of Science

• Bi-directional gated recurrent unit
• class balance strategy
• conditional random field
• sleep staging
• time attention mechanism

The use of artificial intelligence-based tools is regarded as a promising approach to increase clinical efficiency in diagnostic imaging, improve the interpretability of results, and support decision-making for the detection and prevention of diseases. Radiology, endoscopy and pathology images are suitable for deep-learning analysis, potentially changing the way care is delivered in gastroenterology. The aim of this review is to examine the key aspects of different neural network architectures used for the evaluation of gastrointestinal conditions, by discussing how different models behave in critical tasks, such as lesion detection or characterization (i.e. the distinction between benign and malignant lesions of the esophagus, the stomach and the colon). To this end, we provide an overview on recent achievements and future prospects in deep learning methods applied to the analysis of radiology, endoscopy and histologic whole-slide images of the gastrointestinal tract.

• Artificial intelligence
• Machine learning
• Radiology
• Endoscopy
• Pathology
• Gastroenterology

• Artificial Intelligence
• Diagnostic Imaging
• Gastroenterology
• Gastrointestinal Diseases/diagnostic imaging
• Humans
• Neural Networks, Computer

• artificial intelligence
• capsule endoscopy
• deep learning

Artificial intelligence (AI) is a branch of computer science. As a new technological science, it mainly develops and expands human intelligence through the research of intelligence theory, methods and technology. In the medical field, AI has bright application prospects (for example: imaging, diagnosis and treatment). The exploration of robotic gastroscopy and colonoscopy systems is not only a bold attempt, but also an inevitable trend of AI in the development of digestive endoscopy in the future. Based on the current research findings, this article summarizes the research progress of colonoscopy, and looking forward for the application of AI in colonoscopy.

• Artificial intelligence
• Colonoscopy
• Application
• Gastrointestinal
• Endoscopy

• BDFP
• biliverdin
• engineering bacteria
• far-red light
• heme
• phycobiliprotein

• Artificial Intelligence
• Deep Learning
• Gastroenterology
• Hepatic disease
• Machine Learning
• Pancreatic adenocarcinoma

• Artificial intelligence
• Automatic reading
• CNN-based algorithm
• Capsule endoscopy
• Deep-learning

The applicability of artificial intelligence (AI) in gastroenterology is a hot topic because of its disruptive nature. Capsule endoscopy plays an important role in several areas of digestive pathology, namely in the investigation of obscure hemorrhagic lesions and the management of inflammatory bowel disease. Therefore, there is growing interest in the use of AI in capsule endoscopy. Several studies have demonstrated the enormous potential of using convolutional neural networks in various areas of capsule endoscopy. The exponential development of the usefulness of AI in capsule endoscopy requires consideration of its medium- and long-term impact on clinical practice. Indeed, the advent of deep learning in the field of capsule endoscopy, with its evolutionary character, could lead to a paradigm shift in clinical activity in this setting. In this review, we aim to illustrate the state of the art of AI in the field of capsule endoscopy.

• Capsule endoscopy
• artificial intelligence
• deep learning
• gastroenterology
• machine learning

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

• Video capsule endoscopy (VCE)
• artificial intelligence (AI)
• deep learning (DL)
• software enhancement

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

• Artificial Intelligence
• Diagnostic Imaging
• Endoscopy
• Gastroenterology
• Humans
• Machine Learning

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

• endoscopy
• gastrointestinal bleeding
• guidelines
• hemostasis
• non-steroidal anti-inflammatory drugs
• risk factors

• 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

Artificial intelligence (AI) enables machines to provide unparalleled value in a myriad of industries and applications. In recent years, researchers have harnessed artificial intelligence to analyze large-volume, unstructured medical data and perform clinical tasks, such as the identification of diabetic retinopathy or the diagnosis of cutaneous malignancies. Applications of artificial intelligence techniques, specifically machine learning and more recently deep learning, are beginning to emerge in gastrointestinal endoscopy. The most promising of these efforts have been in computer-aided detection and computer-aided diagnosis of colorectal polyps, with recent systems demonstrating high sensitivity and accuracy even when compared to expert human endoscopists. AI has also been utilized to identify gastrointestinal bleeding, to detect areas of inflammation, and even to diagnose certain gastrointestinal infections. Future work in the field should concentrate on creating seamless integration of AI systems with current endoscopy platforms and electronic medical records, developing training modules to teach clinicians how to use AI tools, and determining the best means for regulation and approval of new AI technology.

• Artificial intelligence
• Machine learning
• Gastrointestinal endoscopy
• Computer-assisted decision making
• Computer-aided detection
• Colonic polyps
• Colonoscopy
• Computer-aided diagnosis
• Colorectal adenocarcinoma

Sleep scoring is an important step in the treatment of sleep disorders. Manual annotation of sleep stages is time-consuming and experience-relevant and, therefore, needs to be done using machine learning techniques.

Sleep-EDF polysomnography was used in this study as a dataset. Support vector machines and artificial neural network performance were compared in sleep scoring using wavelet tree features and neighborhood component analysis.

Neighboring component analysis as a combination of linear and non-linear feature selection method had a substantial role in feature dimension reduction. Artificial neural network and support vector machine achieved 90.30% and 89.93% accuracy, respectively.

Similar to the state of the art performance, the introduced method in the present study achieved an acceptable performance in sleep scoring. Furthermore, its performance can be enhanced using a technique combined with other techniques in feature generation and dimension reduction. It is hoped that, in the future, intelligent techniques can be used in the process of diagnosing and treating sleep disorders.

• Artificial neural network
• Machine learning
• Neighboring component analysis
• Sleep scoring
• Support vector machine
• Wavelet tree analysis

• Chromoendoscopy
• Co-occurrence matrix
• Filter bank
• Gabor filter
• Stomach cancer
• Texture analysis

• Algorithms
• Computer Simulation
• Diagnosis, Computer-Assisted
• Early Detection of Cancer
• Gastroscopy/methods
• Humans
• Image Enhancement/methods
• Image Interpretation, Computer-Assisted/methods
• Sensitivity and Specificity
• Stomach Neoplasms/diagnosis
• Support Vector Machine

• capsule endoscopy
• clinical implications
• obscure gastrointestinal bleeding
• small bowel

• Academic Medical Centers
• Adolescent
• Adult
• Aged
• Aged, 80 and over
• Capsule Endoscopy/methods
• Capsule Endoscopy/statistics & numerical data
• Child
• Female
• Humans
• Intestinal Diseases/diagnostic imaging
• Intestine, Small/diagnostic imaging
• Logistic Models
• Male
• Middle Aged
• Retrospective Studies
• Young Adult

Wireless capsule endoscopy (WCE) is the most advanced technology to visualize whole gastrointestinal (GI) tract in a non-invasive way. But the major disadvantage here, it takes long reviewing time, which is very laborious as continuous manual intervention is necessary. In order to reduce the burden of the clinician, in this paper, an automatic bleeding detection method for WCE video is proposed based on the color histogram of block statistics, namely CHOBS. A single pixel in WCE image may be distorted due to the capsule motion in the GI tract. Instead of considering individual pixel values, a block surrounding to that individual pixel is chosen for extracting local statistical features. By combining local block features of three different color planes of RGB color space, an index value is defined. A color histogram, which is extracted from those index values, provides distinguishable color texture feature. A feature reduction technique utilizing color histogram pattern and principal component analysis is proposed, which can drastically reduce the feature dimension. For bleeding zone detection, blocks are classified using extracted local features that do not incorporate any computational burden for feature extraction. From extensive experimentation on several WCE videos and 2300 images, which are collected from a publicly available database, a very satisfactory bleeding frame and zone detection performance is achieved in comparison to that obtained by some of the existing methods. In the case of bleeding frame detection, the accuracy, sensitivity, and specificity obtained from proposed method are 97.85%, 99.47%, and 99.15%, respectively, and in the case of bleeding zone detection, 95.75% of precision is achieved. The proposed method offers not only low feature dimension but also highly satisfactory bleeding detection performance, which even can effectively detect bleeding frame and zone in a continuous WCE video data.

• Bleeding detection
• bleeding zone
• color histogram
• feature extraction
• wireless capsule endoscopy