Artificial intelligence as a means to improve recognition of gastrointestinal angiodysplasia in video capsule endoscopy

Gastrointestinal angiodysplasia (GIAD) is defined as the pathological process where blood vessels, typically venules and capillaries, become engorged, tortuous and thin walled – which then form arteriovenous connections within the mucosal and submucosal layers of the gastrointestinal tract. GIADs are a significant cause of gastrointestinal bleeding and are the main cause for suspected small bowel bleeding. To make the diagnosis, gastroenterologists rely on the use of video capsule endoscopy (VCE) to “target” GIAD. However, the use of VCE can be cumbersome secondary to reader fatigue, suboptimal preparation, and difficulty in distinguishing images. The human eye is imperfect. The same capsule study read by two different readers are noted to have miss rates like other forms of endoscopy. Artificial intelligence (AI) has been a means to bridge the gap between human imperfection and recognition of GIAD. The use of AI in VCE have shown that detection has improved, however the other burdens and limitations still need to be addressed. The use of AI for the diagnosis of GIAD shows promise and the changes needed to enhance the current practice of VCE are near.

Utility of artificial intelligence in colonoscopy

Colorectal cancer is one of the major causes of death worldwide. Colonoscopy is the most important tool that can identify neoplastic lesion in early stages and resect it in a timely manner which helps in reducing mortality related to colorectal cancer. However, the quality of colonoscopy findings depends on the expertise of the endoscopist and thus the rate of missed adenoma or polyp cannot be controlled. It is desirable to standardize the quality of colonoscopy by reducing the number of missed adenoma/polyps. Introduction of artificial intelligence (AI) in the field of medicine has become popular among physicians nowadays. The application of AI in colonoscopy can help in reducing miss rate and increasing colorectal cancer detection rate as per recent studies. Moreover, AI assistance during colonoscopy has also been utilized in patients with inflammatory bowel disease to improve diagnostic accuracy, assessing disease severity and predicting clinical outcomes. We conducted a literature review on the available evidence on use of AI in colonoscopy. In this review article, we discuss about the principles, application, limitations, and future aspects of AI in colonoscopy.

Use of artificial intelligence in endoscopic ultrasound evaluation of pancreatic pathologies

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.

Current situation and prospect of artificial intelligence application in endoscopic diagnosis of Helicobacter pylori infection

With the appearance and prevalence of deep learning, artificial intelligence (AI) has been broadly studied and made great progress in various fields of medicine, including gastroenterology. Helicobacter pylori (H. pylori), closely associated with various digestive and extradigestive diseases, has a high infection rate worldwide. Endoscopic surveillance can evaluate H. pylori infection situations and predict the risk of gastric cancer, but there is no objective diagnostic criteria to eliminate the differences between operators. The computer-aided diagnosis system based on AI technology has demonstrated excellent performance for the diagnosis of H. pylori infection, which is superior to novice endoscopists and similar to skilled. Compared with the visual diagnosis of H. pylori infection by endoscopists, AI possesses voluminous advantages: High accuracy, high efficiency, high quality control, high objectivity, and high-effect teaching. This review summarizes the previous and recent studies on AI-assisted diagnosis of H. pylori infection, points out the limitations, and puts forward prospect for future research.

Progress and prospects of artificial intelligence in colonoscopy

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.

Application of convolutional neural network in detecting and classifying gastric cancer

Gastric cancer (GC) is the fifth most common cancer in the world, and at present, esophagogastroduodenoscopy is recognized as an acceptable method for the screening and monitoring of GC. Convolutional neural networks (CNNs) are a type of deep learning model and have been widely used for image analysis. This paper reviews the application and prospects of CNNs in detecting and classifying GC, aiming to introduce a computer-aided diagnosis system and to provide evidence for subsequent studies.

Colonoscopy and artificial intelligence: Bridging the gap or a gap needing to be bridged?

Research in artificial intelligence (AI) in gastroenterology has increased over the last decade. Colonoscopy represents the most widely published field with regards to its use in gastroenterology. Most studies to date center on polyp detection and characterization, as well as real-time evaluation of adequacy of mucosal exposure for inspection. This review article discusses how advances in AI has bridged certain gaps in colonoscopy. In addition, the gaps formed with the development of AI that currently prevent its routine use in colonoscopy will be explored.

Application of deep learning in image recognition and diagnosis of gastric cancer

In recent years, artificial intelligence has been extensively applied in the diagnosis of gastric cancer based on medical imaging. In particular, using deep learning as one of the mainstream approaches in image processing has made remarkable progress. In this paper, we also provide a comprehensive literature survey using four electronic databases, PubMed, EMBASE, Web of Science, and Cochrane. The literature search is performed until November 2020. This article provides a summary of the existing algorithm of image recognition, reviews the available datasets used in gastric cancer diagnosis and the current trends in applications of deep learning theory in image recognition of gastric cancer. covers the theory of deep learning on endoscopic image recognition. We further evaluate the advantages and disadvantages of the current algorithms and summarize the characteristics of the existing image datasets, then combined with the latest progress in deep learning theory, and propose suggestions on the applications of optimization algorithms. Based on the existing research and application, the label, quantity, size, resolutions, and other aspects of the image dataset are also discussed. The future developments of this field are analyzed from two perspectives including algorithm optimization and data support, aiming to improve the diagnosis accuracy and reduce the risk of misdiagnosis.

Application of artificial intelligence to endoscopy on common gastrointestinal benign diseases

Artificial intelligence (AI) has been widely involved in every aspect of healthcare in the preclinical stage. In the digestive system, AI has been trained to assist auxiliary examinations including histopathology, endoscopy, ultrasonography, computerized tomography, and magnetic resonance imaging in detection, diagnosis, classification, differentiation, prognosis, and quality control. In the field of endoscopy, the application of AI, such as automatic detection, diagnosis, classification, and invasion depth, in early gastrointestinal (GI) cancers has received wide attention. There is a paucity of studies of AI application on common GI benign diseases based on endoscopy. In the review, we provide an overview of AI applications to endoscopy on common GI benign diseases including in the esophagus, stomach, intestine, and colon. It indicates that AI will gradually become an indispensable part of normal endoscopic detection and diagnosis of common GI benign diseases as clinical data, algorithms, and other related work are constantly repeated and improved.

5G mobile communication applications for surgery: An overview of the latest literature

Fifth-generation wireless network, 5G, is expected to bring surgery to a next level. Remote surgery and telementoring could be enabled and be brought into routine medical care due to 5G characteristics, such as extreme high bandwidth, ultra-short latency, multiconnectivity, high mobility, high availability, and high reliability. This work explores the benefits, applications and demands of 5G for surgery. Therefore, the development of previous surgical procedures from using older networks to 5G is outlined. The current state of 5G in surgical research studies is discussed, as well as future aspects and requirements of 5G in surgery are presented.

Artificial intelligence assisted endocytoscopy: A novel eye in endoscopy

Over the past few years, emerging new approaches in endoscopic imaging technologies facilitate a high-quality assessment of lesions found in the gastrointestinal (GI) tract. Endocytoscopy (EC), as a novel tool in endoscopy, aids the more accurate evaluation of superficial mucosal surface. This review article aims to represent the most relevant information related to the latest EC technology and its clinical application in the lower GI tract diagnostic. We discuss EC-computer-aided diagnosis capability to differentiate between non-neoplastic and neoplastic lesion that offers a closer look to in-vivo assessment and diagnosis of cancerous tissue. Nevertheless, artificial-assisted EC diagnostics could also be employed with benefits in patients with inflammatory bowel disease (IBD) by accurately highlighting the presence of mucosal injury. In our review we included those studies comprising data about colonoscopy with narrow banding imaging and computer-aided diagnosis, as well as EC. Last but not least, artificial-assisted EC facilitates in-vivo diagnosis of the lower GI tract and may, in the future, remodel the field of in-vivo endoscopic diagnosis of colorectal lesions, representing another step towards the so-called optical biopsy.

Understanding deep learning in capsule endoscopy: Can artificial intelligence enhance clinical practice?

Wireless capsule endoscopy (WCE) enables physicians to examine the gastrointestinal tract by transmitting images wirelessly from a disposable capsule to a data recorder. Although WCE is the least invasive endoscopy technique for diagnosing gastrointestinal disorders, interpreting a WCE study requires significant time effort and training. Analysis of images by artificial intelligence, through advances such as machine or deep learning, has been increasingly applied to medical imaging. There has been substantial interest in using deep learning to detect various gastrointestinal disorders based on WCE images. This article discusses basic knowledge of deep learning, applications of deep learning in WCE, and the implementation of deep learning model in a clinical setting. We anticipate continued research investigating the use of deep learning in interpreting WCE studies to generate predictive algorithms and aid in the diagnosis of gastrointestinal disorders.

Artificial intelligence in Barrett’s esophagus: A renaissance but not a reformation

Esophageal cancer remains as one of the top ten causes of cancer-related death in the United States. The primary risk factor for esophageal adenocarcinoma is the presence of Barrett’s esophagus (BE). Currently, identification of early dysplasia in BE patients requires an experienced endoscopist performing a diagnostic endoscopy with random 4-quadrant biopsies taken every 1-2 cm using appropriate surveillance intervals. Currently, there is significant difficulty for endoscopists to distinguish different forms of dysplastic BE as well as early adenocarcinoma due to subtleties in mucosal texture and color. This obstacle makes taking multiple random biopsies necessary for appropriate surveillance and diagnosis. Recent advances in artificial intelligence (AI) can assist gastroenterologists in identifying areas of likely dysplasia within identified BE and perform targeted biopsies, thus decreasing procedure time, sedation time, and risk to the patient along with maximizing potential biopsy yield. Though using AI represents an exciting frontier in endoscopic medicine, recent studies are limited by selection bias, generalizability, and lack of robustness for universal use. Before AI can be reliably employed for BE in the future, these issues need to be fully addressed and tested in prospective, randomized trials. Only after that is achieved, will the benefit of AI in those with BE be fully realized.

Application of convolutional neural networks for computer-aided detection and diagnosis in gastrointestinal pathology: A simplified exposition for an endoscopist

The application of artificial intelligence (AI), especially machine learning or deep learning (DL), is advancing at a rapid pace. The need for increased accuracy at endoscopic visualisation of the gastrointestinal (GI) tract is also growing. Convolutional neural networks (CNNs) are one such model of DL, which have been used for endoscopic image analysis, whereby computer-aided detection and diagnosis of GI pathology can be carried out with increased scrupulousness. In this article, we briefly focus on the framework of the utilisation of CNNs in GI endoscopy along with a short review of a few published AI-based articles in the last 4 years.

Techniques to integrate artificial intelligence systems with medical information in gastroenterology

Gastrointestinal (GI) endoscopy is the central element in contemporary gastroenterology as it provides direct evidence to guide targeted therapy. To increase the accuracy of GI endoscopy and to reduce human-related errors, artificial intelligence (AI) has been applied in GI endoscopy, which has been proved to be effective in diagnosing and treating numerous diseases. Therefore, we review current research on the efficacy of AI-assisted GI endoscopy in order to assess its functions, advantages and how the design can be improved.

Emerging artificial intelligence applications in gastroenterology: A review of the literature

Artificial intelligence (AI) allows machines to provide disruptive value in several industries and applications. Applications of AI techniques, specifically machine learning and more recently deep learning, are arising in gastroenterology. Computer-aided diagnosis for upper gastrointestinal endoscopy has growing attention for automated and accurate identification of dysplasia in Barrett’s esophagus, as well as for the detection of early gastric cancers (GCs), therefore preventing esophageal and gastric malignancies. Besides, convoluted neural network technology can accurately assess Helicobacter pylori (H. pylori) infection during standard endoscopy without the need for biopsies, thus, reducing gastric cancer risk. AI can potentially be applied during colonoscopy to automatically discover colorectal polyps and differentiate between neoplastic and non-neoplastic ones, with the possible ability to improve adenoma detection rate, which changes broadly among endoscopists performing screening colonoscopies. In addition, AI permits to establish the feasibility of curative endoscopic resection of large colonic lesions based on the pit pattern characteristics. The aim of this review is to analyze current evidence from the literature, supporting recent technologies of AI both in upper and lower gastrointestinal diseases, including Barrett's esophagus, GC, H. pylori infection, colonic polyps and colon cancer.