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.

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.

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.

Role of capsule endoscopy in inflammatory bowel disease: Anything new?

Capsule endoscopy (CE) is a recently developed diagnostic method for diseases of the small bowel that is non-invasive, safe, and highly tolerable. Its role in patients with inflammatory bowel disease has been widely validated in suspected and established Crohn’s disease (CD) due to its ability to assess superficial lesions not detected by cross-sectional imaging and proximal lesions of the small bowel not evaluable by ileocolonoscopy. Because CE is a highly sensitive but less specific technique, differential diagnoses that can simulate CD must be considered, and its interpretation should be supported by other clinical and laboratory indicators. The use of validated scoring systems to characterize and estimate lesion severity (Lewis score, Capsule Endoscopy Crohn’s Disease Activity Index), as well as the standardization of the language used to define the lesions (Delphi Consensus), have reduced the interobserver variability in CE reading observed in clinical practice, allowing for the optimization of diagnoses and clinical management strategies. The appearance of the panenteric CE, the incorporation of artificial intelligence, magnetically-guided capsules, and tissue biopsies are elements that contribute to CE being a promising, unique diagnostic tool in digestive tract diseases.

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.

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.

Artificial intelligence and early esophageal cancer

The development of esophageal cancer (EC) from early to advanced stage results in a high mortality rate and poor prognosis. Advanced EC not only poses a serious threat to the life and health of patients but also places a heavy economic burden on their families and society. Endoscopy is of great value for the diagnosis of EC, especially in the screening of Barrett’s esophagus and early EC. However, at present, endoscopy has a low diagnostic rate for early tumors. In recent years, artificial intelligence (AI) has made remarkable progress in the diagnosis of digestive system tumors, providing a new model for clinicians to diagnose and treat these tumors. In this review, we aim to provide a comprehensive overview of how AI can help doctors diagnose early EC and precancerous lesions and make clinical decisions based on the predicted results. We analyze and summarize the recent research on AI and early EC. We find that based on deep learning (DL) and convolutional neural network methods, the current computer-aided diagnosis system has gradually developed from in vitro image analysis to real-time detection and diagnosis. Based on powerful computing and DL capabilities, the diagnostic accuracy of AI is close to or better than that of endoscopy specialists. We also analyze the shortcomings in the current AI research and corresponding improvement strategies. We believe that the application of AI-assisted endoscopy in the diagnosis of early EC and precancerous lesions will become possible after the further advancement of AI-related research.

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.

Artificial intelligence model on images of functional dyspepsia

BACKGROUND

Recently, it has been suggested that the duodenum may be the pathological locus of functional dyspepsia (FD). Additionally, an image-based artificial intelligence (AI) model was shown to discriminate colonoscopy images of irritable bowel syndrome from healthy subjects with an area under the curve (AUC) 0.95.

AIM

To evaluate an AI model to distinguish duodenal images of FD patients from healthy subjects.

METHODS

Duodenal images were collected from hospital records and labeled as "functional dyspepsia" or non-FD in electronic medical records. Helicobacter pylori (HP) infection status was obtained from the Japan Endoscopy Database. Google Cloud AutoML Vision was used to classify four groups: FD/HP current infection (n = 32), FD/HP uninfected (n = 35), non-FD/HP current infection (n = 39), and non-FD/HP uninfected (n = 33). Patients with organic diseases (e.g., cancer, ulcer, postoperative abdomen, reflux) and narrow-band or dye-spread images were excluded. Sensitivity, specificity, and AUC were calculated.

RESULTS

In total, 484 images were randomly selected for FD/HP current infection, FD/HP uninfected, non-FD/current infection, and non-FD/HP uninfected. The overall AUC for the four groups was 0.47. The individual AUC values were as follows: FD/HP current infection (0.20), FD/HP uninfected (0.35), non-FD/current infection (0.46), and non-FD/HP uninfected (0.74). Next, using the same images, we constructed models to determine the presence or absence of FD in the HP-infected or uninfected patients. The model exhibited a sensitivity of 58.3%, specificity of 100%, positive predictive value of 100%, negative predictive value of 77.3%, and an AUC of 0.85 in HP uninfected patients.

CONCLUSION

We developed an image-based AI model to distinguish duodenal images of FD from healthy subjects, showing higher accuracy in HP-uninfected patients. These findings suggest AI-assisted endoscopic diagnosis of FD may be feasible.

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.

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.

Role of endoscopic ultrasound in non-variceal upper gastrointestinal bleeding management

Non-variceal upper gastrointestinal bleeding (NVUGIB) is one of the challenging situations in clinical practice. Despite that gastric ulcer and duodenal ulcer are still the main causes of acute NVUGIB, there are other causes of bleeding which might not always be detected through the standard endoscopic evaluation. Standard endoscopic management of UGIB consists of injection, thermal coagulation, hemoclips, and combination therapy. However, these methods are not always successful for rebleeding prevention. Endoscopic ultrasound (EUS) has been used recently for portal hypertension management, especially in managing acute variceal bleeding. EUS has been considered a better tool to visualize the bleeding vessel in gastroesophageal variceal bleeding. There have been studies looking at the role of EUS for managing NVUGIB; however, most of them are case reports. Therefore, it is important to review back to see the evolution and innovation of endoscopic treatment for NVUGIB and the role of EUS for possibility to replace the standard endoscopic haemostasis management in daily practice.

Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease

Assessment of endoscopic disease activity can be difficult in patients with inflammatory bowel disease (IBD) [comprises Crohn's disease (CD) and ulcerative colitis (UC)]. Endoscopic assessment is currently the foundation of disease evaluation and the grading is pivotal for the initiation of certain treatments. Yet, disharmony is found among experts; even when reassessed by the same expert. Some studies have demonstrated that the evaluation is no better than flipping a coin. In UC, the greatest achieved consensus between physicians when assessing endoscopic disease activity only reached a Kappa value of 0.77 (or 77% agreement adjustment for chance/accident). This is unsatisfactory when dealing with patients at risk of surgery or disease progression without proper care. Lately, across all medical specialities, computer assistance has become increasingly interesting. Especially after the emanation of machine learning – colloquially referred to as artificial intelligence (AI). Compared to other data analysis methods, the strengths of AI lie in its capability to derive complex models from a relatively small dataset and its ability to learn and optimise its predictions from new inputs. It is therefore evident that with such a model, one hopes to be able to remove inconsistency among humans and standardise the results across educational levels, nationalities and resources. This has manifested in a handful of studies where AI is mainly applied to capsule endoscopy in CD and colonoscopy in UC. However, due to its recent place in IBD, there is a great inconsistency between the results, as well as the reporting of the same. In this opinion review, we will explore and evaluate the method and results of the published studies utilising AI within IBD (with examples), and discuss the future possibilities AI can offer within IBD.

Impact of endoscopic ultrasound elastography in pancreatic lesion evaluation

Pancreatic malignancy still becomes a major global problem and is considered as one of the most lethal cancers in the field of gastroenterology. Most patients come in the late stage of the disease due to organ’s location, and until now the treatment result is still far away from satisfaction. Early detection is still the main key for good, prolonged survival. However, discerning from other types of tumor sometimes is not easy. Endoscopic ultrasound (EUS) is still the best tool for pancreatic assessment, whereas fine-needle aspiration biopsy (FNAB) is considered as the cornerstone for further management of pancreatic malignancy. Several conditions have become a concern for EUS-FNAB procedure, such as risk of bleeding, pancreatitis, and even needle track-seeding. Recently, an artificial intelligence innovation, such as EUS elastography has been developed to improve diagnostic accuracy in pancreatic lesions evaluation. Studies have shown the promising results of EUS elastography in improving diagnostic accuracy, as well as discerning from other tumor types. However, more studies are still needed with further considerations, such as adequate operator training, expertise, availability, and its cost-effectiveness in comparison to other imaging options.

Early gastrointestinal cancer: The application of artificial intelligence

Early gastrointestinal (GI) cancer has been the core of clinical endoscopic work. Its early detection and treatment are tightly associated with patients’ prognoses. As a novel technology, artificial intelligence has been improved and applied in the field of endoscopy. Studies on detection, diagnosis, risk, and prognosis evaluation of diseases in the GI tract have been in development, including precancerous lesions, adenoma, early GI cancers, and advanced GI cancers. In this review, research on esophagus, stomach, and colon was concluded, and associated with the process from precancerous lesions to early GI cancer, such as from Barrett’s esophagus to early esophageal cancer, from dysplasia to early gastric cancer, and from adenoma to early colonic cancer. A status quo of research on early GI cancers and artificial intelligence was provided.

Robotic pancreaticoduodenectomy: Where do we stand?

Pancreaticoduodenectomy (PD) is a complex operation accompanied by significant morbidity rates. Due to this complexity, the transition to minimally invasive PD has lagged behind other abdominal surgical operations. The safety, feasibility, favorable post-operative outcomes of robotic PD have been suggested by multiple studies. Compared to open surgery and other minimally invasive techniques such as laparoscopy, robotic PD offers satisfactory outcomes, with a non-inferior risk of adverse events. Trends of robotic PD have been on rise with centers substantially increasing the number the operation performed. Although promising, findings on robotic PD need to be corroborated in prospective trials.

Artificial intelligence in polyp detection - where are we and where are we headed?

The goal of artificial intelligence in colonoscopy is to improve adenoma detection rate and reduce interval colorectal cancer. Artificial intelligence in polyp detection during colonoscopy has evolved tremendously over the last decade mainly due to the implementation of neural networks. Computer aided detection (CADe) utilizing neural networks allows real time detection of polyps and adenomas. Current CADe systems are built in single centers by multidisciplinary teams and have only been utilized in limited clinical research studies. We review the most recent prospective randomized controlled trials here. These randomized control trials, both non-blinded and blinded, demonstrated increase in adenoma and polyp detection rates when endoscopists used CADe systems vs standard high definition colonoscopes. Increase of polyps and adenomas detected were mainly small and sessile in nature. CADe systems were found to be safe with little added time to the overall procedure. Results are promising as more CADe have shown to have ability to increase accuracy and improve quality of colonoscopy. Overall limitations included selection bias as all trials built and utilized different CADe developed at their own institutions, non-blinded arms, and question of external validity.

Role of artificial intelligence in colorectal cancer

The sphere of artificial intelligence (AI) is ever expanding. Applications for clinical practice have been emerging over recent years. Although its uptake has been most prominent in endoscopy, this represents only one aspect of holistic patient care. There are a multitude of other potential avenues in which gastrointestinal care may be involved. We aim to review the role of AI in colorectal cancer as a whole. We performed broad scoping and focused searches of the applications of AI in the field of colorectal cancer. All trials including qualitative research were included from the year 2000 onwards. Studies were grouped into pre-operative, intra-operative and post-operative aspects. Pre-operatively, the major use is with endoscopic recognition. Colonoscopy has embraced the use for human derived classifications such as Narrow-band Imaging International Colorectal Endoscopic, Japan Narrow-band Imaging Expert Team, Paris and Kudo. However, novel detection and diagnostic methods have arisen from advances in AI classification. Intra-operatively, adjuncts such as image enhanced identification of structures and assessment of perfusion have led to improvements in clinical outcomes. Post-operatively, monitoring and surveillance have taken strides with potential socioeconomic and environmental savings. The uses of AI within the umbrella of colorectal surgery are multiple. We have identified existing technologies which are already augmenting cancer care. The future applications are exciting and could at least match, if not surpass human standards.

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.