Artificial Intelligence
Real-time artificial intelligence for detecting focal lesions and diagnosing neoplasms of the stomach by white-light endoscopy (with videos)
White-light endoscopy (WLE) is the most pivotal tool to detect gastric cancer in an early stage. However, the skill among endoscopists varies greatly. Here, we aim to develop a deep learning–based system named ENDOANGEL-LD (lesion detection) to assist in detecting all focal gastric lesions and predicting neoplasms by WLE.Artificial intelligence for automatic diagnosis of biliary stricture malignancy status in single-operator cholangioscopy: a pilot study
The diagnosis and characterization of biliary strictures (BSs) is challenging. The introduction of digital single-operator cholangioscopy (DSOC) that allows direct visual inspection of the lesion and targeted biopsy sampling significantly improved the diagnostic yield in patients with indeterminate BSs. However, the diagnostic efficiency of DSOC remains suboptimal. Convolutional neural networks (CNNs) have shown great potential for the interpretation of medical images. We aimed to develop a CNN-based system for automatic detection of malignant BSs in DSOC images.Deep learning model for diagnosing gastric mucosal lesions using endoscopic images: development, validation, and method comparison
Endoscopic differential diagnoses of gastric mucosal lesions (benign gastric ulcer, early gastric cancer [EGC], and advanced gastric cancer) remain challenging. We aimed to develop and validate convolutional neural network–based artificial intelligence (AI) models: lesion detection, differential diagnosis (AI-DDx), and invasion depth (AI-ID; pT1a vs pT1b among EGC) models.Impact of the clinical use of artificial intelligence–assisted neoplasia detection for colonoscopy: a large-scale prospective, propensity score–matched study (with video)
Recently, the use of computer-aided detection (CADe) for colonoscopy has been investigated to improve the adenoma detection rate (ADR). We aimed to assess the efficacy of a regulatory-approved CADe in a large-scale study with high numbers of patients and endoscopists.Challenge to the “impossible”
A Dutch research team engaged in the national colorectal cancer screening program published an “amazing” result in 2020.1 As many as 60% of T1 (submucosally invasive) colorectal cancers detected during the program were misdiagnosed as adenomas by the on-site endoscopists and thus could be susceptible to inappropriate treatment intervention. Is this surprisingly low sensitivity for cancer recognition (40% in this case) reality? We would say yes, although many retrospective studies assessing advanced endoscopic modalities have suggested >90% sensitivities in predicting T1 cancers.Deep learning system compared with expert endoscopists in predicting early gastric cancer and its invasion depth and differentiation status (with videos)
We aimed to develop and validate a deep learning–based system that covers various aspects of early gastric cancer (EGC) diagnosis, including detecting gastric neoplasm, identifying EGC, and predicting EGC invasion depth and differentiation status. Herein, we provide a state-of-the-art comparison of the system with endoscopists using real-time videos in a nationwide human–machine competition.Detection of elusive polyps using a large-scale artificial intelligence system (with videos)
Colorectal cancer is a leading cause of death. Colonoscopy is the criterion standard for detection and removal of precancerous lesions and has been shown to reduce mortality. The polyp miss rate during colonoscopies is 22% to 28%. DEEP DEtection of Elusive Polyps (DEEP2) is a new polyp detection system based on deep learning that alerts the operator in real time to the presence and location of polyps. The primary outcome was the performance of DEEP2 on the detection of elusive polyps.Artificial intelligence for the detection of gastric precancerous conditions using image-enhanced endoscopy: What kind of abilities are required for application in real-world clinical practice?
The term “artificial intelligence” (AI) is used to describe machines that think like humans. It was coined by the computer scientist John McCarthy at the 1956 Dartmouth workshop in the United States. Although there is no fixed definition, AI is the simulation of human intelligence processes by machines, especially computer systems. Image recognition, a type of pattern recognition technology that uses the features of images and videos to identify objects, is one field of AI research. AI image recognition has advanced significantly since the development of convolutional neural networks (CNN), a typical method of deep learning (a type of machine learning).Artificial intelligence−enhanced white-light colonoscopy with attention guidance predicts colorectal cancer invasion depth
Endoscopic submucosal dissection (ESD) and EMR are applied in treating superficial colorectal neoplasms but are contraindicated by deeply invasive colorectal cancer (CRC). The invasion depth of neoplasms can be examined by an automated artificial intelligence (AI) system to determine the applicability of ESD and EMR.EndoBRAIN-EYE and the SUN database: important steps forward for computer-aided polyp detection
Colonoscopy is a durable cancer screening and prevention strategy in the United States and worldwide. Over the past several years, there has been increased attention toward the development and study of artificial intelligence (AI)-based computer-aided detection (CADe) systems for colonoscopy to augment polyp detection by the endoscopist during screening and surveillance colonoscopy.Artificial intelligence in the diagnosis of gastric precancerous conditions by image-enhanced endoscopy: a multicenter, diagnostic study (with video)
Gastric precancerous conditions, including gastric atrophy (GA) and intestinal metaplasia (IM), play an important role in the development of gastric cancer. Image-enhanced endoscopy (IEE) shows great potential in diagnosing gastric precancerous conditions and adenocarcinoma. In this study, a deep convolutional neural network system, named ENDOANGEL, was constructed to detect gastric precancerous conditions by IEE.Meta-analyses of machine learning in endoscopy: stacking apples and oranges
The endoscopic literature is currently overwhelmed by publications on machine learning: “the use of mathematical algorithms (often nicknamed as artificial intelligence) for capturing structure in endoscopic images.”1Artificial intelligence in the upper GI tract: the future is fast approaching
Gastric cancer remains a major cause of morbidity and mortality worldwide.1 Identifying early gastric cancer (EGC) during endoscopy is crucial because of the prognostic consequences associated with early diagnosis. Despite considerable technical developments in endoscopic practice, including magnified endoscopy (ME) with narrow-band imaging (NBI), the gastric cancer missed rate remains as high as 10%.2 Furthermore, considerable interobserver differences in the characterization of lesions identified during gastroscopy have been reported.Artificial intelligence: finding the intersection of predictive modeling and clinical utility
Artificial intelligence (AI) refers to the ability of computers to perform tasks normally reserved for human intelligence. AI is a broad concept and encompasses machine learning in which computers use data to create a binary predictive algorithm: deep learning that enhances machine learning by creating algorithms that select and weigh different variables to best predict an outcome. Convoluted neural networks are also created with interconnected “neurons” for pattern recognition, thereby weighing predictive features and learning from the data to predict outcomes.Artificial intelligence applications in EUS: the journey of a thousand miles begins with a single step
Artificial intelligence (AI) analysis of medical images is a burgeoning field of active research and industry investment. The ability for AI technology to analyze millions of pixels of imaging data for thousands of patients and to extract information beyond what is possible with the human eye and brain holds great promise.1 AI technologies are already seeing expanding use in radiology. It has been said that “AI won’t replace radiologists, but radiologists who use AI will replace radiologists who don’t.”2Guns, germs, and steel…and artificial intelligence
When we think back on the most significant turning points that have affected humanity, most will agree with the 1998 Pulitzer Prize–winning author of Guns, Germs, and Steel that the invention of guns, the discovery of microorganisms, and the invention of steel are among the key turning points.1 For recent years we might add the invention of electricity and the birth of the internet to this list. In years to come, however, we will look back and likely state that the introduction of artificial intelligence (AI) into our society was another big turning point for humanity.Artificial intelligence (computer-assisted detection) is the most recent novel approach to increase adenoma detection
Quality improvement in colorectal cancer screening and surveillance by colonoscopy is based on the assumption that an increase in adenoma detection rate (ADR) has the potential to decrease the risk of colorectal cancer. Techniques and technologies to improve ADR evaluated in a recent network meta-analysis1 included add-on devices (cap, endocuff, endoring, G-EYE), enhanced imaging techniques (chromoendoscopy, narrow-band imaging, flexible spectral imaging color enhancement, blue laser imaging), new endoscopes (full-spectrum endoscopy, extra-wide-angle-view colonoscopy, dual focus), and low-cost optimizing of existing resources (water-aided colonoscopy, second observer, dynamic position change), alone or in combination with high-definition colonoscopy or each other.Computer-aided diagnosis of esophageal cancer and neoplasms in endoscopic images: a systematic review and meta-analysis of diagnostic test accuracy
Diagnosis of esophageal cancer or precursor lesions by endoscopic imaging depends on endoscopist expertise and is inevitably subject to interobserver variability. Studies on computer-aided diagnosis (CAD) using deep learning or machine learning are on the increase. However, studies with small sample sizes are limited by inadequate statistical strength. Here, we used a meta-analysis to evaluate the diagnostic test accuracy (DTA) of CAD algorithms of esophageal cancers or neoplasms using endoscopic images.Emerging role of artificial intelligence in GI endoscopy
Artificial intelligence (AI) is a broad descriptor term that includes machine learning (ML) in which the algorithm, based on the input raw data, analyzes features in a separate dataset without specifically being programmed and delivers a specified classification (Fig. 1). Deep learning techniques such as convolutional neural networks (CNNs) are transformative ML techniques that enable rapid and accurate image discrimination and classification and as such have many applications within medicine. In gastroenterology, CNNs have been used in several areas of GI endoscopy, including colorectal polyp detection, and classification, including assessment of the presence of advanced neoplasia in colonic polyps, evaluation of histologic inflammation in endocytoscopic images obtained during colonoscopy in patients with ulcerative colitis, analysis of endoscopic images for diagnosis of Helicobacter pylori infection, detection and depth assessment of early gastric cancer, dysplasia in Barrett’s esophagus, and detection of various abnormalities in wireless capsule endoscopy images (Table 1).Identifying early gastric cancer under magnifying narrow-band images with deep learning: a multicenter study
Narrow-band imaging with magnifying endoscopy (ME-NBI) has shown advantages in the diagnosis of early gastric cancer (EGC). However, proficiency in diagnostic algorithms requires substantial expertise and experience. In this study, we aimed to develop a computer-aided diagnostic model for EGM (EGCM) to analyze and assist in the diagnosis of EGC under ME-NBI.Artificial intelligence in gastrointestinal endoscopy
Artificial intelligence (AI)-based applications have transformed several industries and are widely used in various consumer products and services. In medicine, AI is primarily being used for image classification and natural language processing and has great potential to affect image-based specialties such as radiology, pathology, and gastroenterology (GE). This document reviews the reported applications of AI in GE, focusing on endoscopic image analysis.Assessing perspectives on artificial intelligence applications to gastroenterology
Applications of artificial intelligence (AI) and machine learning (ML) in medicine are far-reaching and advancing rapidly.1 In gastroenterology, AI and, more specifically, ML have been used for a wide array of applications, such as polyp detection, histologic analysis, report generation, and reduction of fluoroscopy.2 Even with the many different types of methods used, a uniting factor is the goal of accurate automated predictive capacities for the clinical task at hand.2,3Differential diagnosis for esophageal protruded lesions using a deep convolution neural network in endoscopic images
Recent advances in deep convolutional neural networks (CNNs) have led to remarkable results in digestive endoscopy. In this study, we aimed to develop CNN-based models for the differential diagnosis of benign esophageal protruded lesions using endoscopic images acquired during real clinical settings.Artificial intelligence for polyp characterization: Don’t save on your competence!
Colonoscopy screening comes with a significant cost. If you sum up the costs due to health care personnel, endoscopy technology, facilities, and histopathology, colonoscopy is among the most expensive diagnostic procedures, with estimates comparable with whole-body CT or magnetic resonance imaging. When these costs are projected at population level, the absolute magnitude is exceptional, inasmuch as it is estimated to correspond to an annual gross expenditure of more than U.S. $775 million.1Real-time artificial intelligence–based histologic classification of colorectal polyps with augmented visualization
Artificial intelligence (AI)–based computer-aided diagnostic (CADx) algorithms are a promising approach for real-time histology (RTH) of colonic polyps. Our aim is to present a novel in situ CADx approach that seeks to increase transparency and interpretability of results by generating an intuitive augmented visualization of the model’s predicted histology over the polyp surface.
