Application of artificial intelligence in gastrointestinal disease: a narrative review.

We collected evidence on the application of artificial intelligence (AI) in gastroenterology field. The review was carried out from two aspects of endoscopic types and gastrointestinal diseases, and briefly summarized the challenges and future directions in this field. Due to the advancement of computational power and a surge of available data, a solid foundation has been laid for the growth of AI. Specifically, varied machine learning (ML) techniques have been emerging in endoscopic image analysis. To improve the accuracy and efficiency of clinicians, AI has been widely applied to gastrointestinal endoscopy. PubMed electronic database was searched using the keywords containing "AI", "ML", "deep learning (DL)", "convolution neural network", "endoscopy (such as white light endoscopy (WLE), narrow band imaging (NBI) endoscopy, magnifying endoscopy with narrow band imaging (ME-NBI), chromoendoscopy, endocytoscopy (EC), and capsule endoscopy (CE))". Search results were assessed for relevance and then used for detailed discussion. This review described the basic knowledge of AI, ML, and DL, and summarizes the application of AI in various endoscopes and gastrointestinal diseases. Finally, the challenges and directions of AI in clinical application were discussed. At present, the application of AI has solved some clinical problems, but more still needs to be done.

Zhou J ，Hu N ，Huang ZY ，Song B ，Wu CC ，Zeng FX ，Wu M ... -  《-》

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. The MEDLINE database was searched through May 2020 for relevant articles by using key words such as machine learning, deep learning, artificial intelligence, computer-aided diagnosis, convolutional neural networks, GI endoscopy, and endoscopic image analysis. References and citations of the retrieved articles were also evaluated to identify pertinent studies. The manuscript was drafted by 2 authors and reviewed in person by members of the American Society for Gastrointestinal Endoscopy Technology Committee and subsequently by the American Society for Gastrointestinal Endoscopy Governing Board. Deep learning techniques such as convolutional neural networks have been used in several areas of GI endoscopy, including colorectal polyp detection and classification, 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. The implementation of AI technologies across multiple GI endoscopic applications has the potential to transform clinical practice favorably and improve the efficiency and accuracy of current diagnostic methods.

Pannala R ，Krishnan K ，Melson J ，Parsi MA ，Schulman AR ，Sullivan S ，Trikudanathan G ，Trindade AJ ，Watson RR ，Maple JT ，Lichtenstein DR ... -  《-》

Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging.

Magnifying endoscopy with narrow-band imaging (ME-NBI) has made a huge contribution to clinical practice. However, acquiring skill at ME-NBI diagnosis of early gastric cancer (EGC) requires considerable expertise and experience. Recently, artificial intelligence (AI), using deep learning and a convolutional neural network (CNN), has made remarkable progress in various medical fields. Here, we constructed an AI-assisted CNN computer-aided diagnosis (CAD) system, based on ME-NBI images, to diagnose EGC and evaluated the diagnostic accuracy of the AI-assisted CNN-CAD system. The AI-assisted CNN-CAD system (ResNet50) was trained and validated on a dataset of 5574 ME-NBI images (3797 EGCs, 1777 non-cancerous mucosa and lesions). To evaluate the diagnostic accuracy, a separate test dataset of 2300 ME-NBI images (1430 EGCs, 870 non-cancerous mucosa and lesions) was assessed using the AI-assisted CNN-CAD system. The AI-assisted CNN-CAD system required 60 s to analyze 2300 test images. The overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the CNN were 98.7%, 98%, 100%, 100%, and 96.8%, respectively. All misdiagnosed images of EGCs were of low-quality or of superficially depressed and intestinal-type intramucosal cancers that were difficult to distinguish from gastritis, even by experienced endoscopists. The AI-assisted CNN-CAD system for ME-NBI diagnosis of EGC could process many stored ME-NBI images in a short period of time and had a high diagnostic ability. This system may have great potential for future application to real clinical settings, which could facilitate ME-NBI diagnosis of EGC in practice.

Ueyama H ，Kato Y ，Akazawa Y ，Yatagai N ，Komori H ，Takeda T ，Matsumoto K ，Ueda K ，Matsumoto K ，Hojo M ，Yao T ，Nagahara A ，Tada T ... -  《-》

Artificial intelligence in gastrointestinal endoscopy for inflammatory bowel disease: a systematic review and new horizons.

Since the advent of artificial intelligence (AI) in clinical studies, luminal gastrointestinal endoscopy has made great progress, especially in the detection and characterization of neoplastic and preneoplastic lesions. Several studies have recently shown the potential of AI-driven endoscopy for the investigation of inflammatory bowel disease (IBD). This systematic review provides an overview of the current position and future potential of AI in IBD endoscopy. A systematic search was carried out in PubMed and Scopus up to 2 December 2020 using the following search terms: artificial intelligence, machine learning, computer-aided, inflammatory bowel disease, ulcerative colitis (UC), Crohn's disease (CD). All studies on human digestive endoscopy were included. A qualitative analysis and a narrative description were performed for each selected record according to the Joanna Briggs Institute methodologies and the PRISMA statement. Of 398 identified records, 18 were ultimately included. Two-thirds of these (12/18) were published in 2020 and most were cross-sectional studies (15/18). No relevant bias at the study level was reported, although the risk of publication bias across studies cannot be ruled out at this early stage. Eleven records dealt with UC, five with CD and two with both. Most of the AI systems involved convolutional neural network, random forest and deep neural network architecture. Most studies focused on capsule endoscopy readings in CD (n = 5) and on the AI-assisted assessment of mucosal activity in UC (n = 10) for automated endoscopic scoring or real-time prediction of histological disease. AI-assisted endoscopy in IBD is a rapidly evolving research field with promising technical results and additional benefits when tested in an experimental clinical scenario. External validation studies being conducted in large and prospective cohorts in real-life clinical scenarios will help confirm the added value of AI in assessing UC mucosal activity and in CD capsule reading. Artificial intelligence for inflammatory bowel disease endoscopy Artificial intelligence (AI) is a promising technology in many areas of medicine. In recent years, AI-assisted endoscopy has been introduced into several research fields, including inflammatory bowel disease (IBD) endoscopy, with promising applications that have the potential to revolutionize clinical practice and gastrointestinal endoscopy.We have performed the first systematic review of AI and its application in the field of IBD and endoscopy.A formal process of paper selection and analysis resulted in the assessment of 18 records. Most of these (12/18) were published in 2020 and were cross-sectional studies (15/18). No relevant biases were reported. All studies showed positive results concerning the novel technology evaluated, so the risk of publication bias cannot be ruled out at this early stage.Eleven records dealt with UC, five with CD and two with both. Most studies focused on capsule endoscopy reading in CD patients (n = 5) and on AI-assisted assessment of mucosal activity in UC patients (n = 10) for automated endoscopic scoring and real-time prediction of histological disease.We found that AI-assisted endoscopy in IBD is a rapidly growing research field. All studies indicated promising technical results. When tested in an experimental clinical scenario, AI-assisted endoscopy showed it could potentially improve the management of patients with IBD.Confirmatory evidence from real-life clinical scenarios should be obtained to verify the added value of AI-assisted IBD endoscopy in assessing UC mucosal activity and in CD capsule reading.

Tontini GE ，Rimondi A ，Vernero M ，Neumann H ，Vecchi M ，Bezzio C ，Cavallaro F ... -  《-》

Evolving role of artificial intelligence in gastrointestinal endoscopy.

Artificial intelligence (AI) is a combination of different technologies that enable machines to sense, comprehend, and learn with human-like levels of intelligence. AI technology will eventually enhance human capability, provide machines genuine autonomy, and reduce errors, and increase productivity and efficiency. AI seems promising, and the field is full of invention, novel applications; however, the limitation of machine learning suggests a cautious optimism as the right strategy. AI is also becoming incorporated into medicine to improve patient care by speeding up processes and achieving greater accuracy for optimal patient care. AI using deep learning technology has been used to identify, differentiate catalog images in several medical fields including gastrointestinal endoscopy. The gastrointestinal endoscopy field involves endoscopic diagnoses and prognostication of various digestive diseases using image analysis with the help of various gastrointestinal endoscopic device systems. AI-based endoscopic systems can reliably detect and provide crucial information on gastrointestinal pathology based on their training and validation. These systems can make gastroenterology practice easier, faster, more reliable, and reduce inter-observer variability in the coming years. However, the thought that these systems will replace human decision making replace gastrointestinal endoscopists does not seem plausible in the near future. In this review, we discuss AI and associated various technological terminologies, evolving role in gastrointestinal endoscopy, and future possibilities.

Parasher G ，Wong M ，Rawat M 《-》