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Introduction: 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. Methods: 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. Results: 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. Discussion: 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. Plain language summary: 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. [ABSTRACT FROM AUTHOR]

Background: Although colonoscopy is the gold standard for colorectal cancer screening, colonic looping may make complete colonoscopy challenging. Commonly available stiffening device colonoscopy has been described as helpful but not effective enough to prevent looping. In this context the effect on cecal intubation time and rate was described differently in various studies and in some studies had no impact on cecal intubation time at all. The aim of this study was to evaluate whether a novel colonoscope with gradual stiffness (Fujifilm EC760R-V/I- flexibility adjuster, Tokyo, Japan) using four significantly different grades of stiffness can be an alternative to established devices in terms of loop prevention, cecal intubation rate and time, adverse events, and patient/examiner satisfaction. Methods: Consecutive patients without previous colorectal surgery were analyzed retrospectively. Colonoscopy was performed with the new colonoscope and performance characteristics, including time to cecum, withdrawal time, total examination time, and patient and endoscopist satisfaction were recorded. Results: Among 180 consecutive procedures, 98.3% of examinations were complete to the cecum. The endoscopic flexibility adjuster was used in 150 of 180 cases (83.3%). Overall, the device was scored by the examiner as helpful to prevent looping in 146 of the 150 cases (97.7%). Mean cecal intubation time was 6.5 min, with 35% of examination performed in under 5 min with a mean withdrawal time of 7 min. Mean total examination time was 18 min. Patient satisfaction was rated as high in all examinations performed. Conclusion: The new flexibility adjuster colonoscope was shown to be helpful in loop prevention, allowed for fast and successful cecal intubation, and led to a high rate of patients satisfaction. [ABSTRACT FROM AUTHOR]

Artificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imaging diagnosis. The possibility to amass large data to refine algorithms makes adoption of artificial intelligence into global practice a potential reality. Initial studies in luminal endoscopy involve machine learning and are retrospective. Improvement in diagnostic performance is appreciable through the adoption of deep learning. Research foci in the upper gastrointestinal tract include the diagnosis of neoplasia, including Barrett's, squamous cell and gastric where prospective and real-time artificial intelligence studies have been completed demonstrating a benefit of artificial intelligence–augmented endoscopy. Deep learning applied to small bowel capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. Prospective evaluation including the first randomised trial has been performed in the colon, demonstrating improved polyp and adenoma detection rates; however, these appear to be relevant to small polyps. There are potential additional roles of artificial intelligence relevant to improving the quality of endoscopic examinations, training and triaging of referrals. Further large-scale, multicentre and cross-platform validation studies are required for the robust incorporation of artificial intelligence–augmented diagnostic luminal endoscopy into our routine clinical practice. [ABSTRACT FROM AUTHOR]

Keywords: Hereditary diffuse gastric cancer; CDH1; Signet ring cell carcinoma EN Hereditary diffuse gastric cancer CDH1 Signet ring cell carcinoma 1 2 2 05/18/20 20200428 NES 200428 Introduction Inherited CDH1 pathogenic variants cause Hereditary Diffuse Gastric Cancer (HDGC) syndrome, a disease with very poor prognosis.[1] Carriers of such variants present an estimated lifetime risk of gastric signet ring cell carcinoma (SRCC) of 70% in men and 56% in women, by the age of 80 years old.[2] Although risk reduction gastrectomy is the indicated procedure for disease prevention in pre-symptomatic mutation carriers, some carriers opt for delaying surgery and undergo regular endoscopic surveillance. When using a strict endoscopic surveillance protocol (focal visible lesion and random biopsy sampling), SRCC detection rate yield is approximately 60% in CDH1 mutation carriers.[3] In this letter, we wish to call the attention to cervical esophageal gastric inlet patches (GIPs). [Extracted from the article]

The prediction and final survival rate of gastrointestinal cancers are dependent on the stage of disease. The ideal would be to detect those gastrointestinal lesions at early stage or even premalignant forms which are difficult to detect by conventional endoscopy with white light optical imaging as they show minimum or no changes in morphological characteristics and are thus left untreated. The introduction of molecular imaging has greatly changed the pattern for detecting gastrointestinal lesions from purely macroscopic structural imaging to the molecular level. It allows microscopic examination of the gastrointestinal mucosa with endoscopy after the topical or systemic application of molecular probes. In recent years, major advancements in endoscopic instruments and specific molecular probes have been achieved. This review focuses on the current status of endoscopic imaging and highlights the application of molecular imaging in gastrointestinal and hepatic disease in the context of diagnosis and therapy based on recently published literature in this field. We also discuss the challenges of molecular endoscopic imaging, its future directions and potential that could have a tremendous impact on endoscopic research and clinical practice in future. [ABSTRACT FROM AUTHOR]