Colorectal cancer (CRC) prevention depends on effective colonoscopy; yet variability in adenoma detection rates (ADRs) and missed lesions remain significant hurdles. Artificial intelligence-powered computer-aided detection (CADe) systems offer promising advancements in enhancing polyp detection. This review examines the role of CADe in improving ADR and reducing adenoma miss rates (AMRs) while addressing its broader clinical implications. CADe has demonstrated consistent improvements in ADRs and AMRs; largely by detecting diminutive polyps, but shows limited efficacy in identifying advanced adenomas or sessile serrated lesions. Challenges such as operator deskilling and the need for enhanced algorithms persist. Combining CADe with adjunctive techniques has shown potential for further optimizing performance. While CADe has standardized detection quality; its long-term impact on CRC incidence and mortality remains inconclusive. Future research should focus on refining CADe technology and assessing its effectiveness in reducing the global burden of CRC.

Background/Aims: Current literature is lacking in studies comparing the incidence of adverse events (AEs) in patients with inflammatory bowel diseases (IBD) treated with adalimumab (ADA) or vedolizumab (VDZ) in a real-life scenario. Therefore, our primary aim was to compare the AEs occurring in patients taking ADA to those of patients taking VDZ. Methods: In this single center study, data on AEs from IBD patients who underwent treatment with ADA and VDZ were retrospectively collected. AE rates per 100 person-years were calculated. A Cox regression model was used to estimate the hazard ratios of the AEs between the 2 drugs. Results: A total of 16 ADA patients (17.2%) and 11 VDZ patients (7.6%) had AEs causing drug interruption during the study period (P=0.02). Most of the AEs were noninfectious extraintestinal events (50% in ADA and 54.5% in VDZ) while infections accounted for 31.2% of the AEs in patients treated with ADA and 27.3% in those treated with VDZ. The incidence rate of AEs causing withdrawal of therapy was 13.2 per 100 person-years for ADA and 5.3 per 100 person-years for VDZ, corresponding to a 76% lower risk in patients in VDZ. Considering the first year of treatment, we observed 34 subjects treated with ADA (36.5%) having at least 1 AEs and 57 (39.3%) among those taking VDZ (P=0.67). Conclusions: VDZ has a lower incidence rate of AEs causing withdrawal of treatment compared to ADA but a similar risk of AEs not causing drug interruption. Real-life head-to-head studies are still necessary to further explore the safety profile of these drugs.

The outbreak of coronavirus disease 2019 has undeniably affected modalities of medical and gastroenterology training in all endemic areas. Indeed, the adoption of various distance-learning techniques has been mandatory to facilitate education and enhance skills such as problem-solving, self-directed learning, open communication and also holistic non-cognitive attributes such as adaptability and collaboration.

Colorectal cancer (CRC) is the third most common cancer worldwide, with the highest incidence reported in high-income countries. However, because of the slow progression of neoplastic precursors, along with the opportunity for their endoscopic detection and resection, a well-designed endoscopic screening program is expected to strongly decrease colorectal cancer incidence and mortality. In this regard, quality of colonoscopy has been clearly related with the risk of post-colonoscopy colorectal cancer. Recently, the development of artificial intelligence (AI) applications in the medical field has been growing in interest. Through machine learning processes, and, more recently, deep learning, if a very high numbers of learning samples are available, AI systems may automatically extract specific features from endoscopic images/videos without human intervention, helping the endoscopists in different aspects of their daily practice. The aim of this review is to summarize the current knowledge on AI-aided endoscopy, and to outline its potential role in colorectal cancer prevention.

Background and objectives: Subepithelial lesions (SELs) of the gastrointestinal (GI) tract present a diagnostic challenge due to their heterogeneous nature and varied clinical manifestations. Usually, SELs are small and asymptomatic; generally discovered during routine endoscopy or radiological examinations. Currently, endoscopic ultrasound (EUS) is the best tool to characterize gastric SELs. Materials and methods: For this review, the research and the study selection were conducted using the PubMed database. Articles in English language were reviewed from August 2019 to July 2024. Results: This review aims to summarize the international literature to examine and illustrate the progress in the last five years of endosonographic diagnostics and treatment of gastric SELs. Conclusions: Endoscopic ultrasound is the preferred option for the diagnosis of sub-epithelial lesions. In most of the cases, EUS-guided tissue sampling is mandatory; however, ancillary techniques (elastography, CEH-EUS, AI) may help in both diagnosis and prognostic assessment.