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Although fecal microbiota composition is considered to preserve relevant and representative information for distal colonic content, it is evident that it does not represent microbial communities inhabiting the small intestine. Nevertheless, studies investigating the human small intestinal microbiome and its response to dietary intervention are still scarce. The current study investigated the spatio-temporal dynamics of the small intestinal microbiome within a day and over 20 days, as well as its responses to a 14-day synbiotic or placebo control supplementation in 20 healthy subjects. Microbial composition and metabolome of luminal content of duodenum, jejunum, proximal ileum and feces differed significantly from each other. Additionally, differences in microbiota composition along the small intestine were most pronounced in the morning after overnight fasting, whereas differences in composition were not always measurable around noon or in the afternoon. Although overall small intestinal microbiota composition did not change significantly within 1 day and during 20 days, remarkable, individual-specific temporal dynamics were observed in individual subjects. In response to the synbiotic supplementation, only the microbial diversity in jejunum changed significantly. Increased metabolic activity of probiotic strains during intestinal passage, as assessed by metatranscriptome analysis, was not observed. Nevertheless, synbiotic supplementation led to a short-term spike in the relative abundance of genera included in the product in the small intestine approximately 2 hours post-ingestion. Collectively, small intestinal microbiota are highly dynamic. Ingested probiotic bacteria could lead to a transient spike in the relative abundance of corresponding genera and ASVs, suggesting their passage through the entire gastrointestinal tract. This study was registered to http://www.clinicaltrials.gov, NCT02018900.

BACKGROUND Artificial intelligence (AI) has potential in the optical diagnosis of colorectal polyps. AIM To evaluate the feasibility of the real-time use of the computer-aided diagnosis system (CADx) AI for ColoRectal Polyps (AI4CRP) for the optical diagnosis of diminutive colorectal polyps and to compare the performance with CAD EYETM (Fujifilm, Tokyo, Japan). CADx influence on the optical diagnosis of an expert endoscopist was also investigated. METHODS AI4CRP was developed in-house and CAD EYE was proprietary software provided by Fujifilm. Both CADx-systems exploit convolutional neural networks. Colorectal polyps were characterized as benign or premalignant and histopathology was used as gold standard. AI4CRP provided an objective assessment of its characterization by presenting a calibrated confidence characterization value (range 0.0-1.0). A predefined cut-off value of 0.6 was set with values < 0.6 indicating benign and values ≥ 0.6 indicating premalignant colorectal polyps. Low confidence characterizations were defined as values 40% around the cut-off value of 0.6 (< 0.36 and > 0.76). Self-critical AI4CRP’s diagnostic performances excluded low confidence characterizations. RESULTS AI4CRP use was feasible and performed on 30 patients with 51 colorectal polyps. Self-critical AI4CRP, excluding 14 low confidence characterizations [27.5% (14/51)], had a diagnostic accuracy of 89.2%, sensitivity of 89.7%, and specificity of 87.5%, which was higher compared to AI4CRP. CAD EYE had a 83.7% diagnostic accuracy, 74.2% sensitivity, and 100.0% specificity. Diagnostic performances of the endoscopist alone (before AI) increased non-significantly after reviewing the CADx characterizations of both AI4CRP and CAD EYE (AI-assisted endoscopist). Diagnostic performances of the AI-assisted endoscopist were higher compared to both CADx-systems, except for specificity for which CAD EYE performed best. CONCLUSION Real-ti

small intestinal intervention, small intestinal intervention

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths with rising incidence. Since the survival rate of CRC is correlated with the cancer stage at diagnosis, timely detection and adequate treatment strategies are of utmost importance. Technical innovations such as machine learning (ML) and its application in endoscopy show promising results, but the trust of medical doctors in ML is lacking and the 'black box' nature complicates the understanding of such systems in clinical practice. In contrast to CT and MRI, image quality is a limiting factor in especially endoscopic imaging, as it is very operator dependent. However, the influence of image quality on convolutional (deep) neural networks (CNNs) is insufficiently studied in relation to clinical practice and the usage of medical image data for computer-aided detection and diagnosis (CADx) systems. This paper explores the influence of degraded image quality on the performance of CNNs applied to colorectal polyp (CRP) characterization. Five commonly used CNN architectures, from simple to more complex, are employed with a custom classification head for common CRP characterization. To degrade the quality of images, distortions such as noise, blur, and contrast changes are imposed on the data and their influence on the performance degradation is studied for the mentioned CNN architectures. A large prospectively collected in vivo data set, gathered from four Dutch, both academic and community, hospitals is employed. Results for CRP characterization show that promising CNN-based methods are rather susceptible to noise and blur distortions but reasonably resilient to changes in contrast. This implies that image quality needs monitoring and control prior to directly using image data in CNN models, in order to gain trustworthy use of deep learning (DL) models in a clinical setting. We propose that incorporating an image quality indicator in CADx systems will lead to better acceptance of such systems, a

Background and study aims Neuroendocrine neoplasms (NEN) account for a small number of colorectal neoplasms. Endoscopic detection is essential for diagnosis, treatment and follow-up. Little is known about incidence of NENs in colonoscopy populations or the relationship between clinical, endoscopic and histopathologic features. We evaluated epidemiology, endoscopic and clinical characteristics of colorectal NENs in a population-based cohort. Patients and methods Medical records of NEN cases were cross-linked with the national pathology database from January 2001 to December 2015, in South Limburg County, the Netherlands, covering four endoscopy units. Senior pathologists reviewed and classified NENs using World Health Organization 5th edition (2019) guidelines. Results The number of colorectal NEN diagnoses was stable over time with 0.6 NEN per 1,000 patients. A total of NENs were detected in 85 patients: 65 neuroendocrine tumors (NETs) and 20 poorly differentiated neuroendocrine carcinomas (NECs). Rectal NETs were usually small sessile/submucosal lesions with yellowish (lipoma-like) color. Colonic NETs were larger sessile/submucosal lesions with darker color compared to background. Colorectal NECs presented as large, dark-colored lesions with ulcerated/necrotizing areas. Conclusions Our population-based data point to a stable and low incidence of 0.6 NEN per 1,000 patients in the Netherlands. Rectal NETs mainly present as small sessile yellowish lesions. Colonic NETs present as larger and darker lesions than background mucosa and NECs as darker lesions than background with ulceration/necrosis. Standardized endoscopic characterization of colorectal NENs is necessary to improve recognition of these lesions and provide a basis for evidence-based treatment and surveillance recommendations.