Systematic review: Defining, diagnosing and monitoring small bowel strictures in Crohn's disease on intestinal ultrasound.

BACKGROUND: Stricturing Crohn's disease (CD) occurs most commonly in the terminal ileum and poses a clinical problem. Cross-sectional imaging modalities such as intestinal ultrasound (IUS), computed tomography enterography (CTE), and magnetic resonance enterography (MRE) allow for assessment of the entire bowel wall and associated peri-enteric findings. Radiologic definitions of strictures have been developed for CTE and MRE; their reliability and responsiveness are being evaluated in index development programs. A comprehensive assessment strategy for strictures using IUS is needed. AIMS: To provide a detailed summary of definitions, diagnosis and monitoring of strictures on IUS as well as technical aspects of image acquisition. METHODS: We searched four databases up to 6 January 2024. Two-stage screening was done in duplicate. We assessed risk of bias using QUADAS-2. RESULTS: There were 56 studies eligible for inclusion. Definitions for strictures on IUS are heterogeneous, but the overall accuracy for diagnosis of strictures is high. The capability of IUS for characterising inflammation versus fibrosis in strictures is not accurate enough to be used in clinical practice or trials. We summarise definitions for improvement of strictures on IUS, and discuss parameters for image acquisition and standardisation. CONCLUSIONS: This systematic review is the first step for a structured program to develop a stricture IUS index for CD.

Application of SWATH Mass Spectrometry and Machine Learning in the Diagnosis of Inflammatory Bowel Disease Based on the Stool Proteome.

Inflammatory bowel disease (IBD) flare-ups exhibit symptoms that are similar to other diseases and conditions, making diagnosis and treatment complicated. Currently, the gold standard for diagnosing and monitoring IBD is colonoscopy and biopsy, which are invasive and uncomfortable procedures, and the fecal calprotectin test, which is not sufficiently accurate. Therefore, it is necessary to develop an alternative method. In this study, our aim was to provide proof of concept for the application of Sequential Window Acquisition of All Theoretical Mass Spectra-Mass spectrometry (SWATH-MS) and machine learning to develop a non-invasive and accurate predictive model using the stool proteome to distinguish between active IBD patients and symptomatic non-IBD patients. Proteome profiles of 123 samples were obtained and data processing procedures were optimized to select an appropriate pipeline. The differentially abundant analysis identified 48 proteins. Utilizing correlation-based feature selection (Cfs), 7 proteins were selected for proceeding steps. To identify the most appropriate predictive machine learning model, five of the most popular methods, including support vector machines (SVMs), random forests, logistic regression, naive Bayes, and k-nearest neighbors (KNN), were assessed. The generated model was validated by implementing the algorithm on 45 prospective unseen datasets; the results showed a sensitivity of 96% and a specificity of 76%, indicating its performance. In conclusion, this study illustrates the effectiveness of utilizing the stool proteome obtained through SWATH-MS in accurately diagnosing active IBD via a machine learning model.