Inflammatory Bowel Disease

Comparative Evaluation of Caudal Epidural Method to Ultrasound-Guided S1 Transforamen Block in Patient's Lumbar Discectomy with Failed Back Syndrome Symptoms: A Double-Blind Clinical Trial.

BACKGROUND: Post-lumbar surgery syndrome (PLSS) refers to persistent or recurrent pain following spinal surgery with an unknown cause. It is commonly associated with epidural fibrosis (EF). Some studies suggest that epidural steroid injection (ESI) can effectively alleviate pain in PLSS, particularly when targeting the S1 nerve root using S1 transforaminal epidural steroid injection (S1-TFESI). A ...

CRPU-NET: a deep learning model based semantic segmentation for the detection of colorectal polyp in lower gastrointestinal tract.

. The objectives of the proposed work are twofold. Firstly, to develop a specialized light weight CRPU-Net for the segmentation of polyps in colonoscopy images. Secondly, to conduct a comparative analysis of the performance of CRPU-Net with implemented state-of-the-art models.. We have utilized two distinct colonoscopy image datasets such as CVC-ColonDB and CVC-ClinicDB. This paper introduces the ...

A Cohort Study Investigating Zearalenone Concentrations and Selected Steroid Levels in Patients with Sigmoid Colorectal Cancer or Colorectal Cancer.

THE AIM: In this study was to determine if sigmoid colorectal cancer (SCC) and colorectal cancer (CRC) in women (W) and men (M) is accompanied by zear...

Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer.

Irrespective of men and women, colorectal cancer (CRC), is the third most common cancer in the population with more than 1.85 million cases annually. ...

AN ELECTRONIC MEDICAL RECORD PREDICTION MODEL TO IDENTIFY INADEQUATE BOWEL PREPARATION IN PATIENTS AT OUTPATIENT COLONOSCOPY.

BACKGROUND AND AIMS: Inadequate bowel preparation during colonoscopy is associated with decreased adenoma detection, increased costs, and patient proc...

A Machine Learning-Based Diagnostic Model for Crohn's Disease and Ulcerative Colitis Utilizing Fecal Microbiome Analysis.

Recent research has demonstrated the potential of fecal microbiome analysis using machine learning (ML) in the diagnosis of inflammatory bowel disease...

APOLLO 11 Project, Consortium in Advanced Lung Cancer Patients Treated With Innovative Therapies: Integration of Real-World Data and Translational Research.

INTRODUCTION: Despite several therapeutic efforts, lung cancer remains a highly lethal disease. Novel therapeutic approaches encompass immune-checkpoi...

Towards Semi-Autonomous Colon Screening Using an Electromagnetically Actuated Soft-Tethered Colonoscope Based on Visual Servo Control.

OBJECTIVE: Conventional colonoscopy using a flexible colonoscope remains two major limitations, including patient discomfort and difficult manipulatio...

Application of deep learning in the diagnosis and evaluation of ulcerative colitis disease severity.

BACKGROUND: Achieving endoscopic and histological remission is a critical treatment objective in ulcerative colitis (UC). Nevertheless, interobserver ...

Prediction of Clinical Remission with Adalimumab Therapy in Patients with Ulcerative Colitis by Fourier Transform-Infrared Spectroscopy Coupled with Machine Learning Algorithms.

We aimed to develop prediction models for clinical remission associated with adalimumab treatment in patients with ulcerative colitis (UC) using Fouri...

Development and validation of a three-dimensional deep learning-based system for assessing bowel preparation on colonoscopy video.

BACKGROUND: The performance of existing image-based training models in evaluating bowel preparation on colonoscopy videos was relatively low, and only...

Enhancing artificial intelligence-doctor collaboration for computer-aided diagnosis in colonoscopy through improved digital literacy.

Establishing appropriate trust and maintaining a balanced reliance on digital resources are vital for accurate optical diagnoses and effective integra...

Considerations and targeted approaches to identifying bad actors in exposure mixtures.

Variable importance is a key statistical issue in exposure mixtures, as it allows a ranking of exposures as potential targets for intervention, and he...

Role of artificial intelligence in imaging and endoscopy for the diagnosis, monitoring and prognostication of inflammatory bowel disease: a scoping review protocol.

INTRODUCTION: Inflammatory bowel diseases (IBD) are immune-mediated conditions that are increasing in incidence and prevalence worldwide. Their assess...

Identifying risk factors of anti-TNF induced skin lesions and other adverse events in paediatric patients with inflammatory bowel disease.

OBJECTIVES: While higher infliximab (IFX) trough concentrations (TCs) are associated with better outcomes in patients with inflammatory bowel disease ...

Using Histologic Image Analysis to Understand Biophysical Regulations of Epithelial Cell Morphology.

Epithelial mechanics and mechanobiology have become 2 important research fields in life sciences and bioengineering. These fields investigate how phys...

Can artificial intelligence replace endoscopists when assessing mucosal healing in ulcerative colitis? A systematic review and diagnostic test accuracy meta-analysis.

BACKGROUNDS AND AIMS: Mucosal healing (MH) in inflammatory bowel diseases (IBD) is an important landmark for clinical decision making. Artificial inte...

Effectiveness of strategies to suppress antibodies to infliximab in pediatric inflammatory bowel disease.

OBJECTIVES: Antibodies to infliximab (ATIs) are associated with loss of response in children with inflammatory bowel disease (IBD). We aimed to descri...

Effects of Transcranial Direct Current Stimulation on Cognitive Deficits in Depression: A Systematic Review.

BACKGROUND: Major depressive disorder is the leading cause of mental health-related burden globally and up to one-third of major depressive disorder p...

Colonoscopy setting and impact on statistical significance in Artificial Intelligence trials.