Inflammatory Bowel Disease

Mapping two decades of research in rheumatology-specific journals: a topic modeling analysis with BERTopic.

BACKGROUND: Rheumatology has experienced notable changes in the last decades. New drugs, including biologic agents and Janus kinase (JAK) inhibitors, have blossomed. Concepts such as window of opportunity, arthralgia suspicious for progression, or difficult-to-treat rheumatoid arthritis (RA) have appeared; and new management approaches and strategies such as treat-to-target have become popular. St...

A clinical pilot trial of an artificial intelligence-driven smart phone application of bowel preparation for colonoscopy: a randomized clinical trial.

BACKGROUND: High-quality bowel preparation is paramount for a successful colonoscopy. This study aimed to explore the effect of artificial intelligence-driven smartphone software on the quality of bowel preparation.

Integrating single-cell RNA-Seq and machine learning to dissect tryptophan metabolism in ulcerative colitis.

BACKGROUND: Ulcerative colitis (UC) is a persistent inflammatory bowels disease (IBD) characterized by immune response dysregulation and metabolic dis...

Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response.

INTRODUCTION: Head and neck squamous cell carcinoma (HNSCC), a highly heterogeneous malignancy is often associated with unfavorable prognosis. Due to ...

Unravelling metabolite-microbiome interactions in inflammatory bowel disease through AI and interaction-based modelling.

Inflammatory Bowel Diseases (IBDs) are chronic inflammatory disorders of the gastrointestinal tract and colon affecting approximately 7 million indivi...

Deep learning-assisted colonoscopy images for prediction of mismatch repair deficiency in colorectal cancer.

BACKGROUND: Deficient mismatch repair or microsatellite instability is a major predictive biomarker for the efficacy of immune checkpoint inhibitors o...

Surgical Insight-guided Deep Learning for Colorectal Lesion Management.

BACKGROUND: Colonoscopy stands as a pivotal diagnostic tool in identifying gastrointestinal diseases, including potentially malignant tumors. The proc...

Cost-Effectiveness for Artificial Intelligence in Colonoscopy.

Artificial intelligence (AI) is set to transform the field of colonoscopy through the implementation of computer-assisted detection and diagnosis. Whi...

Creating a standardized tool for the evaluation and comparison of artificial intelligence-based computer-aided detection programs in colonoscopy: a modified Delphi approach.

BACKGROUND AND AIMS: Multiple computer-aided detection (CADe) software programs have now achieved regulatory approval in the United States, Europe, an...

Artificial Intelligence in Inflammatory Bowel Disease.

Artificial intelligence (AI) is being increasingly studied and implemented in gastroenterology. In inflammatory bowel disease (IBD), numerous AI model...

Role of Artificial Intelligence for Colon Polyp Detection and Diagnosis and Colon Cancer.

The broad use of artificial intelligence (AI) and its various applications have already shown significant impact in medicine and in everyday life. In ...

CLMS: Bridging domain gaps in medical imaging segmentation with source-free continual learning for robust knowledge transfer and adaptation.

Deep learning shows promise for medical image segmentation but suffers performance declines when applied to diverse healthcare sites due to data discr...

A Multicenter Evaluation of the Impact of Therapies on Deep Learning-Based Electrocardiographic Hypertrophic Cardiomyopathy Markers.

Artificial intelligence-enhanced electrocardiography (AI-ECG) can identify hypertrophic cardiomyopathy (HCM) on 12-lead ECGs and offers a novel way to...

Federated Learning for Predicting Postoperative Remission of Patients with Acromegaly: A Multicentered Study.

BACKGROUND: Decentralized federated learning (DFL) may serve as a useful framework for machine learning (ML) tasks in multicentered studies, maximizin...

In vivo evaluation of complex polyps with endoscopic optical coherence tomography and deep learning during routine colonoscopy: a feasibility study.

Standard-of-care (SoC) imaging for assessing colorectal polyps during colonoscopy, based on white-light colonoscopy (WLC) and narrow-band imaging (NBI...

Interpretable machine learning uncovers epithelial transcriptional rewiring and a role for Gelsolin in COPD.

Transcriptomic analyses have advanced the understanding of complex disease pathophysiology including chronic obstructive pulmonary disease (COPD). How...

Cross-instrument optical coherence tomography-angiography (OCTA)-based prediction of age-related macular degeneration (AMD) disease activity using artificial intelligence.

This study investigates the efficacy of predicting age-related macular degeneration (AMD) activity through deep neural networks (DNN) using a cross-in...

Applying Deep-Learning Algorithm Interpreting Kidney, Ureter, and Bladder (KUB) X-Rays to Detect Colon Cancer.

Early screening is crucial in reducing the mortality of colorectal cancer (CRC). Current screening methods, including fecal occult blood tests (FOBT) ...

Optimizing the Prediction of Depression Remission: A Longitudinal Machine Learning Approach.

Decisions about when to change antidepressant treatment are complex and benefit from accurate prediction of treatment outcome. Prognostic accuracy can...

Colonoscopy polyp classification via enhanced scattering wavelet Convolutional Neural Network.

Among the most common cancers, colorectal cancer (CRC) has a high death rate. The best way to screen for colorectal cancer (CRC) is with a colonoscopy...