Inflammatory Bowel Disease

Towards Polyp Counting In Full-Procedure Colonoscopy Videos

Automated colonoscopy reporting holds great potential for enhancing quality control and improving cost-effectiveness of colonoscopy procedures. A major challenge lies in the automated identification, tracking, and re-association (ReID) of polyps tracklets across full-procedure colonoscopy videos. This is essential for precise polyp counting and enables automated computation of key quality metric...

Evaluation of risk factors for thromboembolic events in multiple myeloma patients using multiple machine learning models.

Venous thromboembolic events (VTE) is a frequent complication in multiple myeloma (MM) patients, raising mortality. This study aims to use machine learning to identify VTE risk factors in MM, helping to pinpoint high-risk individuals for better clinical management and prognosis. A retrospective analysis was conducted on the basic information, laboratory test results, treatment plans, and thrombosi...

Diverse Image Generation with Diffusion Models and Cross Class Label Learning for Polyp Classification

Pathologic diagnosis is a critical phase in deciding the optimal treatment procedure for dealing with colorectal cancer (CRC). Colonic polyps, precu...

A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation

Following recent advancements in computer-aided detection and diagnosis systems for colonoscopy, the automated reporting of colonoscopy procedures i...

Prevalence, incidence, and mortality of inflammatory bowel disease in the Netherlands: development and external validation of machine learning models.

BACKGROUND AND AIMS: Large registries are promising tools to study the epidemiology of inflammatory bowel disease (IBD). We aimed to develop and valid...

Exploring Transfer Learning for Deep Learning Polyp Detection in Colonoscopy Images Using YOLOv8

Deep learning methods have demonstrated strong performance in objection tasks; however, their ability to learn domain-specific applications with lim...

The Application Value of an Artificial Intelligence-Driven Intestinal Image Recognition Model to Evaluate Intestinal Preparation before Colonoscopy.

Artificial intelligence (AI), with advantages such as automatic feature extraction and high data processing capacity and being unaffected by fatigue,...

Polyp detection in colonoscopy images using YOLOv11

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers all over the world. It starts as a polyp in the inner lining of the colon. To ...

Artificial Intelligence-assisted Video Colonoscopy for Disease Monitoring of Ulcerative Colitis: A Prospective Study.

BACKGROUNDS AND AIMS: The Mayo endoscopic subscore [MES] is the most popular endoscopic disease activity measure of ulcerative colitis [UC]. Artificia...

Artificial intelligence-enabled histology exhibits comparable accuracy to pathologists in assessing histological remission in ulcerative colitis: a systematic review, meta-analysis, and meta-regression.

BACKGROUND AND AIMS: Achieving histological remission is a desirable emerging treatment target in ulcerative colitis (UC), yet its assessment is chall...

Combination of white-light imaging-based and narrow-band imaging-based artificial intelligence models during colonoscopy in patients with ulcerative colitis.

BACKGROUND AND AIMS: The long-term treat-to-target (T2T) approach in ulcerative colitis (UC) aims for endoscopic remission, but variability among endo...

[Value of the deep learning automated quantification of tumor-stroma ratio in predicting efficacy and prognosis of neoadjuvant therapy for breast cancer based on residual cancer burden grading].

To investigate the prognostic value of deep learning-based automated quantification of tumor-stroma ratio (TSR) in patients undergoing neoadjuvant th...

Human gut flagellome profiling using FlaPro reveals TLR5-related phenotype-specific alterations in IBD

Flagellin is the protein monomer of the bacterial flagellum, which confers motility, allowing bacteria to reach their favored niches. Flagellin is hig...

ATOMIC: A graph attention neural network for ATOpic dermatitis prediction on human gut MICrobiome

Atopic dermatitis (AD) is a chronic inflammatory skin disease driven by complex interactions among genetic, environmental, and microbial factors; howe...

Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity

Despite increased understanding of psoriasis pathogenesis, molecular classification of clinical phenotypes and disease severity is poorly defined. Kno...

MechanoAge, a machine learning platform to identify individuals susceptible to breast cancer based on mechanical properties of single cells

Existing breast cancer risk models inadequately identify individuals at latent risk, particularly among women without known genetic mutations or famil...

Extracellular Vesicle Gene Expression Enables Sensitive Detection of Colorectal Neoplasia

Extracellular vesicles (EVs), including exosomes, are emerging as promising carriers of disease-specific biomarkers due to their molecular cargo refle...

Targeted Enzymatic Fragmentation of Lipoprotein(a) via Kringle IV Domains: A Clearance-Enhancing Therapeutic Strategy for Cardiovascular Disease

Elevated lipoprotein(a) [Lp(a)] is an independent, genetically determined risk factor for atherosclerotic cardiovascular disease (ASCVD). Its unique a...

Colon-Specific Epigenetic Clocks from Minimal Features Reveal Disease-Driven Aging

Epigenetic clocks estimate chronological and biological age from DNA methylation patterns, but conventional models typically train on hundreds of thou...

Inter- and intra-individual differences in brain sex map to neuroendocrine profiles

Sex hormone fluctuations modulate structural and functional brain dynamics, yet little is known how sex steroid levels map onto the expression of sex ...