Gastroenterology

SLIC-Former: a superpixel-guided transformer framework for automatic liver segmentation in CT images.

Accurate and efficient liver segmentation from computed tomography (CT) images remains a critical challenging task due to the organ's irregular shape, variable intensity, and lies close to surrounding organs with similar appearance. In this study, we propose SLIC-Former, a superpixel-guided transformer framework for automatic liver segmentation in abdominal CT scans. The method replaces fixed squa...

A vision transformer deep learning model for assessing pediatric ileocolic intussusception severity using ultrasound images.

Timely identification of children with ileocolic intussusception likely to fail air-enema reduction is critical to avoid delays and bowel perforation. However, even expert sonographers show inter-observer variability. We developed and prospectively validated a Vision Transformer (ViT) deep learning system to predict reduction failure from static B-mode ultrasound images. This multicenter bidirecti...

Uncovering the mechanism of DCLK3 in colorectal cancer progression through WGCNA and machine learning.

BACKGROUND: Colorectal cancer (CRC) is a prevalent malignant tumor of the digestive tract with high morbidity and mortality rates. Although doublecort...

Evaluation of Large Language Models in the Clinical Management of Patients With Upper Gastrointestinal Bleeding: Insights From Real-World Patient Data.

OBJECTIVE: To evaluate large language models (LLMs) for pre-endoscopy (PE) risk stratification and prediction of endoscopic findings in upper gastroin...

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

Flagellin, the structural protein of bacterial flagella, activates the innate immune receptor Toll-like receptor 5 (TLR5). However, the ability of dif...

Machine Learning-Based Identification of Survival-Associated CpG Biomarkers in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an exceptionally aggressive cancer with a 5-year survival rate of less than 10%, driven by late-stage diagn...

Procedure-specific prediction of surgical difficulty in laparoscopic and robotic right hemicolectomy: Interpretable machine learning models.

BACKGROUND: Laparoscopic (L-RHC) and robotic (R-RHC) right hemicolectomy are standard treatments for colon cancer, but procedure-specific prediction o...

Trait-based machine learning modeling of soluble carbohydrate content in Trachyspermum ammi exposed to organic and chemical fertilization and salicylic acid.

Accurate assessment of biochemical traits in medicinal plants is essential for supporting environmentally responsible agriculture, improving crop qual...

Brain network correlates of fatigue, depression, and anxiety in patients with Crohn's Disease in different disease states.

BACKGROUND: Symptoms of fatigue, depression or anxiety are frequent in Crohn's Disease (CD) and may relate to disturbed brain-gut interactions. While ...

Epigenetic editing approaches maturity: AI-driven precision design, delivery innovation, and the road to clinical translation.

Epigenetic editing achieves durable gene silencing through targeted modification of chromatin and DNA methylation states without altering the genomic ...

Colorectal Cancer Screening in Hereditary and Familial High-Risk Populations: Best Practices and Future Directions.

Colorectal cancer (CRC) remains a leading cause of cancer-related morbidity and mortality worldwide yet is largely preventable through effective scree...

Automatic recognition of anatomical structures and surgical phases in robot-assisted minimally invasive esophagectomy (RAMIE) using deep learning: a retrospective cohort study.

BACKGROUND: Curative treatment of resectable esophageal cancer comprises neoadjuvant chemoradiotherapy and esophagectomy. Robot-assisted minimally inv...

Evaluating human-AI interaction in optical diagnosis of early colorectal carcinoma through endoscopist responses to simulated artificial intelligence: a multinational online study.

Background Suboptimal human - artificial intelligence (AI) interaction is a potential roadblock for implementation of AI in clinical practice. We aime...

Machine Learning-Based Privacy Preserving via CT/MRI and Organ Metadata Prediction.

Medical imaging plays a significant role in diagnosis and treatment planning, with significant efforts focused on training machine learning (ML) algor...

Digital Pathology-Enabled Artificial Intelligence for Fibrosis Assessment in Metabolic Dysfunction-Associated Steatohepatitis: Needs, Current Progress, and Barriers.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is emerging as the most common chronic liver disorder worldwide. However, most of the...

Using artificial intelligence to identify characteristics associated with clinical and economic outcomes in MASH (FOCUS-MASH).

BACKGROUND: Heterogeneity in the disease characteristics of metabolic dysfunction-associated steatohepatitis (MASH) complicates efforts to identify in...

A systematic review on colon cancer classification by convolutional neural networks: Architecture, accuracy, and research directions.

INTRODUCTION: Recent studies indicated the advancement of Convolutional Neural Networks (CNNs) have facilitated the colon cancer diagnosis process via...

Dysregulated glucocorticoid-responsive immune genes in peripheral blood mononuclear cells as a shared molecular signature of autism spectrum disorder and irritable bowel syndrome.

BACKGROUND: Autism spectrum disorder (ASD) is frequently accompanied by gastrointestinal (GI) disturbances resembling irritable bowel syndrome (IBS). ...

Physically intelligent capsule robots with embodied memory and logic in the gastrointestinal tract.

Miniaturized medical robots offer a promising solution for minimally invasive measurements and interventions in the gastrointestinal (GI) tract. Clini...

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