Gastroenterology

Radiomics analysis of restaging MRI for detection of pathological complete response in locally advanced rectal cancer.

PURPOSE: magnetic resonance imaging (MRI)-based radiomics has emerged as a promising approach for non-invasive prediction of treatment response in rectal cancer. This study aimed to develop and validate a machine learning model based on radiomic features extracted from restaging MRI after neoadjuvant therapy in patients with locally advanced rectal cancer (LARC), to identify those achieving pathol...

A novel endoscopic ultrasound system assisted by artificial intelligence for the recognition of pancreatic parenchyma and the detection of solid/cystic lesions.

Fully Automated Colon Delineation and Volume Estimation in T2-Weighted MRI with a 2D U-Net.

Artificial intelligence has the potential to provide an objective, accurate and fast evaluation of the colon for clinical assessment of gastrointestin...

Building an Ontology-Based Cohort of Liver Cancer Imaging Data for AI Development on the European Federated Platform EUCAIM.

Hepatocellular carcinoma (HCC) is steadily increasing in incidence worldwide and requires data-driven approaches to improve diagnosis, prognosis, and ...

Multimodal Graph-Based Model for Discrete-Time Survival Prediction in Liver Cancer.

Liver cancer is a leading cause of cancer mortality; hepatocellular carcinoma (HCC), its predominant form, requires accurate survival prediction to gu...

Random Forest Model for the Prediction of Herbal-Induced Liver Injury: Application to Molecules from the West African Pharmacopoeia.

Herbal medicines play a crucial role in primary healthcare across West Africa, yet their potential for liver toxicity remains poorly documented. Predi...

CACRC-Pan: A Concept Annotated Case Report Corpus and Processing Pipeline for Rare Pancreatic Diseases.

We introduce CACRC-Pan, a corpus of case reports for six rare pancreatic diseases (AIP1-2, CF, HCP, PS and SDS) annotated at two concept levels: token...

Extraction of Endoscopic Markers from Clinical Notes in Italian Patients with Autoimmune Atrophic Gastritis Using Small Language Models.

While Large Language Models (LLMs) hold great potential for clinical applications, their use is limited by concerns regarding data privacy, high compu...

Uncovering Topics in Dutch Patient Messages in Inflammatory Bowel Disease: A Comparative Study of Embedding Models for Neural Topic Modeling.

This study applied natural language processing to identify common topics in 12,054 Dutch patient-provider messages in inflammatory bowel disease. Usin...

The CT colonography radiology and data system: history, updates, and future directions.

Computed tomography colonography, also known as virtual colonoscopy, is a minimally invasive imaging technique developed in the early 1990s to evaluat...

Artificial Intelligence Algorithm to Monitor Inspiratory Muscle Effort and Patient-Ventilator Dyssynchrony During Mechanical Ventilation.

OBJECTIVE: Current methods for estimating inspiratory muscle pressure (Pmus) during mechanical ventilation are either invasive or dependent on occlusi...

Blocking MOXD1-derived ACOX1 peroxisome trafficking suppresses metabolic dysfunction-associated steatohepatitis.

BACKGROUND: Metabolic dysfunction-associated steatohepatitis (MASH) has become a major global health burden yet effective pharmacological treatments r...

Optimizing hepatitis C diagnosis through reinforcement learning feature selection and multi-model machine learning evaluation.

Hepatitis C virus (HCV) infection remains a leading cause of liver cirrhosis and hepatocellular carcinoma globally, affecting approximately 50 million...

Maximizing pancreatic carcinoma classification performance using parrot optimized vision transformer.

Pancreatic cancer is a rare kind of cancer that is detected during the final stages. This is because the symptoms are very common and also do not show...

Machine learning and artificial intelligence in liquid biopsy-based early detection of pancreatic cancer: a scoping review.

Pancreatic ductal adenocarcinoma (PDAC) presents as a cancer with an especially poor prognosis, largely due to the challenges surrounding its early di...

Development and validation of a novel YOLOv5-based artificial intelligence model for gastric mucosal lesion detection.

BACKGROUND AND AIMS: Artificial intelligence (AI) has been widely used in endoscopic diagnosis; however, an AI model capable of comprehensively diagno...

LRF-CNN: An explainable lightweight receptive field-based CNN for colorectal cancer histopathological image classification.

Colorectal cancer (CRC) screening and diagnosis rely on histopathological assessment, but many high-performing deep learning (DL) models remain comput...

Preoperative identification of tumor deposits in rectal cancer using a transformer-based multimodal fusion model: a multicenter retrospective study.

OBJECTIVE: To develop and validate a transformer-based deep learning-radiomics model for the non-invasive preoperative discrimination of tumor deposit...

Comparing the performance of statistical and machine learning survival models in predicting timing and determinants of postnatal care in Rwanda.

BACKGROUND: Postnatal care (PNC) remains the least utilized component of the maternal care continuum despite its critical role in preventing maternal ...

Multi-dimensional analysis revealing the mechanism of T-2 toxin driving liver fibrosis: integration of network toxicology, machine learning, experimental verification, and molecular docking.

BACKGROUND: T-2 toxin is a highly toxic mycotoxin commonly present in food and the environment, with accumulating evidence supporting its hepatotoxic ...