Gastroenterology

FA-Net: A hierarchical feature fusion and interactive attention-based network for dose prediction in liver cancer patients.

Dose prediction is a crucial step in automated radiotherapy planning for liver cancer. Several deep learning-based approaches for dose prediction have been proposed to enhance the design efficiency and quality of radiotherapy plan. However, these approaches usually take CT images and contours of organs at risk (OARs) and planning target volume (PTV) as a multi-channel input and is thus difficult t...

Prediction of 28-Day All-Cause Mortality in Heart Failure Patients with Clostridioides difficile Infection Using Machine Learning Models: Evidence from the MIMIC-IV Database.

INTRODUCTION: Heart failure (HF) may induce bowel hypoperfusion, leading to hypoxia of the villa of the bowel wall and the occurrence of Clostridioides difficile infection (CDI). However, the risk factors for the development of CDI in HF patients have yet to be fully illustrated, especially because of a lack of evidence from real-world data.

Prediction of CD8+T lymphocyte infiltration levels in gastric cancer from contrast-enhanced CT and clinical factors using machine learning.

BACKGROUND: CD8+ T lymphocyte infiltration is closely associated with the prognosis and immunotherapy response of gastric cancer (GC). For now, the ex...

MalariaFlow: A comprehensive deep learning platform for multistage phenotypic antimalarial drug discovery.

Malaria remains a significant global health challenge due to the growing drug resistance of Plasmodium parasites and the failure to block transmission...

Machine learning-based identification of biomarkers and drugs in immunologically cold and hot pancreatic adenocarcinomas.

BACKGROUND: Pancreatic adenocarcinomas (PAADs) often exhibit a "cold" or immunosuppressive tumor milieu, which is associated with resistance to immune...

Deep Learning-Based Prediction of Post-treatment Survival in Hepatocellular Carcinoma Patients Using Pre-treatment CT Images and Clinical Data.

The objective of this study was to develop and evaluate a model for predicting post-treatment survival in hepatocellular carcinoma (HCC) patients usin...

Predictive Study of Machine Learning-Based Multiparametric MRI Radiomics Nomogram for Perineural Invasion in Rectal Cancer: A Pilot Study.

This study aimed to establish and validate the efficacy of a nomogram model, synthesized through the integration of multi-parametric magnetic resonanc...

Effect of deep learning reconstruction on the assessment of pancreatic cystic lesions using computed tomography.

This study aimed to compare the image quality and detection performance of pancreatic cystic lesions between computed tomography (CT) images reconstru...

Focal liver lesion diagnosis with deep learning and multistage CT imaging.

Diagnosing liver lesions is crucial for treatment choices and patient outcomes. This study develops an automatic diagnosis system for liver lesions us...

Decoding IBS: a machine learning approach to psychological distress and gut-brain interaction.

PURPOSE: Irritable bowel syndrome (IBS) is a diagnosis defined by gastrointestinal (GI) symptoms like abdominal pain and changes associated with defec...

Development of machine learning-based personalized predictive models for risk evaluation of hepatocellular carcinoma in hepatitis B virus-related cirrhosis patients with low levels of serum alpha-fetoprotein.

INTRODUCTION AND OBJECTIVES: The increasing incidence of hepatocellular carcinoma (HCC) in China is an urgent issue, necessitating early diagnosis and...

Artificial Intelligence, Large Language Models, and Digital Health in the Management of Alcohol-Associated Liver Disease.

Artificial intelligence (AI) has the potential to aid in the diagnosis and management of alcohol-associated liver disease (ALD). Machine learning algo...

Polyp detection with colonoscopy assisted by the GI Genius artificial intelligence endoscopy module compared with standard colonoscopy in routine colonoscopy practice (COLO-DETECT): a multicentre, open-label, parallel-arm, pragmatic randomised controlled trial.

BACKGROUND: Increased polyp detection during colonoscopy is associated with decreased post-colonoscopy colorectal cancer incidence and mortality. The ...

Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning.

The increasing availability of biomedical data creates valuable resources for developing new deep learning algorithms to support experts, especially i...

A novel model for predicting postoperative liver metastasis in R0 resected pancreatic neuroendocrine tumors: integrating computational pathology and deep learning-radiomics.

BACKGROUND: Postoperative liver metastasis significantly impacts the prognosis of pancreatic neuroendocrine tumor (panNET) patients after R0 resection...

Development and validation of a machine learning-based, point-of-care risk calculator for post-ERCP pancreatitis and prophylaxis selection.

BACKGROUND AND AIMS: A robust model of post-ERCP pancreatitis (PEP) risk is not currently available. We aimed to develop a machine learning-based tool...

AI-KODA score application for cleanliness assessment in video capsule endoscopy frames.

BACKGROUND: Currently, there is no automated method for assessing cleanliness in video capsule endoscopy (VCE). Our objectives were to automate the pr...

Identification and analysis of significant genes in nonalcoholic steatohepatitis-hepatocellular carcinoma transformation: Bioinformatics analysis and machine learning approach.

PURPOSE: Nonalcoholic steatohepatitis (NASH) has been an increasingly significant contributor to hepatocellular carcinoma (HCC). Understanding the pro...

Efficient artificial intelligence-based assessment of the gastroesophageal valve with Hill classification through active learning.

Standardized assessment of the gastroesophageal valve during endoscopy, attainable via the Hill classification, is important for clinical assessment a...

Evaluation of four chatbots in autoimmune liver disease: A comparative analysis.

INTRODUCTION AND OBJECTIVES: Autoimmune liver diseases (AILDs) are rare and require precise evaluation, which is often challenging for medical provide...