Gastroenterology

Latest AI and machine learning research in gastroenterology for healthcare professionals.

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Opportunistic Screening for Pancreatic Cancer using Computed Tomography Imaging and Radiology Reports

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer, with most cases diagnosed at stage IV and a five-year overall survival rate below 5%. Early detection and prognosis modeling are crucial for improving patient outcomes and guiding early intervention strategies. In this study, we developed and evaluated a deep learning fusion model that integrates radiology reports and CT imag...

AI-Assisted Colonoscopy: Polyp Detection and Segmentation using Foundation Models

In colonoscopy, 80% of the missed polyps could be detected with the help of Deep Learning models. In the search for algorithms capable of addressing this challenge, foundation models emerge as promising candidates. Their zero-shot or few-shot learning capabilities, facilitate generalization to new data or tasks without extensive fine-tuning. A concept that is particularly advantageous in the med...

PolypSegTrack: Unified Foundation Model for Colonoscopy Video Analysis

Early detection, accurate segmentation, classification and tracking of polyps during colonoscopy are critical for preventing colorectal cancer. Many...

Evaluating accuracy in artificial intelligence-powered serial segmentation for sectional images applied to morphological studies with three-dimensional reconstruction.

Three-dimensional (3D) reconstruction is time-consuming owing to segmentation work. We evaluated the accuracy of the artificial intelligence (AI)-base...

Mar 31 2025 39948740
Facing the challenges of autoimmune pancreatitis diagnosis: The answer from artificial intelligence.

Current diagnosis of autoimmune pancreatitis (AIP) is challenging and often requires combining multiple dimensions. There is a need to explore new met...

Mar 28 2025 40182594
Vision Language Models versus Machine Learning Models Performance on Polyp Detection and Classification in Colonoscopy Images

Introduction: This study provides a comprehensive performance assessment of vision-language models (VLMs) against established convolutional neural n...

Self-Reported Confidence of Large Language Models in Gastroenterology: Analysis of Commercial, Open-Source, and Quantized Models

This study evaluated self-reported response certainty across several large language models (GPT, Claude, Llama, Phi, Mistral, Gemini, Gemma, and Qwe...

LGPS: A Lightweight GAN-Based Approach for Polyp Segmentation in Colonoscopy Images

Colorectal cancer (CRC) is a major global cause of cancer-related deaths, with early polyp detection and removal during colonoscopy being crucial fo...

A Comparative Analysis of Image Descriptors for Histopathological Classification of Gastric Cancer

Gastric cancer ranks as the fifth most common and fourth most lethal cancer globally, with a dismal 5-year survival rate of approximately 20%. Despi...

Construction and validation of machine learning-based predictive model for colorectal polyp recurrence one year after endoscopic mucosal resection.

BACKGROUND: Colorectal polyps are precancerous diseases of colorectal cancer. Early detection and resection of colorectal polyps can effectively reduc...

Mar 21 2025 40124266
Learning to Efficiently Adapt Foundation Models for Self-Supervised Endoscopic 3D Scene Reconstruction from Any Cameras

Accurate 3D scene reconstruction is essential for numerous medical tasks. Given the challenges in obtaining ground truth data, there has been an inc...

FedSAF: A Federated Learning Framework for Enhanced Gastric Cancer Detection and Privacy Preservation

Gastric cancer is one of the most commonly diagnosed cancers and has a high mortality rate. Due to limited medical resources, developing machine lea...

A deep learning model trained on expressed transcripts across different tissue types reveals cell-type codon-optimization preferences.

Species-specific differences in protein translation can affect the design of protein-based drugs. Consequently, efficient expression of recombinant pr...

Mar 20 2025 40156867
Enhancing Pancreatic Cancer Staging with Large Language Models: The Role of Retrieval-Augmented Generation

Purpose: Retrieval-augmented generation (RAG) is a technology to enhance the functionality and reliability of large language models (LLMs) by retrie...

3D Densification for Multi-Map Monocular VSLAM in Endoscopy

Multi-map Sparse Monocular visual Simultaneous Localization and Mapping applied to monocular endoscopic sequences has proven efficient to robustly r...

MSWAL: 3D Multi-class Segmentation of Whole Abdominal Lesions Dataset

With the significantly increasing incidence and prevalence of abdominal diseases, there is a need to embrace greater use of new innovations and tech...

Toward a Human-Centered AI-assisted Colonoscopy System in Australia

While AI-assisted colonoscopy promises improved colorectal cancer screening, its success relies on effective integration into clinical practice, not...

Evaluation of Intra-operative Patient-specific Methods for Point Cloud Completion for Minimally Invasive Liver Interventions

The registration between the pre-operative model and the intra-operative surface is crucial in image-guided liver surgery, as it facilitates the eff...

Artificial intelligence in gastroenterology: Ethical and diagnostic challenges in clinical practice.

This article discusses the manuscript recently published in the , which explores the application of deep learning models in decision-making processes ...

Mar 14 2025 40093670
Mitochondrial mt12361A>G increased risk of metabolic dysfunction-associated steatotic liver disease among non-diabetes.

BACKGROUND: Insulin resistance, lipotoxicity, and mitochondrial dysfunction contribute to the pathogenesis of metabolic dysfunction-associated steatot...

Mar 14 2025 40093674
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