Gastroenterology

Predicting and Monitoring Immune Checkpoint Inhibitor Therapy Using Artificial Intelligence in Pancreatic Cancer.

Pancreatic cancer remains one of the most lethal cancers, primarily due to its late diagnosis and limited treatment options. This review examines the challenges and potential of using immunotherapy to treat pancreatic cancer, highlighting the role of artificial intelligence (AI) as a promising tool to enhance early detection and monitor the effectiveness of these therapies. By synthesizing recent ...

Large-scale multi-center CT and MRI segmentation of pancreas with deep learning.

Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep learning methods. In this retrospective st...

A multi-layer neural network approach for the stability analysis of the Hepatitis B model.

In the present study, we explore the dynamics of Hepatitis B virus infection, a significant global health issue, through a newly developed dynamics sy...

Pred-AHCP: Robust Feature Selection-Enabled Sequence-Specific Prediction of Anti-Hepatitis C Peptides via Machine Learning.

Every year, an estimated 1.5 million people worldwide contract Hepatitis C, a significant contributor to liver problems. Although many studies have ex...

Deep learning in image segmentation for cancer.

This article discusses the role of deep learning (DL) in cancer imaging, focusing on its applications for automatic image segmentation. It highlights ...

Establishing an AI model and application for automated capsule endoscopy recognition based on convolutional neural networks (with video).

BACKGROUND: Although capsule endoscopy (CE) is a crucial tool for diagnosing small bowel diseases, the need to process a vast number of images imposes...

A lightweight deep-learning model for parasite egg detection in microscopy images.

BACKGROUND: Intestinal parasitic infections are still a serious public health problem in developing countries, and the diagnosis of parasitic infectio...

Random survival forest algorithm for risk stratification and survival prediction in gastric neuroendocrine neoplasms.

This study aimed to construct and assess a machine-learning algorithm designed to forecast survival rates and risk stratification for patients with ga...

Computed tomography enterography radiomics and machine learning for identification of Crohn's disease.

BACKGROUND: Crohn's disease is a severe chronic and relapsing inflammatory bowel disease. Although contrast-enhanced computed tomography enterography ...

Evaluating the Efficacy of Deep Learning Reconstruction in Reducing Radiation Dose for Computer-Aided Volumetry for Liver Tumor: A Phantom Study.

OBJECTIVE: The purpose of this study was to compare radiation dose reduction capability for accurate liver tumor measurements of a computer-aided volu...

A deep learning approach for gastroscopic manifestation recognition based on Kyoto Gastritis Score.

OBJECTIVE: The risk of gastric cancer can be predicted by gastroscopic manifestation recognition and the Kyoto Gastritis Score. This study aims to val...

MCI Net: Mamba- Convolutional lightweight self-attention medical image segmentation network.

With the development of deep learning in the field of medical image segmentation, various network segmentation models have been developed. Currently, ...

Interpretable prediction of 30-day mortality in patients with acute pancreatitis based on machine learning and SHAP.

BACKGROUND: Severe acute pancreatitis (SAP) can be fatal if left unrecognized and untreated. The purpose was to develop a machine learning (ML) model ...

Artificial intelligence-aided colonoscopic differential diagnosis between Crohn's disease and gastrointestinal tuberculosis.

BACKGROUND AND AIM: Differentiating between Crohn's disease (CD) and gastrointestinal tuberculosis (GITB) is challenging. We aimed to evaluate the cli...

Clinical validation of an AI-based pathology tool for scoring of metabolic dysfunction-associated steatohepatitis.

Metabolic dysfunction-associated steatohepatitis (MASH) is a major cause of liver-related morbidity and mortality, yet treatment options are limited. ...

A deep learning framework for hepatocellular carcinoma diagnosis using MS1 data.

Clinical proteomics analysis is of great significance for analyzing pathological mechanisms and discovering disease-related biomarkers. Using computat...

A Multi-Task Based Deep Learning Framework With Landmark Detection for MRI Couinaud Segmentation.

To achieve precise Couinaud liver segmentation in preoperative planning for hepatic surgery, accommodating the complex anatomy and significant variati...

Accuracy of machine learning in diagnosing microsatellite instability in gastric cancer: A systematic review and meta-analysis.

BACKGROUND: Significant challenges persist in the early identification of microsatellite instability (MSI) within current clinical practice. In recent...

From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers.

OBJECTIVES: Routine monitoring of renal and hepatic function during chemotherapy ensures that treatment-related organ damage has not occurred and clea...

Transformative artificial intelligence in gastric cancer: Advancements in diagnostic techniques.

Gastric cancer represents a significant global health challenge with elevated incidence and mortality rates, highlighting the need for advancements in...