Gastroenterology

AI-powered innovations in pancreatitis imaging: a comprehensive literature synthesis.

Early identification of pancreatitis remains a significant clinical diagnostic challenge that impacts patient outcomes. The evolution of quantitative imaging followed by deep learning models has shown great promise in the non-invasive diagnosis of pancreatitis and its complications. We provide an overview of advancements in diagnostic imaging and quantitative imaging methods along with the evoluti...

Foundation models in gastrointestinal endoscopic AI: Impact of architecture, pre-training approach and data efficiency.

Pre-training deep learning models with large data sets of natural images, such as ImageNet, has become the standard for endoscopic image analysis. This approach is generally superior to training from scratch, due to the scarcity of high-quality medical imagery and labels. However, it is still unknown whether the learned features on natural imagery provide an optimal starting point for the downstre...

Current and Emerging Applications of Artificial Intelligence (AI) in the Management of Pancreatobiliary (PB) disorders.

PURPOSE OF REVIEW: In this review, we aim to summarize the existing literature and future directions on the use of artificial intelligence (AI) for th...

Machine learning of Raman spectra predicts drug release from polysaccharide coatings for targeted colonic delivery.

Colonic drug delivery offers numerous pharmaceutical opportunities, including direct access to local therapeutic targets and drug bioavailability bene...

Clinical artificial intelligence: teaching a large language model to generate recommendations that align with guidelines for the surgical management of GERD.

BACKGROUND: Large Language Models (LLMs) provide clinical guidance with inconsistent accuracy due to limitations with their training dataset. LLMs are...

Utilizing machine learning to integrate single-cell and bulk RNA sequencing data for constructing and validating a novel cell adhesion molecules related prognostic model in gastric cancer.

BACKGROUND: Cell adhesion molecules (CAMs) play a vital role in cell-cell interactions, immune response modulation, and tumor cell migration. However,...

Development and experimental validation of hypoxia-related gene signatures for osteosarcoma diagnosis and prognosis based on WGCNA and machine learning.

Osteosarcoma (OS) is the most common primary malignant tumour of the bone with high mortality. Here, we comprehensively analysed the hypoxia signallin...

Assessing the performance of AI-assisted technicians in liver segmentation, Couinaud division, and lesion detection: a pilot study.

BACKGROUND: In patients with primary and secondary liver cancer, the number and sizes of lesions, their locations within the Couinaud segments, and th...

Diagnosis of Hirschsprung disease by analyzing acetylcholinesterase staining using artificial intelligence.

OBJECTIVES: Classical Hirschsprung disease (HD) is defined by the absence of ganglion cells in the rectosigmoid colon. The diagnosis is made from rect...

AI support for colonoscopy quality control using CNN and transformer architectures.

BACKGROUND: Construct deep learning models for colonoscopy quality control using different architectures and explore their decision-making mechanisms.

Multi-transcriptomics analysis of microvascular invasion-related malignant cells and development of a machine learning-based prognostic model in hepatocellular carcinoma.

BACKGROUND: Microvascular invasion (MVI) stands as a pivotal pathological hallmark of hepatocellular carcinoma (HCC), closely linked to unfavorable pr...

Predicting Disease-Metabolite Associations Based on the Metapath Aggregation of Tripartite Heterogeneous Networks.

The exploration of the interactions between diseases and metabolites holds significant implications for the diagnosis and treatment of diseases. Howev...

An integrated platform for decoding hydrophilic peptide fingerprints of hepatocellular carcinoma using artificial intelligence and two-dimensional nanosheets.

Hydrophilic peptides (HPs) play a critical role in the pathogenesis of hepatocellular carcinoma (HCC). However, the comprehensive and in-depth high-th...

Deep learning ensemble approach with explainable AI for lung and colon cancer classification using advanced hyperparameter tuning.

Lung and colon cancers are leading contributors to cancer-related fatalities globally, distinguished by unique histopathological traits discernible th...

Development and validation of prediction models for nosocomial infection and prognosis in hospitalized patients with cirrhosis.

BACKGROUND: Nosocomial infections (NIs) frequently occur and adversely impact prognosis for hospitalized patients with cirrhosis. This study aims to d...

DCNNLFS: A Dilated Convolutional Neural Network With Late Fusion Strategy for Intelligent Classification of Gastric Histopathology Images.

Gastric cancer has a high incidence rate, significantly threatening patients' health. Gastric histopathology images can reliably diagnose related dise...

Precision and Robust Models on Healthcare Institution Federated Learning for Predicting HCC on Portal Venous CT Images.

Hepatocellular carcinoma (HCC), the most common type of liver cancer, poses significant challenges in detection and diagnosis. Medical imaging, especi...

Preoperative prediction of rectal Cancer staging combining MRI deep transfer learning, radiomics features, and clinical factors: accurate differentiation from stage T2 to T3.

BACKGROUND: This study evaluates the efficacy of integrating MRI deep transfer learning, radiomic signatures, and clinical variables to accurately pre...

Lesion-Decoupling-Based Segmentation With Large-Scale Colon and Esophageal Datasets for Early Cancer Diagnosis.

Lesions of early cancers often show flat, small, and isochromatic characteristics in medical endoscopy images, which are difficult to be captured. By ...

Linked Color Imaging with Artificial Intelligence Improves the Detection of Early Gastric Cancer.

INTRODUCTION: Esophagogastroduodenoscopy is the most important tool to detect gastric cancer (GC). In this study, we developed a computer-aided detect...