Gastroenterology

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

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Hepatic and portal vein segmentation with dual-stream deep neural network.

BACKGROUND: Liver lesions mainly occur inside the liver parenchyma, which are difficult to locate an...

Deep learning and machine learning approaches to classify stomach distant metastatic tumors using DNA methylation profiles.

Distant metastasis of cancer is a significant contributor to cancer-related complications, and early...

Integrated multi-omics analysis and machine learning identify hub genes and potential mechanisms of resistance to immunotherapy in gastric cancer.

BACKGROUND: Patients with gastric cancer respond poorly to immunotherapy. There are still unknowns a...

HBCVTr: an end-to-end transformer with a deep neural network hybrid model for anti-HBV and HCV activity predictor from SMILES.

Hepatitis B and C viruses (HBV and HCV) are significant causes of chronic liver diseases, with appro...

VENet: Variational energy network for gland segmentation of pathological images and early gastric cancer diagnosis of whole slide images.

BACKGROUND AND OBJECTIVE: Gland segmentation of pathological images is an essential but challenging ...

Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction.

Hepatitis C, a particularly dangerous form of viral hepatitis caused by hepatitis C virus (HCV) infe...

Machine learning for predicting colon cancer recurrence.

INTRODUCTION: Colorectal cancer (CRC) is a global public health concern, ranking among the most comm...

Development of Novel Methods for QSAR Modeling by Machine Learning Repeatedly: A Case Study on Drug Distribution to Each Tissue.

Artificial intelligence is expected to help identify excellent candidates in drug discovery. However...

Machine-learning-based plasma metabolomic profiles for predicting long-term complications of cirrhosis.

BACKGROUND AND AIMS: The complications of liver cirrhosis occur after long asymptomatic stages of pr...

Improving diagnosis and outcome prediction of gastric cancer via multimodal learning using whole slide pathological images and gene expression.

For the diagnosis and outcome prediction of gastric cancer (GC), machine learning methods based on w...

The performance of artificial intelligence large language model-linked chatbots in surgical decision-making for gastroesophageal reflux disease.

BACKGROUND: Large language model (LLM)-linked chatbots may be an efficient source of clinical recomm...

Deep learning assists detection of esophageal cancer and precursor lesions in a prospective, randomized controlled study.

Endoscopy is the primary modality for detecting asymptomatic esophageal squamous cell carcinoma (ESC...

Validation of artificial intelligence-based bowel preparation assessment in screening colonoscopy (with video).

BACKGROUND AND AIMS: Accurate bowel preparation assessment is essential for determining colonoscopy ...

Artificial intelligence in liver cancer - new tools for research and patient management.

Liver cancer has high incidence and mortality globally. Artificial intelligence (AI) has advanced ra...

Global contextual representation via graph-transformer fusion for hepatocellular carcinoma prognosis in whole-slide images.

Current methods of digital pathological images typically employ small image patches to learn local r...

A Swin transformer encoder-based StyleGAN for unbalanced endoscopic image enhancement.

With the rapid development of artificial intelligence, automated endoscopy-assisted diagnostic syste...

Single-Image-Based Deep Learning for Segmentation of Early Esophageal Cancer Lesions.

Accurate segmentation of lesions is crucial for diagnosis and treatment of early esophageal cancer (...

HRU-Net: A high-resolution convolutional neural network for esophageal cancer radiotherapy target segmentation.

BACKGROUND AND OBJECTIVE: The effective segmentation of esophageal squamous carcinoma lesions in CT ...

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