Gastroenterology

Identification of prognostic signatures in remnant gastric cancer through an interpretable risk model based on machine learning: a multicenter cohort study.

OBJECTIVE: The purpose of this study was to develop an individual survival prediction model based on multiple machine learning (ML) algorithms to predict survival probability for remnant gastric cancer (RGC).

Efficiency of endoscopic artificial intelligence in the diagnosis of early esophageal cancer.

BACKGROUND: The accuracy of artificial intelligence (AI) and experts in diagnosing early esophageal cancer (EC) and its infiltration depth was summarized and analyzed, thus identifying the advantages of AI over traditional manual diagnosis, with a view to more accurately assisting doctors in evaluating the patients' conditions and improving their cure and survival rates.

Application of artificial intelligence in pancreas endoscopic ultrasound imaging- A systematic review.

The pancreas is a vital organ in digestive system which has significant health implications. It is imperative to evaluate and identify malignant pancr...

Deep learning-aided 3D proxy-bridged region-growing framework for multi-organ segmentation.

Accurate multi-organ segmentation in 3D CT images is imperative for enhancing computer-aided diagnosis and radiotherapy planning. However, current dee...

Multiclassification of Hepatic Cystic Echinococcosis by Using Multiple Kernel Learning Framework and Ultrasound Images.

UNLABELLED: To properly treat and care for hepatic cystic echinococcosis (HCE), it is essential to make an accurate diagnosis before treatment.

Machine learning for predicting liver and/or lung metastasis in colorectal cancer: A retrospective study based on the SEER database.

OBJECTIVE: This study aims to establish a machine learning (ML) model for predicting the risk of liver and/or lung metastasis in colorectal cancer (CR...

UViT-Seg: An Efficient ViT and U-Net-Based Framework for Accurate Colorectal Polyp Segmentation in Colonoscopy and WCE Images.

Colorectal cancer (CRC) stands out as one of the most prevalent global cancers. The accurate localization of colorectal polyps in endoscopy images is ...

Artificial Intelligence for Quantifying Cumulative Small Bowel Disease Severity on CT-Enterography in Crohn's Disease.

INTRODUCTION: Assessing the cumulative degree of bowel injury in ileal Crohn's disease (CD) is difficult. We aimed to develop machine learning (ML) me...

Deep learning-accelerated T2WI: image quality, efficiency, and staging performance against BLADE T2WI for gastric cancer.

PURPOSE: The purpose of our study is to investigate image quality, efficiency, and diagnostic performance of a deep learning-accelerated single-shot b...

Development and validation of machine learning models and nomograms for predicting the surgical difficulty of laparoscopic resection in rectal cancer.

BACKGROUND: The objective of this study is to develop and validate a machine learning (ML) prediction model for the assessment of laparoscopic total m...

Improved deep learning for automatic localisation and segmentation of rectal cancer on T2-weighted MRI.

INTRODUCTION: The automatic segmentation approaches of rectal cancer from magnetic resonance imaging (MRI) are very valuable to relieve physicians fro...

BiliQML: a supervised machine-learning model to quantify biliary forms from digitized whole slide liver histopathological images.

The progress of research focused on cholangiocytes and the biliary tree during development and following injury is hindered by limited available quant...

An explainable machine learning model to predict early and late acute kidney injury after major hepatectomy.

BACKGROUND: Risk assessment models for acute kidney injury (AKI) after major hepatectomy that differentiate between early and late AKI are lacking. Th...

Imaging segmentation mechanism for rectal tumors using improved U-Net.

OBJECTIVE: In radiation therapy, cancerous region segmentation in magnetic resonance images (MRI) is a critical step. For rectal cancer, the automatic...

Integrated multi-omics analysis and machine learning developed a prognostic model based on mitochondrial function in a large multicenter cohort for Gastric Cancer.

BACKGROUND: Gastric cancer (GC) is a common and aggressive type of cancer worldwide. Despite recent advancements in its treatment, the prognosis for p...

Endoscopic surgical field clarity index: An artificial intelligence-based measure of transnasal endoscopic surgical field quality.

Clear visualization during transnasal endoscopic surgery (TNES) is crucial for safe, efficient surgery. The endoscopic surgical field clarity index (E...

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 and have complicated relationships with essential ve...

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 identification of unidentified stomach adenocarci...

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 approximately 350 million infections globally. To accel...

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 about the biomarkers associated with immunotherapy ...