Gastroenterology

Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC-MS-Based Untargeted Metabolomics.

Untargeted metabolomics based on liquid chromatography coupled with mass spectrometry (LC-MS) can detect thousands of features in samples and produce highly complex datasets. The accurate extraction of meaningful features and the building of discriminant models are two crucial steps in the data analysis pipeline of untargeted metabolomics. In this study, pure ion chromatograms were extracted from ...

A frogspawn-like Ag@C core-shell structure for an ultrasensitive label-free electrochemical immunosensing of carcinoembryonic antigen in blood plasma.

A three-dimensional (3D) frogspawn-like structure was achieved by simply coating nano-carbon outside silver nanospheres (Ag@C NFS) and used as a probe to capture the anti-carcinoembryonic antigen for the electrochemical immunosensing of carcinoembryonic antigen (CEA), a typical biomarker of several diseases such as gastric cancer, intestinal cancer and colon cancer. Moreover, Ag@C nanocables (Ag@C...

Comparative Effectiveness of Machine Learning Approaches for Predicting Gastrointestinal Bleeds in Patients Receiving Antithrombotic Treatment.

IMPORTANCE: Anticipating the risk of gastrointestinal bleeding (GIB) when initiating antithrombotic treatment (oral antiplatelets or anticoagulants) i...

A novel multi-DoF surgical robotic system for brachytherapy on liver tumor: Design and control.

PURPOSE: Radioactive seed implantation is an effective invasive treatment method for malignant liver tumors in hepatocellular carcinomas. However, cha...

Improvement of image quality for pancreatic cancer using deep learning-generated virtual monochromatic images: Comparison with single-energy computed tomography.

PURPOSE: To construct a deep convolutional neural network that generates virtual monochromatic images (VMIs) from single-energy computed tomography (S...

A Machine Learning Model Approach to Risk-Stratify Patients With Gastrointestinal Cancer for Hospitalization and Mortality Outcomes.

PURPOSE: Patients with gastrointestinal (GI) cancer frequently experience unplanned hospitalizations, but predictive tools to identify high-risk patie...

Chinese guideline for the application of rectal cancer staging recognition systems based on artificial intelligence platforms (2021 edition).

Gastric polyp detection in gastroscopic images using deep neural network.

This paper presents the research results of detecting gastric polyps with deep learning object detection method in gastroscopic images. Gastric polyps...

Machine learning for the prediction of pathologic pneumatosis intestinalis.

BACKGROUND: The radiographic finding of pneumatosis intestinalis can indicate a spectrum of underlying processes ranging from a benign finding to a li...

An artificial intelligence deep learning model for identification of small bowel obstruction on plain abdominal radiographs.

OBJECTIVES: Small bowel obstruction is a common surgical emergency which can lead to bowel necrosis, perforation and death. Plain abdominal X-rays are...

A deep learning approach for 2D ultrasound and 3D CT/MR image registration in liver tumor ablation.

BACKGROUND AND OBJECTIVE: Liver tumor ablation is often guided by ultrasound (US). Due to poor image quality, intraoperative US is fused with preopera...

Liver tumor segmentation using 2.5D UV-Net with multi-scale convolution.

Liver tumor segmentation networks are generally based on U-shaped encoder-decoder network with 2D or 3D structure. However, 2D networks lose the inter...

Application of Supervised SOM Algorithms in Predicting the Hepatotoxic Potential of Drugs.

The hepatotoxic potential of drugs is one of the main reasons why a number of drugs never reach the market or have to be withdrawn from the market. Th...

Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit.

Acute gastrointestinal bleeding is the most common gastrointestinal cause for hospitalization. For high-risk patients requiring intensive care unit st...

Using Graph Representation Learning to Predict Salivary Cortisol Levels in Pancreatic Cancer Patients.

Cortisol is a glucocorticoid hormone that is critical to immune system functioning. Studies show that prolonged exposure to high levels of cortisol ca...

Joint segmentation and classification of hepatic lesions in ultrasound images using deep learning.

OBJECTIVES: To develop a convolutional neural network system to jointly segment and classify a hepatic lesion selected by user clicks in ultrasound im...

Impact of polyphenol-rich extracts of fruits and seeds on viability of human intestinal and liver cells .

(Kakadu plum) is a native Australian fruit consumed by Indigenous Australians for centuries. Commercial interest in has increased in recent years du...

Robotic versus laparoscopic distal gastrectomy in patients with gastric cancer: a propensity score-matched analysis.

BACKGROUND: Robotic distal gastrectomy (RDG) has been increasingly used for the treatment of gastric cancer (GC). However, whether RDG has a clinical ...

Comparison of the short-term operative, Oncological, and Functional Outcomes between two types of robot-assisted total mesorectal excision for rectal cancer: Da Vinci versus Micro Hand S surgical robot.

OBJECTIVE: This study aimed to evaluate the difference of two various robotic technology applied in R- Total mesorectal excision (TME).

Distinguishing Rectal Cancer from Colon Cancer Based on the Support Vector Machine Method and RNA-sequencing Data.

Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Several studies have indicated that rectal cancer is significantly diff...