Gastroenterology

Accurate prediction of acute pancreatitis severity with integrative blood molecular measurements.

BACKGROUND: Early diagnosis of severe acute pancreatitis (SAP) is essential to minimize its mortality and improve prognosis. We aimed to develop an accurate and applicable machine learning predictive model based on routine clinical testing results for stratifying acute pancreatitis (AP) severity.

Deep learning dose prediction for IMRT of esophageal cancer: The effect of data quality and quantity on model performance.

PURPOSE: To investigate the effect of data quality and quantity on the performance of deep learning (DL) models, for dose prediction of intensity-modulated radiotherapy (IMRT) of esophageal cancer.

DeepDose: a robust deep learning-based dose engine for abdominal tumours in a 1.5 T MRI radiotherapy system.

We present a robust deep learning-based framework for dose calculations of abdominal tumours in a 1.5 T MRI radiotherapy system. For a set of patient ...

The effect of diabetes on major robotic hepatectomy.

Studies regarding the influence of diabetes on perioperative outcomes after major hepatectomy are conflicting. The objective of this study is to analy...

Novel "Modified Bascule Method" for Lymphadenectomy Along the Left Recurrent Laryngeal Nerve During Robot-Assisted Minimally Invasive Esophagectomy.

BACKGROUND: Given the worldwide popularization of conventional minimally invasive esophagectomy (C-MIE), robot-assisted MIE (RAMIE) can be expected to...

Robotic taTME using the da Vinci SP: technical notes in a cadaveric model.

Transanal total mesorectal excision (taTME) is an emerging albeit complex technique to treat rectal cancer. The aim of this proof-of-concept preclinic...

MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer.

Neoadjuvant chemo-radiotherapy (CRT) followed by total mesorectal excision (TME) represents the standard treatment for patients with locally advanced ...

State of the art in robotic rectal surgery: marginal gains worth the pain?

After their first introduction in the 1990s to overcome the limitations of conventional laparoscopic surgery, especially in confined spaces such as th...

Towards radiologist-level cancer risk assessment in CT lung screening using deep learning.

PURPOSE: Lung cancer is the leading cause of cancer mortality in the US, responsible for more deaths than breast, prostate, colon and pancreas cancer ...

Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach.

Drug-induced liver injury (DILI) presentation varies biochemically and histologically. Certain drugs present quite consistent injury patterns, i.e., D...

Prediction of the histology of colorectal neoplasm in white light colonoscopic images using deep learning algorithms.

The treatment plan of colorectal neoplasm differs based on histology. Although new endoscopic imaging systems have been developed, there are clear dia...

Expanding the drug discovery space with predicted metabolite-target interactions.

Metabolites produced in the human gut are known modulators of host immunity. However, large-scale identification of metabolite-host receptor interacti...

Minimally invasive (laparoscopic and robot-assisted) versus open approach for central pancreatectomies: a single-center experience.

BACKGROUND: There continues to be an interest in minimally invasive approaches to pancreatic surgery. At our institution, there has been a progressive...

Automatic liver segmentation using 3D convolutional neural networks with a hybrid loss function.

PURPOSE: Automatic liver segmentation from abdominal computed tomography (CT) images is a fundamental task in computer-assisted liver surgery programs...

Therapeutic Potentials of Selected Antihypertensive Agents and Their Fixed-Dose Combinations Against Trastuzumab-Mediated Cardiotoxicity.

Trastuzumab () is useful in the clinical management of HER2-positive metastatic breast, gastric, and colorectal carcinoma but has been limited by its ...

Risk of chyle leak after robotic versus video-assisted thoracoscopic esophagectomy.

BACKGROUND: We investigate the incidence and risk factors for post-operative outcomes including chyle leak following minimally invasive esophagectomy ...

High-dimensional hepatopath data analysis by machine learning for predicting HBV-related fibrosis.

Chronic HBV infection, the main cause of liver cirrhosis and hepatocellular carcinoma, has become a global health concern. Machine learning algorithms...

Deep learning systems detect dysplasia with human-like accuracy using histopathology and probe-based confocal laser endomicroscopy.

Probe-based confocal laser endomicroscopy (pCLE) allows for real-time diagnosis of dysplasia and cancer in Barrett's esophagus (BE) but is limited by ...

A Deep Learning Model for Classification of Endoscopic Gastroesophageal Reflux Disease.

Gastroesophageal reflux disease (GERD) is a common disease with high prevalence, and its endoscopic severity can be evaluated using the Los Angeles cl...

ASO Author Reflections: "Modified Bascule Method" during Robot-Assisted Minimally Invasive Esophagectomy Has Potential to Decrease Recurrent Laryngeal Nerve Palsy.