Gastroenterology

Efficacy of endoscopic ultrasound with artificial intelligence for the diagnosis of gastrointestinal stromal tumors.

BACKGROUND: Although endoscopic ultrasound (EUS) is reported to be suitable for determining the layer from which subepithelial lesions (SELs) originate, it is difficult to distinguish gastrointestinal stromal tumor (GIST) from non-GIST using only EUS images. If artificial intelligence (AI) can be used for the diagnosis of SELs, it should provide several benefits, including objectivity, simplicity,...

CT-based deep learning radiomics analysis for evaluation of serosa invasion in advanced gastric cancer.

PURPOSE: This work aimed to develop and validate a deep learning radiomics model for evaluating serosa invasion in gastric cancer.

Incorporating standardised reporting guidelines in clinical trials of artificial intelligence in gastrointestinal endoscopy.

Transcervical (SP) and Transhiatal DaVinci Robotic Esophagectomy: A Cadaveric Study.

BACKGROUND: This is a preclinical cadaveric study to investigate the feasibility of a fully robotic McKeown esophagectomy in simultaneous rendezvous t...

Scattering Signatures of Normal versus Abnormal Livers with Support Vector Machine Classification.

Fifty years of research on the nature of backscatter from tissues has resulted in a number of promising diagnostic parameters. We recently introduced ...

Fluence Map Prediction Using Deep Learning Models - Direct Plan Generation for Pancreas Stereotactic Body Radiation Therapy.

Treatment planning for pancreas stereotactic body radiation therapy (SBRT) is a difficult and time-consuming task. In this study, we aim to develop a...

Lower Incidence of Postoperative Pulmonary Complications Following Robot-Assisted Minimally Invasive Esophagectomy for Esophageal Cancer: Propensity Score-Matched Comparison to Conventional Minimally Invasive Esophagectomy.

BACKGROUND: Whether robot-assisted minimally invasive surgery (RAMIE) is more beneficial than conventional minimally invasive surgery (MIE) remains un...

Artificial intelligence technology applications in the pathologic diagnosis of the gastrointestinal tract.

Artificial intelligence (AI) is a complex technology with a steady flow of new applications, including in the pathology laboratory. Applications of AI...

Application of optical character recognition with natural language processing for large-scale quality metric data extraction in colonoscopy reports.

BACKGROUND AND AIMS: Colonoscopy is commonly performed for colorectal cancer screening in the United States. Reports are often generated in a non-stan...

Ontological approach to the knowledge systematization of a toxic process and toxic course representation framework for early drug risk management.

Various types of drug toxicity can halt the development of a drug. Because drugs are xenobiotics, they inherently have the potential to cause injury. ...

Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach.

Oral bioavailability (OBA)-related pharmacokinetic properties, such as aqueous solubility, lipophilicity, and intestinal membrane permeability, play a...

Deep learning-based lumbosacral reconstruction for difficulty prediction of percutaneous endoscopic transforaminal discectomy at L5/S1 level: A retrospective cohort study.

BACKGROUND: Deep learning has been validated as a promising technique for automatic segmentation and rapid three-dimensional (3D) reconstruction of lu...

Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients With Hepatitis C Cirrhosis.

IMPORTANCE: Deep learning, a family of machine learning models that use artificial neural networks, has achieved great success at predicting outcomes ...

Improved classification and localization approach to small bowel capsule endoscopy using convolutional neural network.

BACKGROUND: Although great advances in artificial intelligence for interpreting small bowel capsule endoscopy (SBCE) images have been made in recent y...

ASO Author Reflections: Clinical Benefit of Robot-Assisted Minimally Invasive Esophagectomy over Conventional Minimally Invasive Esophagectomy.

Multi-level feature aggregation network for instrument identification of endoscopic images.

Identification of surgical instruments is crucial in understanding surgical scenarios and providing an assistive process in endoscopic image-guided su...

Robotic "Double Loop" Roux-en-Y gastric bypass reduces the risk of postoperative internal hernias: a prospective observational study.

BACKGROUND: Internal herniation (IH) is a potentially serious complication after laparoscopic Roux-en-Y gastric bypass (RYGB). The aim of the study is...

Application of artificial intelligence using a novel EUS-based convolutional neural network model to identify and distinguish benign and malignant hepatic masses.

BACKGROUND AND AIMS: Detection and characterization of focal liver lesions (FLLs) is key for optimizing treatment for patients who may have a primary ...

HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy.

Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and l...

Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning.

PURPOSE: Microvascular invasion (MVI) is a valuable predictor of survival in hepatocellular carcinoma (HCC) patients. This study developed predictive ...