Gastroenterology

Artificial intelligence based system for predicting permanent stoma after sphincter saving operations.

Although the goal of rectal cancer treatment is to restore gastrointestinal continuity, some patient...

Laparoscopic vs. robotic colectomy for left-sided diverticulitis.

Diverticulitis is a prevalent gastrointestinal disease that often warrants surgical intervention. Ho...

Managing Ulcerative Colitis and Crohn's Disease: Should the Target Be Endoscopy, Histology, or Both?

In inflammatory bowel disease (IBD), mucosal healing is the primary long-term treatment goal, encomp...

A new architecture combining convolutional and transformer-based networks for automatic 3D multi-organ segmentation on CT images.

PURPOSE: Deep learning-based networks have become increasingly popular in the field of medical image...

Public Imaging Datasets of Gastrointestinal Endoscopy for Artificial Intelligence: a Review.

With the advances in endoscopic technologies and artificial intelligence, a large number of endoscop...

Using robotics to move a neurosurgeon's hands to the tip of their endoscope.

A major advantage of surgical robots is that they can reduce the invasiveness of a procedure by enab...

Deep learning to predict lymph node status on pre-operative staging CT in patients with colon cancer.

INTRODUCTION: Lymph node (LN) metastases are an important determinant of survival in patients with c...

Deep learning driven de novo drug design based on gastric proton pump structures.

Existing drugs often suffer in their effectiveness due to detrimental side effects, low binding affi...

Robot-assisted fluorescent sentinel lymph node identification in early-stage colon cancer.

BACKGROUND: Patients with cT1-2 colon cancer (CC) have a 10-20% risk of lymph node metastases. Senti...

What we need to do to integrate artificial intelligence into GI endoscopy.

An Overview of Robotic Colorectal Surgery Adoption and Training in Brazil.

Robotic surgical systems have rapidly become integrated into colorectal surgery practice in recent ...

Artificial Intelligence and liver: Opportunities and barriers.

Artificial Intelligence (AI) has recently been shown as an excellent tool for the study of the liver...

Identification of Fast Progressors Among Patients With Nonalcoholic Steatohepatitis Using Machine Learning.

BACKGROUND AND AIMS: There is a high unmet need to develop noninvasive tools to identify nonalcoholi...

Automating Ground Truth Annotations for Gland Segmentation Through Immunohistochemistry.

Microscopic evaluation of glands in the colon is of utmost importance in the diagnosis of inflammato...

Deep learning-based iodine contrast-augmenting algorithm for low-contrast-dose liver CT to assess hypovascular hepatic metastasis.

PURPOSE: To investigate the image quality and diagnostic performance of low-contrast-dose liver CT u...

Liver dysfunction on admission worsens clinical manifestations and outcomes of coronavirus disease 2019.

BACKGROUND: Liver dysfunction was common in coronavirus disease 2019 (COVID-19), but its association...

Successful Development of a Natural Language Processing Algorithm for Pancreatic Neoplasms and Associated Histologic Features.

OBJECTIVES: Natural language processing (NLP) algorithms can interpret unstructured text for commonl...

Deep learning in negative small-bowel capsule endoscopy improves small-bowel lesion detection and diagnostic yield.

OBJECTIVES: Although several studies have shown the usefulness of artificial intelligence to identif...

The Fidelity of Artificial Intelligence to Multidisciplinary Tumor Board Recommendations for Patients with Gastric Cancer: A Retrospective Study.

PURPOSE: Due to significant growth in the volume of information produced by cancer research, staying...

Real-time liver motion estimation via deep learning-based angle-agnostic X-ray imaging.

BACKGROUND: Real-time liver imaging is challenged by the short imaging time (within hundreds of mill...

Collagen fiber centerline tracking in fibrotic tissue via deep neural networks with variational autoencoder-based synthetic training data generation.

The role of fibrillar collagen in the tissue microenvironment is critical in disease contexts rangin...