Gastroenterology

Comparative outcomes of minimally invasive and robotic-assisted esophagectomy.

OBJECTIVE: Minimally invasive esophagectomy (MIE) has demonstrated superior outcomes compared to open approaches. The myriad of techniques has precluded the recommendation of a standard approach. The addition of robotics to esophageal resection has potential benefits. We sought to examine the outcomes with MIE to include robotics.

[Artificial Intelligence in Endoscopy: Deep Neural Nets for Endoscopic Computer Vision - Methods & Perspectives].

Artificial neural networks, as a specific approach towards artificial intelligence (AI), can open up a variety of new perspectives for endoscopy, such as automated lesion detection and the precise prediction of a lesion's histology by its endoscopic appearance. Whilst early experiments do suggest an enormous potential for these methods, public expectations on their application in various fields of...

Assessment of Machine Learning Detection of Environmental Enteropathy and Celiac Disease in Children.

IMPORTANCE: Duodenal biopsies from children with enteropathies associated with undernutrition, such as environmental enteropathy (EE) and celiac disea...

Ontology-Based Interactive Visualization of Patient-Generated Research Questions.

BACKGROUND: Crohn's disease and colitis are chronic conditions that affect every facet of patients' lives (e.g., social interaction, family, work, die...

Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer.

Microsatellite instability determines whether patients with gastrointestinal cancer respond exceptionally well to immunotherapy. However, in clinical ...

VesselNet: A deep convolutional neural network with multi pathways for robust hepatic vessel segmentation.

Extraction or segmentation of organ vessels is an important task for surgical planning and computer-aided diagnoses. This is a challenging task due to...

Automated Machine Learning Diagnostic Support System as a Computational Biomarker for Detecting Drug-Induced Liver Injury Patterns in Whole Slide Liver Pathology Images.

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Explant liver evaluation decodes the mystery of cryptogenic cirrhosis!

BACKGROUND AND AIM: To determine the concordance of liver explants with the pretransplant diagnosis.

[French comment on article A deep neural network improves endoscopic detection of early gastric cancer without blind spots].

Applications of deep learning for the analysis of medical data.

Over the past decade, deep learning has demonstrated superior performances in solving many problems in various fields of medicine compared with other ...

Pre-Treatment Biomarkers of Anti-Tumour Necrosis Factor Therapy Response in Crohn's Disease-A Systematic Review and Gene Ontology Analysis.

The most prominent treatment for the serious cases of Crohn's disease (CD) are biological tumour necrosis factor (TNF) inhibitors. Unfortunately, ther...

Deep transfer learning methods for colon cancer classification in confocal laser microscopy images.

PURPOSE: The gold standard for colorectal cancer metastases detection in the peritoneum is histological evaluation of a removed tissue sample. For fee...

Machine Learning Prediction of Liver Stiffness Using Clinical and T2-Weighted MRI Radiomic Data.

The purpose of this study is to develop a machine learning model to categorically classify MR elastography (MRE)-derived liver stiffness using clinic...

Does transanal total mesorectal excision of rectal cancer improve histopathology metrics and/or complication rates? A meta-analysis.

BACKGROUND: The aim of this meta-analysis was to determine whether transanal total mesorectal excision (taTME) improves histopathology metrics and/or ...

Reducing inter-observer variability and interaction time of MR liver volumetry by combining automatic CNN-based liver segmentation and manual corrections.

PURPOSE: To compare manual corrections of liver masks produced by a fully automatic segmentation method based on convolutional neural networks (CNN) w...

The Pelvis-First Approach for Robotic Proctectomy in Patients with Redundant Abdominal Colon.

BACKGROUND: Robotic surgery is increasingly performed for low rectal cancer.1 A redundant sigmoid colon makes retraction and pelvic dissection challen...

Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features.

OBJECTIVES: To develop a proof-of-concept "interpretable" deep learning prototype that justifies aspects of its predictions from a pre-trained hepatic...

Neo-adjuvant chemoradiotherapy response prediction using MRI based ensemble learning method in rectal cancer patients.

OBJECTIVES: The aim of this study was to investigate and validate the performance of individual and ensemble machine learning models (EMLMs) based on ...

Endoscopic Surveillance of Barrett's Esophagus Using Volumetric Laser Endomicroscopy With Artificial Intelligence Image Enhancement.

Classification for invasion depth of esophageal squamous cell carcinoma using a deep neural network compared with experienced endoscopists.

BACKGROUND AND AIMS: Cancer invasion depth is a critical factor affecting the choice of treatment in patients with superficial squamous cell carcinoma...