Gastroenterology

Artificial Intelligence and the Risk for Intuition Decline in Clinical Medicine.

Artificial intelligence (AI) is revolutionizing big data analytics. In this issue of The American Journal of Gastroenterology, Ahn et al. introduce the AI-cirrhosis-electrocardiogram score that can grade the electrophysiologic cardiac changes present in patients with cirrhosis. Apart from showing excellent accuracy to identify cirrhosis, the AI-cirrhosis-electrocardiogram algorithm identified a bi...

Development of the AI-Cirrhosis-ECG Score: An Electrocardiogram-Based Deep Learning Model in Cirrhosis.

INTRODUCTION: Cirrhosis is associated with cardiac dysfunction and distinct electrocardiogram (ECG) abnormalities. This study aimed to develop a proof-of-concept deep learning-based artificial intelligence (AI) model that could detect cirrhosis-related signals on ECG and generate an AI-Cirrhosis-ECG (ACE) score that would correlate with disease severity.

Development and validation of an ensemble machine learning framework for detection of all-cause advanced hepatic fibrosis: a retrospective cohort study.

BACKGROUND: Cirrhosis is the result of advanced scarring (or fibrosis) of the liver, and is often diagnosed once decompensation with associated compli...

Learning curve for robot-assisted Ivor Lewis esophagectomy.

This study aimed to demonstrate the learning curve of robot-assisted minimally invasive esophagectomy (RAMIE). A retrospective analysis of the first 1...

Robot-Assisted Laparoscopic Distal Ureteroureterostomy for Distal Benign Ureteral Strictures with Long-Term Follow-Up.

To demonstrate feasibility of robot-assisted laparoscopic (RAL) ureteroureterostomy (UU) for benign distal ureteral strictures (DUS) in our robotic r...

[Deep Learning-based Risk Prediction Model for Postoperative Healthcare-associated Infections].

Objective To develop a risk prediction model combining pre/intraoperative risk factors and intraoperative vital signs for postoperative healthcare-ass...

Identification of Ulcers and Erosions by the Novel Pillcam™ Crohn's Capsule Using a Convolutional Neural Network: A Multicentre Pilot Study.

BACKGROUND AND AIMS: Capsule endoscopy is a central element in the management of patients with suspected or known Crohn's disease. In 2017, PillCam™ C...

Convolutional neural network-based artificial intelligence for the diagnosis of early esophageal cancer based on endoscopic images: A meta-analysis.

BACKGROUND: Early screening and treatment of esophageal cancer (EC) is particularly important for the survival and prognosis of patients. However, ear...

Using the da Vinci X® - System for Esophageal Surgery.

Robotic esophageal surgery is becoming more widely adopted. Several publications on the feasibility, short-term outcomes and technical aspects are ava...

Transit Time Flow Measurement in Robotic Totally Endoscopic Coronary Artery Bypass: What Do the Numbers Mean?

OBJECTIVE: Transit time flow measurement (TTFM) is valuable for assessing intraoperative graft patency in coronary artery bypass surgery (CAB). The si...

A Deep Learning Convolutional Neural Network Can Differentiate Between Helicobacter Pylori Gastritis and Autoimmune Gastritis With Results Comparable to Gastrointestinal Pathologists.

CONTEXT.—: Pathology studies using convolutional neural networks (CNNs) have focused on neoplasms, while studies in inflammatory pathology are rare. W...

Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study.

BACKGROUND: Accurate prediction of tumour response to neoadjuvant chemoradiotherapy enables personalised perioperative therapy for locally advanced re...

Percutaneous Endoscopic Robot-Assisted Transforaminal Lumbar Interbody Fusion (PE RA-TLIF) for Lumbar Spondylolisthesis: A Technical Note and Two Years Clinical Results.

BACKGROUND: Minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) and percutaneous endoscopic TLIF (PE-TILF) have been widely used in s...

miRNAs expression pattern and machine learning models elucidate risk for gastric GIST.

BACKGROUND: Gatrointestinal stromal tumors (GISTs) are the main mesenchymal tumors found in the gastrointestinal system. GISTs clinical phenotypes dif...

Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images.

BACKGROUND: Early stage diagnosis of Pancreatic Ductal Adenocarcinoma (PDAC) is challenging due to the lack of specific diagnostic biomarkers. However...

Machine learning analyses of highly-multiplexed immunofluorescence identifies distinct tumor and stromal cell populations in primary pancreatic tumors.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a formidable challenge for patients and clinicians.

Diversified Applications of Self-assembled Nanocluster Delivery Systems- A State-ofthe- art Review.

BACKGROUND: For the nanoparticulate system and the transportation of cellular elements for the fabrication of microelectronic devices, self-assembled ...

[Comparison of robot-assisted and conventional endoscopic surgeries in the Russian Federation. (A systematic review and meta-analysis)].

OBJECTIVE: Comparative assessment of immediate and long-term results of robot-assisted and conventional endoscopic technologies in the Russian Federat...

The Use of Machine Learning in MicroRNA Diagnostics: Current Perspectives.

MicroRNAs constitute small non-coding RNAs that play a pivotal role in regulating the translation and degradation of mRNA and have been associated wit...

Weakly Supervised Learning for Poorly Differentiated Adenocarcinoma Classification in GastricEndoscopic Submucosal Dissection Whole Slide Images.

Endoscopic submucosal dissection (ESD) is the preferred technique for treating early gastric cancers including poorly differentiated adenocarcinoma w...