AIMC Topic: Gastrointestinal Neoplasms
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Analysis of ultrasonographic images using a deep learning-based model as ancillary diagnostic tool for diagnosing gallbladder polyps.
Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
Jul 3, 2023
BACKGROUND: Accurately diagnosing gallbladder polyps (GBPs) is important to avoid misdiagnosis and overtreatment.
Salivary and serum levels of soluble E-cadherin in patients with gastrointestinal cancers: A comparative study.
Journal of cancer research and therapeutics
Apr 27, 2023
AIM: According to the literature, high levels of salivary soluble E-cadherin may be lined to advanced stage and poor prognosis in cancers. This research aimed at comparing salivary and serum levels of soluble E-cadherin in cases with esophageal, gast...
Robotic Transanal Minimally Invasive Surgery (rTAMIS): Large Tubulovillous Adenoma.
The American surgeon
Feb 28, 2023
Many transanal platforms have recently evolved to manage rectal pathologies. Transanal endoscopic microsurgery (TEM) and transanal laparoscopic minimally invasive surgery (TAMIS) have been developed to address the limitations of conventional transana...
Pure robotic major hepatectomy with biliary reconstruction for hepatobiliary malignancies: first European results.
Surgical endoscopy
Feb 9, 2023
BACKGROUND: Combined liver and bile duct resection with biliary reconstruction for hepatobiliary malignancies are defined as highly complex surgical procedures. The robotic platform may overcome some major limitations of conventional laparoscopic sur...
Machine learning applications in upper gastrointestinal cancer surgery: a systematic review.
Surgical endoscopy
Aug 11, 2022
BACKGROUND: Machine learning (ML) has seen an increase in application, and is an important element of a digital evolution. The role of ML within upper gastrointestinal surgery for malignancies has not been evaluated properly in the literature. Theref...
Artificial Intelligence and Deep Learning for Upper Gastrointestinal Neoplasia.
Gastroenterology
Dec 11, 2021
Upper gastrointestinal (GI) neoplasia account for 35% of GI cancers and 1.5 million cancer-related deaths every year. Despite its efficacy in preventing cancer mortality, diagnostic upper GI endoscopy is affected by a substantial miss rate of neoplas...
Utilization of Ultrasonic Image Characteristics Combined with Endoscopic Detection on the Basis of Artificial Intelligence Algorithm in Diagnosis of Early Upper Gastrointestinal Cancer.
Journal of healthcare engineering
Nov 29, 2021
The aim of this study was to evaluate the diagnostic value of artificial intelligence algorithm combined with ultrasound endoscopy in early esophageal cancer and precancerous lesions by comparing the examination of conventional endoscopy and artifici...
Gastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review.
European journal of cancer (Oxford, England : 1990)
Aug 11, 2021
BACKGROUND: Gastrointestinal cancers account for approximately 20% of all cancer diagnoses and are responsible for 22.5% of cancer deaths worldwide. Artificial intelligence-based diagnostic support systems, in particular convolutional neural network ...
A Machine Learning Model Approach to Risk-Stratify Patients With Gastrointestinal Cancer for Hospitalization and Mortality Outcomes.
International journal of radiation oncology, biology, physics
Apr 29, 2021
PURPOSE: Patients with gastrointestinal (GI) cancer frequently experience unplanned hospitalizations, but predictive tools to identify high-risk patients are lacking. We developed a machine learning model to identify high-risk patients.
Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment.
European journal of nuclear medicine and molecular imaging
Dec 16, 2020
PURPOSE: Advanced medical image analytics is increasingly used to predict clinical outcome in patients diagnosed with gastrointestinal tumors. This review provides an overview on the value of radiomics in predicting response to treatment in patients ...
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