AIMC Topic: Gastrointestinal Neoplasms
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Distinction of surgically resected gastrointestinal stromal tumor by near-infrared hyperspectral imaging.
Scientific reports
Dec 14, 2020
The diagnosis of gastrointestinal stromal tumor (GIST) using conventional endoscopy is difficult because submucosal tumor (SMT) lesions like GIST are covered by a mucosal layer. Near-infrared hyperspectral imaging (NIR-HSI) can obtain optical informa...
Current and Potential Applications of Artificial Intelligence in Gastrointestinal Stromal Tumor Imaging.
Contrast media & molecular imaging
Nov 26, 2020
The most common mesenchymal tumors are gastrointestinal stromal tumors (GISTs), which have malignant potential and can occur anywhere along the gastrointestinal system. Imaging methods are important and indispensable of GISTs in diagnosis, risk stagi...
Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers.
Gut
Nov 19, 2020
Artificial intelligence (AI) can extract complex information from visual data. Histopathology images of gastrointestinal (GI) and liver cancer contain a very high amount of information which human observers can only partially make sense of. Complemen...
Efficacy of endoscopic ultrasound with artificial intelligence for the diagnosis of gastrointestinal stromal tumors.
Journal of gastroenterology
Sep 11, 2020
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...
Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning.
Scientific reports
Jul 6, 2020
The Ki-67 index is an established prognostic factor in gastrointestinal neuroendocrine tumors (GI-NETs) and defines tumor grade. It is currently estimated by microscopically examining tumor tissue single-immunostained (SS) for Ki-67 and counting the ...
Utilizing artificial intelligence in endoscopy: a clinician's guide.
Expert review of gastroenterology & hepatology
Jun 17, 2020
INTRODUCTION: Artificial intelligence (AI) that surpasses human ability in image recognition is expected to be applied in the field of gastrointestinal endoscopes. Accordingly, its research and development (R &D) is being actively conducted. With the...
Capsule Endoscopy
Precancerous Conditions
Pharyngeal Neoplasms
Rectal Diseases
Endoscopy, Gastrointestinal
Helicobacter Infections
Helicobacter pylori
Gastrointestinal Neoplasms
Esophageal Neoplasms
Colonic Polyps
Image Interpretation, Computer-Assisted
Humans
Stomach Neoplasms
Colorectal Neoplasms
Deep Learning
Next generation of endoscopy: Harmony with artificial intelligence and robotic-assisted devices.
Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
Apr 9, 2020
Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study.
The Lancet. Oncology
Oct 4, 2019
BACKGROUND: Upper gastrointestinal cancers (including oesophageal cancer and gastric cancer) are the most common cancers worldwide. Artificial intelligence platforms using deep learning algorithms have made remarkable progress in medical imaging but ...
Modelling disease risk for amyloid A (AA) amyloidosis in non-human primates using machine learning.
Amyloid : the international journal of experimental and clinical investigation : the official journal of the International Society of Amyloidosis
Jun 18, 2019
Amyloid A (AA) amyloidosis is found in humans and non-human primates, but quantifying disease risk prior to clinical symptoms is challenging. We applied machine learning to identify the best predictors of amyloidosis in rhesus macaques from availabl...
Amyloidosis
Triglycerides
Arthritis
Male
Macaca mulatta
Wounds and Injuries
Gastrointestinal Neoplasms
Retrospective Studies
Case-Control Studies
Colitis
Adenocarcinoma
Endometriosis
Humans
Female
Diarrhea
Serum Amyloid A Protein
Disease Models, Animal
Risk Factors
Machine Learning
Cholesterol, HDL
Models, Statistical
Animals
Biomarkers
Cholesterol, LDL
Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer.
Nature medicine
Jun 3, 2019
Microsatellite instability determines whether patients with gastrointestinal cancer respond exceptionally well to immunotherapy. However, in clinical practice, not every patient is tested for MSI, because this requires additional genetic or immunohis...
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