AIMC Topic: Gastroscopy

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Artificial intelligence in endoscopy: the guardian angel is around the corner.

Gastrointestinal endoscopy Jan 22, 2020
Artificial Intelligence Prospective Studies Single-Blind Method Gastroscopy
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Convolutional Neural Network for Differentiating Gastric Cancer from Gastritis Using Magnified Endoscopy with Narrow Band Imaging.

Digestive diseases and sciences Oct 4, 2019
BACKGROUND: Early detection of early gastric cancer (EGC) allows for less invasive cancer treatment. However, differentiating EGC from gastritis remains challenging. Although magnifying endoscopy with narrow band imaging (ME-NBI) is useful for differ...
Male Early Detection of Cancer Diagnosis, Differential Gastritis Female Humans False Positive Reactions Stomach Neoplasms Neural Networks, Computer Gastroscopy Sensitivity and Specificity Retrospective Studies Narrow Band Imaging Radiographic Magnification
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Comparing blind spots of unsedated ultrafine, sedated, and unsedated conventional gastroscopy with and without artificial intelligence: a prospective, single-blind, 3-parallel-group, randomized, single-center trial.

Gastrointestinal endoscopy Sep 18, 2019
BACKGROUND AND AIMS: EGD is the most vital procedure for the diagnosis of upper GI lesions. We aimed to compare the performance of unsedated ultrathin transoral endoscopy (U-TOE), unsedated conventional EGD (C-EGD), and sedated C-EGD with or without ...
Artificial Intelligence Prospective Studies Female Aged Humans Pain, Procedural Endoscopy, Digestive System Conscious Sedation Gastroscopes Anxiety Image Processing, Computer-Assisted Single-Blind Method Gastroscopy Adult Male Middle Aged
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Deep-learning based detection of gastric precancerous conditions.

Gut Aug 2, 2019
Deep Learning Chronic Disease Image Interpretation, Computer-Assisted Precancerous Conditions Stomach Neoplasms Male Middle Aged Gastroscopy Gastritis, Atrophic Humans Algorithms Female Aged Aged, 80 and over Adult
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[French comment on article A deep neural network improves endoscopic detection of early gastric cancer without blind spots].

Endoscopy May 28, 2019
Gastroscopy Humans Neural Networks, Computer Stomach Neoplasms Diagnosis, Computer-Assisted
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Real-time gastric polyp detection using convolutional neural networks.

PloS one Mar 25, 2019
Computer-aided polyp detection in gastric gastroscopy has been the subject of research over the past few decades. However, despite significant advances, automatic polyp detection in real time is still an unsolved problem. In this paper, we report on ...
Gastroscopy Adenomatous Polyps Humans Neural Networks, Computer Female Stomach Neoplasms Male Image Processing, Computer-Assisted
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A deep neural network improves endoscopic detection of early gastric cancer without blind spots.

Endoscopy Mar 12, 2019
BACKGROUND: Gastric cancer is the third most lethal malignancy worldwide. A novel deep convolution neural network (DCNN) to perform visual tasks has been recently developed. The aim of this study was to build a system using the DCNN to detect early g...
Diagnosis, Differential Humans Observer Variation Neural Networks, Computer Gastroscopy Stomach Neoplasms Sensitivity and Specificity Clinical Competence Early Detection of Cancer
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Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy.

Gastrointestinal endoscopy Nov 16, 2018
BACKGROUND AND AIMS: According to guidelines, endoscopic resection should only be performed for patients whose early gastric cancer invasion depth is within the mucosa or submucosa of the stomach regardless of lymph node involvement. The accurate pre...
Image Processing, Computer-Assisted Humans Neoplasm Invasiveness Neural Networks, Computer Carcinoma Gastrectomy Sensitivity and Specificity Serous Membrane Artificial Intelligence Gastroscopy Male Female Gastric Mucosa Diagnosis, Computer-Assisted Stomach Neoplasms Endoscopic Mucosal Resection ROC Curve
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Detecting and Locating Gastrointestinal Anomalies Using Deep Learning and Iterative Cluster Unification.

IEEE transactions on medical imaging May 15, 2018
This paper proposes a novel methodology for automatic detection and localization of gastrointestinal (GI) anomalies in endoscopic video frame sequences. Training is performed with weakly annotated images, using only image-level, semantic labels inste...
Humans Algorithms Databases, Factual Deep Learning Video Recording Image Interpretation, Computer-Assisted Gastrointestinal Tract Gastrointestinal Diseases Gastroscopy
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Automated classification of celiac disease during upper endoscopy: Status quo and quo vadis.

Computers in biology and medicine Apr 27, 2018
A large amount of digital image material is routinely captured during esophagogastroduodenoscopies but, for the most part, is not used for confirming the diagnosis process of celiac disease which is primarily based on histological examination of biop...
Image Processing, Computer-Assisted Pattern Recognition, Automated Duodenum Decision Making Gastroscopy Algorithms Biopsy Humans Celiac Disease Image Interpretation, Computer-Assisted Endoscopy Neural Networks, Computer Observer Variation Diagnosis, Computer-Assisted Machine Learning Automation Deep Learning
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