AIMC Topic: Colonic Polyps

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A comparative study on polyp classification using convolutional neural networks.

PloS one Jul 30, 2020
Colorectal cancer is the third most common cancer diagnosed in both men and women in the United States. Most colorectal cancers start as a growth on the inner lining of the colon or rectum, called 'polyp'. Not all polyps are cancerous, but some can d...
Female Humans Male Neural Networks, Computer Deep Learning Algorithms Colonic Polyps
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Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study.

Gastroenterology Jun 17, 2020
BACKGROUND AND AIMS: Up to 30% of adenomas might be missed during screening colonoscopy-these could be polyps that appear on-screen but are not recognized by endoscopists or polyps that are in locations that do not appear on the screen at all. Comput...
Prospective Studies Female Reproducibility of Results Aged Humans China Adolescent Predictive Value of Tests Colonic Neoplasms Colonic Polyps Image Interpretation, Computer-Assisted Adenomatous Polyps Missed Diagnosis Young Adult Adult Male Middle Aged Colonoscopy Diagnosis, Computer-Assisted Deep Learning
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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...
Deep Learning Image Interpretation, Computer-Assisted Rectal Diseases Capsule Endoscopy Humans Helicobacter pylori Endoscopy, Gastrointestinal Precancerous Conditions Stomach Neoplasms Gastrointestinal Neoplasms Pharyngeal Neoplasms Colorectal Neoplasms Helicobacter Infections Esophageal Neoplasms Colonic Polyps
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Polyp detection algorithm can detect small polyps: Ex vivo reading test compared with endoscopists.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society May 28, 2020
BACKGROUND AND STUDY AIMS: Small polyps are occasionally missed during colonoscopy. This study was conducted to validate the diagnostic performance of a polyp-detection algorithm to alert endoscopists to unrecognized lesions.
Colonic Polyps Adenomatous Polyps Humans Algorithms Colonoscopy Deep Learning
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The impact of deep convolutional neural network-based artificial intelligence on colonoscopy outcomes: A systematic review with meta-analysis.

Journal of gastroenterology and hepatology Apr 26, 2020
BACKGROUND AND AIM: The utility of artificial intelligence (AI) in colonoscopy has gained popularity in current times. Recent trials have evaluated the efficacy of deep convolutional neural network (DCNN)-based AI system in colonoscopy for improving ...
Neural Networks, Computer Artificial Intelligence Colonoscopy Mass Screening Humans Adenoma Colonic Neoplasms Colonic Polyps
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Evaluation of a Deep Neural Network for Automated Classification of Colorectal Polyps on Histopathologic Slides.

JAMA network open Apr 1, 2020
IMPORTANCE: Histologic classification of colorectal polyps plays a critical role in screening for colorectal cancer and care of affected patients. An accurate and automated algorithm for the classification of colorectal polyps on digitized histopatho...
Colonic Polyps Image Interpretation, Computer-Assisted Histocytochemistry Neural Networks, Computer Algorithms Sensitivity and Specificity Deep Learning Humans
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Improved Accuracy in Optical Diagnosis of Colorectal Polyps Using Convolutional Neural Networks with Visual Explanations.

Gastroenterology Feb 29, 2020
BACKGROUND & AIMS: Narrow-band imaging (NBI) can be used to determine whether colorectal polyps are adenomatous or hyperplastic. We investigated whether an artificial intelligence (AI) system can increase the accuracy of characterizations of polyps b...
Humans Reproducibility of Results Colorectal Neoplasms Colonoscopy Diagnosis, Computer-Assisted Deep Learning Visual Perception Seoul Adenomatous Polyps Colonic Polyps Narrow Band Imaging Image Interpretation, Computer-Assisted Clinical Competence Observer Variation Predictive Value of Tests Hyperplasia Workflow
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Polyp fingerprint: automatic recognition of colorectal polyps' unique features.

Surgical endoscopy Feb 11, 2020
BACKGROUND: Content-based image retrieval (CBIR) is an application of machine learning used to retrieve images by similarity on the basis of features. Our objective was to develop a CBIR system that could identify images containing the same polyp ('p...
Image Interpretation, Computer-Assisted Information Storage and Retrieval Humans Machine Learning Colonoscopy Colonic Polyps
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Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study.

The lancet. Gastroenterology & hepatology Jan 22, 2020
BACKGROUND: Colonoscopy with computer-aided detection (CADe) has been shown in non-blinded trials to improve detection of colon polyps and adenomas by providing visual alarms during the procedure. We aimed to assess the effectiveness of a CADe system...
China Incidence Mass Screening Adenoma Case-Control Studies Humans Clinical Alarms Diagnosis, Computer-Assisted Female Colorectal Neoplasms Double-Blind Method Deep Learning Adult Male Colonic Polyps Middle Aged Observer Variation Colonoscopy Early Diagnosis
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Endoscopic diagnosis and treatment planning for colorectal polyps using a deep-learning model.

Scientific reports Jan 8, 2020
We aimed to develop a computer-aided diagnostic system (CAD) for predicting colorectal polyp histology using deep-learning technology and to validate its performance. Near-focus narrow-band imaging (NBI) pictures of colorectal polyps were retrieved f...
Narrow Band Imaging Humans Artificial Intelligence Retrospective Studies Colonoscopy Models, Statistical Diagnosis, Computer-Assisted Adenoma Deep Learning Endoscopy, Gastrointestinal Colonic Polyps
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