AIMC Topic: Colonic Polyps
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Optical classification of neoplastic colorectal polyps - a computer-assisted approach (the COACH study).
Scandinavian journal of gastroenterology
Sep 29, 2018
BACKGROUND AND AIMS: Clinical data suggest that the quality of optical diagnoses of colorectal polyps differs markedly among endoscopists. The aim of this study was to develop a computer program that was able to differentiate neoplastic from non-neop...
Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy: A Prospective Study.
Annals of internal medicine
Aug 14, 2018
BACKGROUND: Computer-aided diagnosis (CAD) for colonoscopy may help endoscopists distinguish neoplastic polyps (adenomas) requiring resection from nonneoplastic polyps not requiring resection, potentially reducing cost.
Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy.
Gastroenterology
Jun 18, 2018
BACKGROUND & AIMS: The benefit of colonoscopy for colorectal cancer prevention depends on the adenoma detection rate (ADR). The ADR should reflect the adenoma prevalence rate, which is estimated to be higher than 50% in the screening-age population. ...
Observer Variation
Adenomatous Polyps
Prognosis
Reproducibility of Results
Colorectal Neoplasms
Colonoscopy
Humans
Neural Networks, Computer
Machine Learning
Area Under Curve
Feasibility Studies
Colonic Polyps
Video Recording
Image Interpretation, Computer-Assisted
Diagnosis, Computer-Assisted
ROC Curve
Early Detection of Cancer
Predictive Value of Tests
Plasminogen activator inhibitor-1 is associated with the metabolism and development of advanced colonic polyps.
Translational research : the journal of laboratory and clinical medicine
Jun 6, 2018
Implications of plasminogen activator inhibitor-1 (PAI-1) in colonic polyps remain elusive. A prospective study was conducted with 188 consecutive subjects who underwent colonoscopy at a tertiary referral center. Biochemical parameters, serum PAI-1 l...
Natural Language Processing Accurately Calculates Adenoma and Sessile Serrated Polyp Detection Rates.
Digestive diseases and sciences
Apr 26, 2018
BACKGROUND: ADR is a widely used colonoscopy quality indicator. Calculation of ADR is labor-intensive and cumbersome using current electronic medical databases. Natural language processing (NLP) is a method used to extract meaning from unstructured o...
Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience.
Gastroenterology
Apr 11, 2018
Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience.
Oncology
Dec 20, 2017
BACKGROUND AND AIM: Computer-aided diagnosis (CAD) is becoming a next-generation tool for the diagnosis of human disease. CAD for colon polyps has been suggested as a particularly useful tool for trainee colonoscopists, as the use of a CAD system avo...
Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model.
Gut
Oct 24, 2017
BACKGROUND: In general, academic but not community endoscopists have demonstrated adequate endoscopic differentiation accuracy to make the 'resect and discard' paradigm for diminutive colorectal polyps workable. Computer analysis of video could poten...
A hierarchical classifier based on human blood plasma fluorescence for non-invasive colorectal cancer screening.
Artificial intelligence in medicine
Sep 20, 2017
Colorectal cancer (CRC) a leading cause of death by cancer, and screening programs for its early identification are at the heart of the increasing survival rates. To motivate population participation, non-invasive, accurate, scalable and cost-effecti...
Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos.
IEEE journal of biomedical and health informatics
Dec 7, 2016
Automated polyp detection in colonoscopy videos has been demonstrated to be a promising way for colorectal cancer prevention and diagnosis. Traditional manual screening is time consuming, operator dependent, and error prone; hence, automated detectio...
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