Related references
Note: Only part of the references are listed.Artificial intelligence for polyp detection during colonoscopy: a systematic review and meta-analysis
Ishita Barua et al.
ENDOSCOPY (2021)
Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis
Cesare Hassan et al.
GASTROINTESTINAL ENDOSCOPY (2021)
Development of a computer-aided detection system for colonoscopy and a publicly accessible large colonoscopy video database (with video)
Masashi Misawa et al.
GASTROINTESTINAL ENDOSCOPY (2021)
Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study
Yuchen Luo et al.
JOURNAL OF GASTROINTESTINAL SURGERY (2021)
Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
Hyuna Sung et al.
CA-A CANCER JOURNAL FOR CLINICIANS (2021)
Artificial Intelligence-Aided Colonoscopy for Polyp Detection: A Systematic Review and Meta-Analysis of Randomized Clinical Trials
Yuanchuan Zhang et al.
JOURNAL OF LAPAROENDOSCOPIC & ADVANCED SURGICAL TECHNIQUES (2021)
Artificial intelligence (AI) real-time detection vs. routine colonoscopy for colorectal neoplasia: a meta-analysis and trial sequential analysis
Smit S. Deliwala et al.
INTERNATIONAL JOURNAL OF COLORECTAL DISEASE (2021)
PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews
Matthew J. Page et al.
BMJ-BRITISH MEDICAL JOURNAL (2021)
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
Matthew J. Page et al.
BMJ-BRITISH MEDICAL JOURNAL (2021)
Impact of real-time use of artificial intelligence in improving adenoma detection during colonoscopy: A systematic review and meta-analysis
Munish Ashat et al.
ENDOSCOPY INTERNATIONAL OPEN (2021)
Impact of a real-time automatic quality control system on colorectal polyp and adenoma detection: a prospective randomized controlled study
Jing-Ran Su et al.
GASTROINTESTINAL ENDOSCOPY (2020)
Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study
Dexin Gong et al.
LANCET GASTROENTEROLOGY & HEPATOLOGY (2020)
Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study
Pu Wang et al.
LANCET GASTROENTEROLOGY & HEPATOLOGY (2020)
Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial
Alessandro Repici et al.
GASTROENTEROLOGY (2020)
The impact of deep convolutional neural network-based artificial intelligence on colonoscopy outcomes: A systematic review with meta-analysis
Muhammad Aziz et al.
JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY (2020)
Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study
Pu Wang et al.
GASTROENTEROLOGY (2020)
Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies
Peng Xue et al.
BMC MEDICINE (2020)
Real-time computer aided colonoscopy versus standard colonoscopy for improving adenoma detection rate: A meta-analysis of randomized-controlled trials
Babu P. Mohan et al.
ECLINICALMEDICINE (2020)
Endocytoscopy with NBI has the potential to correctly diagnose diminutive colorectal polyps that are difficult to diagnose using conventional NBI
Shinichi Kataoka et al.
ENDOSCOPY INTERNATIONAL OPEN (2020)
Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy
Wen-Na Liu et al.
SAUDI JOURNAL OF GASTROENTEROLOGY (2020)
Magnitude, Risk Factors, and Factors Associated With Adenoma Miss Rate of Tandem Colonoscopy: A Systematic Review and Meta-analysis
Shengbing Zhao et al.
GASTROENTEROLOGY (2019)
Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study
Pu Wang et al.
GUT (2019)
Automated polyp segmentation for colonoscopy images: A method based on convolutional neural networks and ensemble learning
Xudong Guo et al.
MEDICAL PHYSICS (2019)
Compared Abilities of Endoscopic Techniques to Increase Colon Adenoma Detection Rates: A Network Meta-analysis
Antonio Facciorusso et al.
CLINICAL GASTROENTEROLOGY AND HEPATOLOGY (2019)
Effectiveness of screening colonoscopy in reducing the risk of death from right and left colon cancer: a large community-based study
Chyke A. Doubeni et al.
GUT (2018)
Comparative Efficacy of Colonoscope Distal Attachment Devices in Increasing Rates of Adenoma Detection: A Network Meta-analysis
Antonio Facciorusso et al.
CLINICAL GASTROENTEROLOGY AND HEPATOLOGY (2018)
Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience
Masashi Misawa et al.
GASTROENTEROLOGY (2018)
Deep learning and conditional random fields-based depth estimation and topographical reconstruction from conventional endoscopy
Faisal Mahmood et al.
MEDICAL IMAGE ANALYSIS (2018)
Will Computer-Aided Detection and Diagnosis Revolutionize Colonoscopy?
Michael F. Byrne et al.
GASTROENTEROLOGY (2017)
Colorectal Cancer Screening: Recommendations for Physicians and Patients from the US Multi-Society Task Force on Colorectal Cancer
Douglas K. Rex et al.
AMERICAN JOURNAL OF GASTROENTEROLOGY (2017)
Factors associated with colorectal cancer occurrence after colonoscopy that did not diagnose colorectal cancer
Danny Cheung et al.
GASTROINTESTINAL ENDOSCOPY (2016)
Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information
Nima Tajbakhsh et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2016)
Screening for Colorectal Cancer US Preventive Services Task Force Recommendation Statement
Kirsten Bibbins-Domingo et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2016)
Adenoma Detection Rate and Risk of Colorectal Cancer and Death
Douglas A. Corley et al.
NEW ENGLAND JOURNAL OF MEDICINE (2014)
Nurse Observation During Colonoscopy Increases Polyp Detection: A Randomized Prospective Study
Harry R. Aslanian et al.
AMERICAN JOURNAL OF GASTROENTEROLOGY (2013)
Development of AMSTAR: a measurement tool to assess the methodological quality of systematic reviews
Beverley J. Shea et al.
BMC MEDICAL RESEARCH METHODOLOGY (2007)