4.5 Article

Adenoma detection rate is not influenced by the time of day in computer-aided detection colonoscopy

Journal

MEDICINE
Volume 99, Issue 51, Pages -

Publisher

LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/MD.0000000000023685

Keywords

adenoma detection rate; afternoon; colonoscopy; computer-aided detection; morning

Ask authors/readers for more resources

Because of endoscopist fatigue, the time of colonoscopy have been shown to influence adenoma detection rate (ADR). Computer-aided detection (CADe) provides simultaneous visual alerts on polyps during colonoscopy and thus to increase adenoma detection rate. This is attributable to the strengthening of endoscopists diagnostic level and alleviation of fatigue. The aim of the study was to investigate whether CADe colonoscopy could eliminate the influence of the afternoon fatigue on ADR. We retrospectively analyzed the recorded data of patients who were performed CADe colonoscopy from September 2017 to February 2019 in Endoscopy Center of Sichuan Provincial People's Hospital. Patients demographic as well as baseline data recorded during colonoscopy were used for the analysis. Morning colonoscopy was defined as colonoscopic procedures starting between 8:00 am and 12:00 noon. Afternoon colonoscopy was defined as procedures starting at 2:00 pm and thereafter. The primary outcome was ADR. Univariate analysis and multivariate regression analysis were also performed. A total of 484 CADe colonoscopies were performed by 4 endoscopists in the study. The overall polyp detection rate was 52% and overall ADR was 35.5%. The mean number of adenomas detected per colonoscopy (0.62 vs 0.61, P > .05) and ADR (0.36 vs 0.35, P > .05) were similar in the am and pm group. Multivariable analysis shows that the ADR of CADe colonoscopy was influenced by the age (P < .001), gender (P = .004) and withdrawal time (P < .001), no correlation was found regarding bowel preparation (P = .993) and endoscopist experience (P = .804). CADe colonoscopy could eliminate the influence of the afternoon fatigue on ADR. The ADR during CADe colonoscopy is significantly affected by age, gender and withdrawal time.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.5
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

Article Gastroenterology & Hepatology

Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study

Pu Wang, Tyler M. Berzin, Jeremy Romek Glissen Brown, Shishira Bharadwaj, Aymeric Becq, Xun Xiao, Peixi Liu, Liangping Li, Yan Song, Di Zhang, Yi Li, Guangre Xu, Mengtian Tu, Xiaogang Liu

Article Medicine, General & Internal

Factors predicting the colorectal adenoma detection rate in colonoscopic screening of a Chinese population A prospective study

Han Wang, Pu Wang, Xiaogang Liu, Liangping Li, Xun Xiao, Peixi Liu, Di Zhang, Yi Li, Guangre Xu, Mengtian Tu, Yan Song

MEDICINE (2019)

Article Gastroenterology & Hepatology

Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study

Pu Wang, Xiaogang Liu, Tyler M. Berzin, Jeremy R. Glissen Brown, Peixi Liu, Chao Zhou, Lei Lei, Liangping Li, Zhenzhen Guo, Shan Lei, Fei Xiong, Han Wang, Yan Song, Yan Pan, Guanyu Zhou

LANCET GASTROENTEROLOGY & HEPATOLOGY (2020)

Article Multidisciplinary Sciences

Computer aided detection for laterally spreading tumors and sessile serrated adenomas during colonoscopy

Guanyu Zhou, Xun Xiao, Mengtian Tu, Peixi Liu, Dan Yang, Xiaogang Liu, Renyi Zhang, Liangping Li, Shan Lei, Han Wang, Yan Song, Pu Wang

PLOS ONE (2020)

Editorial Material Gastroenterology & Hepatology

A case report of drug-induced ischemic colitis accompanied by ulcerative colitis

Guanyu Zhou, Fei Xiong, Xudan Yang, Pu Wang

GASTROENTEROLOGIA Y HEPATOLOGIA (2021)

Article Gastroenterology & Hepatology

Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study

Pu Wang, Peixi Liu, Jeremy R. Glissen Brown, Tyler M. Berzin, Guanyu Zhou, Shan Lei, Xiaogang Liu, Liangping Li, Xun Xiao

GASTROENTEROLOGY (2020)

Letter Gastroenterology & Hepatology

Use of Artificial Intelligence in Endoscopic Training: Is Deskilling a Real Fear? Reply

Pu Wang, Tyler M. Berzin, Jeremy R. Glissen Brown

GASTROENTEROLOGY (2021)

Article Gastroenterology & Hepatology

Deep Learning Computer-aided Polyp Detection Reduces Adenoma Miss Rate: A United States Multi-center Randomized Tandem Colonoscopy Study (CADeT-CS Trial)

Jeremy R. Glissen Brown, Nabil M. Mansour, Pu Wang, Maria Aguilera Chuchuca, Scott B. Minchenberg, Madhuri Chandnani, Lin Liu, Seth A. Gross, Neil Sengupta, Tyler M. Berzin

Summary: In a U.S. multicenter tandem colonoscopy randomized controlled trial, the use of a CADe-system was shown to decrease AMR and SSL miss rate while increasing first-pass APC compared to HDWL colonoscopy alone.

CLINICAL GASTROENTEROLOGY AND HEPATOLOGY (2022)

Article Gastroenterology & Hepatology

Impact of Artificial Intelligence on Colonoscopy Surveillance After Polyp Removal: A Pooled Analysis of Randomized Trials

Yuichi Mori, Pu Wang, Magnus Loberg, Masashi Misawa, Alessandro Repici, Marco Spadaccini, Loredana Correale, Giulio Antonelli, Honggang Yu, Dexin Gong, Misaki Ishiyama, Shin-ei Kudo, Shunsuke Kamba, Kazuki Sumiyama, Yutaka Saito, Haruo Nishino, Peixi Liu, Jeremy R. Glissen Brown, Nabil M. Mansour, Seth A. Gross, Mette Kalager, Michael Bretthauer, Douglas K. Rex, Prateek Sharma, Tyler M. Berzin, Cesare Hassan

Summary: The use of AI during colonoscopy increases the proportion of patients needing intensive surveillance by approximately 35% in the United States and 20% in Europe. This contributes to improved cancer prevention but adds significant patient burden and healthcare costs.

CLINICAL GASTROENTEROLOGY AND HEPATOLOGY (2023)

Article Gastroenterology & Hepatology

Artificial intelligence empowers the second-observer strategy for colonoscopy: a randomized clinical trial

Pu Wang, Xiao-Gang Liu, Min Kang, Xue Peng, Mei-Ling Shu, Guan-Yu Zhou, Pei-Xi Liu, Fei Xiong, Ming-Ming Deng, Hong-Fen Xia, Jian-Jun Li, Xiao-Qi Long, Yan Song, Liang-Ping Li

Summary: In colonoscopy screening for colorectal cancer, human vision limitations may lead to higher miss rate of lesions. Artificial intelligence (AI) assistance has been demonstrated to improve polyp detection. This study aimed to compare the effectiveness of AI and human observer during colonoscopy.

GASTROENTEROLOGY REPORT (2023)

Article Gastroenterology & Hepatology

Initial experience of visualized biliary cannulation during ERCP

Wei-hui Liu, Xin-yu Huang, Xiao Hu, Pu Wang, Yun-chao Yang, Pei-xi Liu, Xiao-gang Liu

ENDOSCOPY (2023)

Meeting Abstract Gastroenterology & Hepatology

IMPACT OF ARTIFICIAL INTELLIGENCE ON COLONOSCOPY SURVEILLANCE AFTER POLYP REMOVAL: A POOLED ANALYSIS OF RANDOMIZED TRIALS

Yuichi Mori, Pu Wang, Magnus Loberg, Masashi Misawa, Alessandro Repici, Marco Spadaccini, Loredana Correale, Giulio Antonelli, Honggang Yu, Dexin Gong, Misaki Ishiyama, Shinei Kudo, Shunsuke Kamba, Kazuki Sumiyama, Yutaka Saito, Haruo Nishino, Peixi Liu, Jeremy R. Glissen Brown, Nabil M. Mansour, Seth Gross, Mette Kalager, Michael Bretthauer, Douglas K. Rex, Prateek Sharma, Tyler M. Berzin, Cesare Hassan

GASTROINTESTINAL ENDOSCOPY (2022)

Article Gastroenterology & Hepatology

Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method

Omer F. Ahmad, Yuichi Mori, Masashi Misawa, Shin-ei Kudo, John T. Anderson, Jorge Bernal, Tyler M. Berzin, Raf Bisschops, Michael F. Byrne, Peng-Jen Chen, James E. East, Tom Eelbode, Daniel S. Elson, Suryakanth R. Gurudu, Aymeric Histace, William E. Karnes, Alessandro Repici, Rajvinder Singh, Pietro Valdastri, Michael B. Wallace, Pu Wang, Danail Stoyanov, Laurence B. Lovat

Summary: This study identified key implementation research priorities for artificial intelligence in colonoscopy through an international expert panel, focusing on clinical trial design/end points, technological developments, clinical adoption/integration, data access/annotation, and regulatory approval. These findings provide a framework for future research to accelerate the clinical implementation of AI in endoscopy.

ENDOSCOPY (2021)

Article Gastroenterology & Hepatology

The single-monitor trial: an embedded CADe system increased adenoma detection during colonoscopy: a prospective randomized study

Peixi Liu, Pu Wang, Jeremy R. Glissen Brown, Tyler M. Berzin, Guanyu Zhou, Weihui Liu, Xun Xiao, Ziyang Chen, Zhihong Zhang, Chao Zhou, Lei Lei, Fei Xiong, Liangping Li, Xiaogang Liu

THERAPEUTIC ADVANCES IN GASTROENTEROLOGY (2020)

No Data Available