The Impacts of Computer-Aided Detection of Colorectal Polyps on Subsequent Colonoscopy Surveillance Intervals: Simulation Study

被引:2
作者
Lui, Ka Luen Thomas [1 ]
Liu, Sze Hang Kevin [1 ]
Leung, Kathy [2 ]
Wu, Joseph T. [2 ]
Zauber, Ann G. [3 ]
Leung, Wai Keung [1 ]
机构
[1] Univ Hong Kong, Li Ka Shing Fac Med, Sch Clin Med, Dept Med, 102 Pokfulam Rd, Hong Kong, Peoples R China
[2] Univ Hong Kong, Li Ka Shing Fac Med, WHO Collaborating Ctr Infect Dis Epidemiol & Contr, Sch Publ Hlth, Hong Kong, Peoples R China
[3] Mem Sloan Kettering Canc Ctr, Dept Epidemiol & Biostat, New York, NY USA
关键词
artificial intelligence; surveillance colonoscopy; colonic polyp; polyp; colonoscopy; computer-aided; detect; adenoma; endoscopic; endoscopy; simulation; simulated; surveillance; INTELLIGENCE-ASSISTED COLONOSCOPY; ARTIFICIAL-INTELLIGENCE; TIME; CANCERS;
D O I
10.2196/42665
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Background: Computer-aided detection (CADe) of colorectal polyps has been shown to increase adenoma detection rates, which would potentially shorten subsequent surveillance intervals. Objective: The purpose of this study is to simulate the potential changes in subsequent colonoscopy surveillance intervals after the application of CADe in a large cohort of patients. Methods: We simulated the projected increase in polyp and adenoma detection by universal CADe application in our patients who had undergone colonoscopy with complete endoscopic and histological findings between 2016 and 2020. The simulation was based on bootstrapping the published performance of CADe. The corresponding changes in surveillance intervals for each patient, as recommended by the US Multi-Society Task Force on Colorectal Cancer (USMSTF) or the European Society of Gastrointestinal Endoscopy (ESGE), were determined after the CADe was determined. Results: A total of 3735 patients who had undergone colonoscopy were included. Based on the simulated CADe effect, the application of CADe would result in 19.1% (n=714) and 1.9% (n=71) of patients having shorter surveillance intervals, according to the USMSTF and ESGE guidelines, respectively. In particular, all (or 2.7% (n=101) of the total) patients who were originally scheduled to have 3-5 years of surveillance would have their surveillance intervals shortened to 3 years, following the USMSTF guidelines. The changes in this group of patients were largely attributed to an increase in the number of adenomas (n=75, 74%) rather than serrated lesions being detected. Conclusions: Widespread adoption of CADe would inevitably increase the demand for surveillance colonoscopies with the shortening of original surveillance intervals, particularly following the current USMSTF guideline.
引用
收藏
页数:12
相关论文
共 58 条
[1]   Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method [J].
Ahmad, Omer F. ;
Mori, Yuichi ;
Misawa, Masashi ;
Kudo, Shin-ei ;
Anderson, John T. ;
Bernal, Jorge ;
Berzin, Tyler M. ;
Bisschops, Raf ;
Byrne, Michael F. ;
Chen, Peng-Jen ;
East, James E. ;
Eelbode, Tom ;
Elson, Daniel S. ;
Gurudu, Suryakanth R. ;
Histace, Aymeric ;
Karnes, William E. ;
Repici, Alessandro ;
Singh, Rajvinder ;
Valdastri, Pietro ;
Wallace, Michael B. ;
Wang, Pu ;
Stoyanov, Danail ;
Lovat, Laurence B. .
ENDOSCOPY, 2021, 53 (09) :893-901
[2]   High-quality Studies of Artificial Intelligence in Colonoscopy Illuminate a Next Important Step [J].
Ahuja, Amisha ;
Mori, Yuichi .
GASTROENTEROLOGY, 2022, 163 (03) :582-583
[3]   Artificial intelligence-aided colonoscopy: Recent developments and future perspectives [J].
Antonelli, Giulio ;
Gkolfakis, Paraskevas ;
Tziatzios, Georgios ;
Papanikolaou, Ioannis S. ;
Triantafyllou, Konstantinos ;
Hassan, Cesare .
WORLD JOURNAL OF GASTROENTEROLOGY, 2020, 26 (47) :7436-7443
[4]   Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study [J].
Areia, Miguel ;
Mori, Yuichi ;
Correale, Loredana ;
Repici, Alessandro ;
Bretthauer, Michael ;
Sharma, Prateek ;
Taveira, Filipe ;
Spadaccini, Marco ;
Antonelli, Giulio ;
Ebigbo, Alanna ;
Kudo, Shin-Ei ;
Arribas, Julia ;
Barua, Ishita ;
Kaminski, Michal F. ;
Messmann, Helmut ;
Rex, Douglas K. ;
Dinis-Ribeiro, Mario ;
Hassan, Cesare .
LANCET DIGITAL HEALTH, 2022, 4 (06) :E436-E444
[5]   The impact of deep convolutional neural network-based artificial intelligence on colonoscopy outcomes: A systematic review with meta-analysis [J].
Aziz, Muhammad ;
Fatima, Rawish ;
Dong, Charles ;
Lee-Smith, Wade ;
Nawras, Ali .
JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY, 2020, 35 (10) :1676-1683
[6]   Artificial intelligence for polyp detection during colonoscopy: a systematic review and meta-analysis [J].
Barua, Ishita ;
Vinsard, Daniela Guerrero ;
Jodal, Henriette C. ;
Loberg, Magnus ;
Kalager, Mette ;
Holme, Oyvind ;
Misawa, Masashi ;
Bretthauer, Michael ;
Mori, Yuichi .
ENDOSCOPY, 2021, 53 (03) :277-284
[7]   Using Computer-Aided Polyp Detection During Colonoscopy [J].
Bilal, Mohammad ;
Glissen Brown, Jeremy R. ;
Berzin, Tyler M. .
AMERICAN JOURNAL OF GASTROENTEROLOGY, 2020, 115 (07) :963-966
[8]   Deep Learning Computer-aided Polyp Detection Reduces Adenoma Miss Rate: A United States Multi-center Randomized Tandem Colonoscopy Study (CADeT-CS Trial) [J].
Brown, Jeremy R. Glissen ;
Mansour, Nabil M. ;
Wang, Pu ;
Chuchuca, Maria Aguilera ;
Minchenberg, Scott B. ;
Chandnani, Madhuri ;
Liu, Lin ;
Gross, Seth A. ;
Sengupta, Neil ;
Berzin, Tyler M. .
CLINICAL GASTROENTEROLOGY AND HEPATOLOGY, 2022, 20 (07) :1499-+
[9]   Defining the next steps for artificial intelligence in colonoscopy [J].
Burgess, Nicholas G. .
ENDOSCOPY, 2021, 53 (09) :902-904
[10]   Computer-aided detection of diagnostic and therapeutic operations in colonoscopy videos [J].
Cao, Yu ;
Liu, Danyu ;
Tavanapong, Wallapak ;
Wong, Johnny ;
Oh, JungHwan ;
de Groen, Piet C. .
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2007, 54 (07) :1268-1279