Artificial intelligence-based assessments of colonoscopic withdrawal technique: a new method for measuring and enhancing the quality of fold examination

Wei Liu; Yu Wu; Xianglei Yuan; Jingyu Zhang; Yao Zhou; Wanhong Zhang; Peipei Zhu; Zhang Tao; Long He; Bing Hu; Zhang Yi

doi:10.1055/a-1799-8297

Endoscopy, Inhaltsverzeichnis

CC BY-NC-ND 4.0 · Endoscopy 2022; 54(10): 972-979
DOI: 10.1055/a-1799-8297

Original article

Artificial intelligence-based assessments of colonoscopic withdrawal technique: a new method for measuring and enhancing the quality of fold examination

Wei Liu∗^*

¹Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China

,

Yu Wu∗^*

²Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, China

,

Xianglei Yuan

¹Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China

,

Jingyu Zhang

³State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, Sichuan, China

,

Yao Zhou

²Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, China

,

Wanhong Zhang

⁴Department of Gastroenterology, Cangxi Peopleʼs Hospital, Guangyuan, Sichuan, China

,

Peipei Zhu

⁵Department of Gastroenterology, Dazhou Integrated Traditional Chinese and Western Medicine Hosptial, Dazhou, Sichuan, China

,

Zhang Tao

⁶Department of Gastroenterology, Nanchong Central Hospital, Nanchong, Sichuan, China

,

Long He

¹Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China

,

Bing Hu

¹Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China

,

Zhang Yi

²Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, China

› Institutsangaben

Abstract

Background This study aimed to develop an artificial intelligence (AI)-based system for measuring fold examination quality (FEQ) of colonoscopic withdrawal technique. We also examined the relationship between the system’s evaluation of FEQ and FEQ scores from experts, and adenoma detection rate (ADR) and withdrawal time of colonoscopists, and evaluated the system’s ability to improve FEQ during colonoscopy.

Methods First, we developed an AI-based system for measuring FEQ. Next, 103 consecutive colonoscopies performed by 11 colonoscopists were collected for evaluation. Three experts graded FEQ of each colonoscopy, after which the recorded colonoscopies were evaluated by the system. We further assessed the system by correlating its evaluation of FEQ against expert scoring, historical ADR, and withdrawal time of each colonoscopist. We also conducted a prospective observational study to evaluate the systemʼs performance in enhancing fold examination.

Results The system’s evaluations of FEQ of each endoscopist were significantly correlated with expertsʼ scores (r = 0.871, P < 0.001), historical ADR (r = 0.852, P = 0.001), and withdrawal time (r = 0.727, P = 0.01). For colonoscopies performed by colonoscopists with previously low ADRs (< 25 %), AI assistance significantly improved the FEQ, evaluated by both the AI system (0.29 [interquartile range (IQR) 0.27–0.30] vs. 0.23 [0.17–0.26]) and experts (14.00 [14.00–15.00] vs. 11.67 [10.00–13.33]) (both P < 0.001).

Conclusion The system’s evaluation of FEQ was strongly correlated with FEQ scores from experts, historical ADR, and withdrawal time of each colonoscopist. The system has the potential to enhance FEQ.

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Referenzen

References
1 Leufkens AM, van Oijen MG, Vleggaar FP. et al. Factors influencing the miss rate of polyps in a back-to-back colonoscopy study. Endoscopy 2012; 44: 470-475
2 Kaminski MF, Regula J, Kraszewska E. et al. Quality indicators for colonoscopy and the risk of interval cancer. N Engl J Med 2010; 362: 1795-1803
3 Rex DK. Colonoscopic withdrawal technique is associated with adenoma miss rates. Gastrointest Endosc 2000; 51: 33-36
4 Freedman D, Blau Y, Katzir L. et al. Detecting deficient coverage in colonoscopies. IEEE Trans Med Imaging 2020; 39: 3451-3462
5 Wang P, Xiao X, Glissen Brown JR. et al. Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nat Biomed Eng 2018; 2: 741-748
6 Zhou J, Wu L, Wan X. et al. A novel artificial intelligence system for the assessment of bowel preparation (with video). Gastrointest Endosc 2020; 91: 428-435
7 Su J-R, Li Z, Shao X-J. et al. Impact of a real-time automatic quality control system on colorectal polyp and adenoma detection: a prospective randomized controlled study (with videos). Gastrointest Endosc 2020; 91: 415-424
8 Gong D, Wu L, Zhang J. et al. Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. Lancet Gastroenterol Hepatol 2020; 5: 352-361
9 Duloy A, Yadlapati RH, Benson M. et al. Video-based assessments of colonoscopy inspection quality correlate with quality metrics and highlight areas for improvement. Clin Gastroenterol Hepatol 2019; 17: 691-700
10 Stanek SR, Tavanapong W, Wong J. et al. SAPPHIRE: a toolkit for building efficient stream programs for medical video analysis. Comput Methods Programs Biomed 2013; 112: 407-421
11 Rex DK, Schoenfeld PS, Cohen J. et al. Quality indicators for colonoscopy. Am J Gastroenterol 2015; 110: 72-90
12 Lee RH, Tang RS, Muthusamy VR. et al. Quality of colonoscopy withdrawal technique and variability in adenoma detection rates (with videos). Gastrointest Endosc 2011; 74: 128-134
13 May FP, Shaukat A. State of the science on quality indicators for colonoscopy and how to achieve them. Am J Gastroenterol 2020; 115: 1183-1190
14 Rex DK. Polyp detection at colonoscopy: endoscopist and technical factors. Best Pract Res Clin Gastroenterol 2017; 31: 425-433
15 Thirumurthi S, Ross WA, Raju GS. Can technology improve the quality of colonoscopy?. Curr Gastroenterol Rep 2016; 18: 38
16 Moons LMG, Gralnek IM, Siersema PD. Techniques and technologies to maximize mucosal exposure. Gastrointest Endosc Clin N Am 2015; 25: 199-210
17 Kaminski MF, Robertson DJ, Senore C. et al. Optimizing the quality of colorectal cancer screening worldwide. Gastroenterology 2020; 158: 404-417
18 Gessl I, Waldmann E, Penz D. et al. Evaluation of adenomas per colonoscopy and adenomas per positive participant as new quality parameters in screening colonoscopy. Gastrointest Endosc 2019; 89: 496-502
19 Dawwas MF. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med 2014; 370: 2539-2540
20 Chittleborough TJ, Luck A, Boussioutas A. et al. The conundrum of quality in colonoscopy. ANZ J Surg 2018; 88: 263-264
21 Barclay RL, Vicari JJ, Doughty AS. et al. Colonoscopic withdrawal times and adenoma detection during screening colonoscopy. N Engl J Med 2006; 355: 2533-2541
22 Shaukat A, Rector TS, Church TR. et al. Longer withdrawal time is associated with a reduced incidence of interval cancer after screening colonoscopy. Gastroenterology 2015; 149: 952-957

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