IJGII Inernational Journal of Gastrointestinal Intervention

pISSN 2636-0004 eISSN 2636-0012
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Article

Review Article

Int J Gastrointest Interv 2024; 13(3): 65-73

Published online July 31, 2024 https://doi.org/10.18528/ijgii240013

Copyright © International Journal of Gastrointestinal Intervention.

Effect of artificial intelligence-aided colonoscopy on the adenoma detection rate: A systematic review

Anson Mwango1,2 , Tayyab Saeed Akhtar2,3 , Sameen Abbas4 , Dua Sadaf Abbasi4 , and Amjad Khan4,5,*

1Department of Clinical Medicine and Therapeutics, University of Nairobi, Nairobi, Kenya
2Faculty of Life Science and Education, University of South Wales, Cardiff, United Kingdom
3Center for Liver and Digestive Diseases, Holy Family Hospital, Rawalpindi, Pakistan
4Department of Pharmacy, Quaid-i-Azam University, Islamabad, Pakistan
5Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmacy, Health Science Center, Xi’an Jiaotong University, Xi’an, China

Correspondence to:*Department of Pharmacy, Quaid-i-Azam University, Islamabad 45320, Pakistan.
E-mail address: amjadkhan@qau.edu.pk (A. Khan).

Received: March 11, 2024; Revised: March 28, 2024; Accepted: May 10, 2024

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/4.0) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Colorectal cancer has substantial morbidity and mortality. Approximately one-quarter of cases are overlooked during screening colonoscopy, leading to interval colorectal cancer. The use of artificial intelligence (AI) through deep learning systems has demonstrated promising results in the detection of polyps and adenomas. Consequently, our objective was to evaluate the impact of AI on adenoma detection. To identify relevant studies, we searched the PubMed, MEDLINE, and Cochrane Library databases without restrictions on publication date. Ultimately, we analyzed 16 randomized controlled trials involving 13,685 participants. The primary outcome assessed was the effect of AI-assisted colonoscopy (AIAC) on the adenoma detection rate (ADR). Secondary outcomes included the polyp detection rate (PDR) and adenomas per colonoscopy (APC). A random-effects model was used to calculate pooled effect sizes, and statistical heterogeneity was evaluated using the Higgins I2 statistic, with I2 cutoff points of 25%, 50%, and 75% indicating low, moderate, and high heterogeneity, respectively. Publication bias was investigated using a funnel plot, and the quality of evidence was appraised using the Grading of Recommendations, Assessment, Development, and Evaluation framework. The findings indicated a 26% greater ADR with AIAC than with standard colonoscopy (40.4% vs. 31.9%). Additionally, AIAC was associated with a 30% greater PDR (52.9% vs. 40.1%) and a 44% higher APC. The findings demonstrate that the integration of AI in colonoscopy improves ADR, PDR, and APC, potentially reducing the incidence of interval colorectal cancer.

Keywords: Adenoma, Artificial intelligence, Colonoscopy, Colorectal neoplasms

Colorectal cancer (CRC) is a significant contributor to global morbidity and mortality. In 2019, it accounted for 2.17 million cases and 1.09 million deaths, while its incidence has more than doubled over the last 10 years.1 Risk factors for CRC include both modifiable and non-modifiable characteristics. Non-modifiable factors include age, family history of CRC, genetic mutations, race, and a history of inflammatory bowel disease. Modifiable factors include diet, smoking, alcohol intake, physical inactivity, and high body mass index. Most CRC lesions arise from adenomatous polyps or sessile serrated lesions. The adenoma-carcinoma pathway accounts for 60%–70% of all CRCs, while the serrated pathway produces about 15%–30% of CRC lesions. These premalignant lesions exhibit identifiable features on colonoscopy.2

CRC mortality can be reduced by addressing modifiable risk factors and employing various screening methods. These include stool-based tests, semi-invasive radiographic methods, and direct visualization of the distal or entire colon through sigmoidoscopy or colonoscopy.2 While colonoscopy is highly sensitive in detecting precancerous and cancerous lesions, its effectiveness can vary, with overlooked cases potentially developing into post-colonoscopy CRC (PCCRC) or interval CRC.3 A recent meta-analysis reported a 26% rate of missed adenomas during colonoscopy, which can be attributed to limitations in mucosal exposure and endoscopist recognition.4 Quality measures have been developed to improve lesion detection, such as the adenoma detection rate (ADR) and the cecal intubation rate. A minimum ADR of 30% for men and 20% for women is recommended, with each 1% increase in ADR reportedly resulting in a 3% decline in PCCRC incidence.5,6

Artificial intelligence (AI) using deep learning shows promise in advancing colonoscopy practice by autonomously analyzing image data to identify patterns. Computer-aided detection (CADe) and computer-aided diagnosis (CADx) are key applications of AI in this field.7 AI also aids in polyp detection, classification, screening, and surveillance, potentially reducing healthcare costs by eliminating the need to remove low-risk polyps. AI may also be beneficial in medical education.810 Physician sentiment toward AI is generally positive, and recent clinical trials have broadened the evidence base for the impact of AI on adenoma detection. Given the importance of screening for CRC, the role of AI in colonoscopy is becoming increasingly pivotal. This technology can address the known stages of CRC development, the time required for CRC to develop, and the available procedures for detecting and removing polyps. Consequently, it presents a considerable opportunity to advance CRC prevention and early detection through screening. CADe systems improve adenoma detection, instilling confidence in physicians. This systematic review aims to evaluate the impact of AI-assisted colonoscopy (AIAC) on ADR. The study also explores the effects of AIAC on withdrawal time, polyp detection rate (PDR), and advanced adenomas per colonoscopy (APC).

The application of AI in gastrointestinal endoscopy, particularly colonoscopy, has garnered considerable interest in recent years. Accordingly, this review was conducted, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.11

Data sources and search strategy

A comprehensive literature search was conducted across the PubMed, MEDLINE, and Cochrane Libraries databases using specific search criteria. A filter for “trials” was applied without publication date limitations. The search terms included “Colonoscopy,” “Adenoma,” “AI,” “polyp,” “ADR,” “CADe,” “Colon,” “Colorectal,” “Deep learning,” and other relevant terms. Additional articles were obtained from the references of the identified studies.

Inclusion and exclusion criteria

The inclusion criteria for this review were participants aged 18 years or older who had undergone colonoscopy. Studies eligible for inclusion were required to be published in English or translated into English, utilize a randomized controlled trial (RCT) design, and include an intervention involving AIAC. Articles published solely as abstracts without detailed data were excluded from the analysis. These criteria were established to ensure a thorough and rigorous examination of relevant research on the effects of AIAC in randomized controlled environments, emphasizing studies with adequate detail for a substantive analysis.

Data extraction

The selection process involved multiple stages to ensure the inclusion of relevant studies. Following the removal of duplicates, titles and abstracts were screened to identify potentially eligible articles. Subsequently, the full texts of these articles were retrieved for a more detailed evaluation based on the inclusion and exclusion criteria. A primary investigator was responsible for data extraction. To capture relevant study details, a data abstraction form was employed, which included information such as the study title, first author, publication year, study design, AI methodology, patient demographics (number, mean age, sex, and Boston Bowel Preparation Scale), characteristics of the endoscopists, ADR, PDR, and withdrawal time.

Quality assessment

A risk of bias assessment was conducted to ensure the validity and quality of the included studies. The Cochrane risk of bias tool was utilized for RCTs to identify potential sources of bias that could affect the reliability and generalizability of the results. Studies with a high risk of bias were excluded.

Ethical consideration

Before initiating this study, we obtained ethical approval from the Subgroup and Faculty Research Ethics Committee at the University of South Wales.

Data management

Statistical analysis was performed using SPSS version 21 (IBM Corp.), with a P-value of less than 0.05 considered to indicate statistical significance. For each trial, quantitative data were collated, and the effect of the intervention was assessed. Pooled effect sizes were calculated using a random-effects model. Statistical heterogeneity was assessed using the Higgins I2 statistic, with I2 cutoff points of 25%, 50%, and 75% indicating low, moderate, and high heterogeneity, respectively.12 Publication bias was examined using a funnel plot. The quality of evidence was appraised using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach.13

Study selection

The initial search strategy identified 633 studies. Of these, 51 were excluded due to being duplicates. The remaining 582 studies underwent screening for inclusion in the analysis, resulting in the exclusion of 565. The full details for one study could not be retrieved. Ultimately, 16 studies were evaluated for eligibility. Further scrutiny of the references for these studies did not yield any additional studies for inclusion. Therefore, only these 16 studies were included in the analysis (Fig. 1).

Figure 1. PRISMA flow diagram for identification of studies. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Study characteristics

The 16 studies that met the inclusion criteria involved a total of 13,685 participants, with 6,820 undergoing AIAC and 6,865 undergoing standard colonoscopy. These studies were conducted between 2017 and 2022. The smallest study had a sample size of 223 participants, while the largest included 3,059 participants. Seven studies were conducted in China,1420 three in Italy,2123 and one each in England,24 the United States,25 Israel,26 Thailand,27 Latvia,28 and Spain.29 All but six of the studies were single-center in design, and five were not blinded. No significant differences were found between groups undergoing AIAC or standard colonoscopy. Each study employed a convolutional neural network machine learning approach within their respective CADe systems. Tables 1 and 21429 provide a more detailed summary of the study and sociodemographic characteristics. The overall risk of bias among the included studies was low, as indicated in Fig. 2 and 3.1429

Table 1 . Characteristics of the Included Studies.

AuthorPublication yearCountryAI useNo. of endoscopistsEndoscopist experienceScreening colonoscopy (%)
Liu et al142020ChinaWithdrawal--66
Repici et al222022ItalyWithdrawal and insertion10Non-expert29
Liu et al152020ChinaWithdrawal11Both23
Yao et al162022ChinaWithdrawal4Expert89
Wang et al172020ChinaWithdrawal4Expert16
Wang et al182019ChinaWithdrawal8Both8
Wang et al192023ChinaWithdrawal8Expert17
Xu et al202023ChinaWithdrawal and insertion12Both100
Lachter et al262023IsraelWithdrawal and insertion7Expert32
Rondonotti et al212022ItalyWithdrawal and insertion21Expert0
Repici et al232020ItalyWithdrawal and insertion6Expert22
Gimeno-García et al292023SpainWithdrawal8Expert40
Vilkoite et al282023LatviaWithdrawal2Expert-
Aniwan et al272023ThailandWithdrawal and insertion17Both89
Ahmad et al242023EnglandWithdrawal and insertion8Expert0
Glissen Brown et al252022USAWithdrawal-Expert60

AI, artificial intelligence..



Table 2 . Sociodemographic Characteristics of the Populations Analyzed in the Included Studies.

AuthorPatients (AI:Control)Age (yr)-AI Mean ± SD or mean (range)Age (yr)-C Mean ± SD or mean (range)Male-AI n (%)Male-C n (%)
Liu et al141,026 (508:518)51.0 ± 12.350.1 ± 12.7264 (52)287 (55)
Repici et al22660 (330:330)61.9 ± 9.862.6 ± 10.2174 (53)156 (47)
Liu et al15790 (393:397)49.8 ± 13.148.8 ± 13.0180 (46)194 (49)
Yao et al16539 (268:271)50.7 ± 13.250.9 ± 13.6121 (45)114 (42)
Wang et al17962 (484:478)49 (39.0–60.0)49 (40.3–56.0)241 (50)254 (53)
Wang et al181,058 (522:536)51.1 ± 13.249.9 ± 13.8263 (50)249 (46)
Wang et al191,261 (636:625)46 (36.75–54.00)47 (37.00–55.00)364 (57)326 (52)
Xu et al203,059 (1,519:1,540)57.49 ± 7.5557.03 ± 7.43707 (47)728 (47)
Lachter et al26674 (330:344)61.0 ± 9.9560.8 ± 9.79--
Rondonotti et al21800 (405:395)62 (56–68)61 (55–67)213 (53)196 (50)
Repici et al23685 (341:344)61.5 ± 9.761.1 ± 10.6169 (50)179 (52)
Gimeno-García et al29312 (155:157)62.99 ± 10.2664.71 ± 11.7982 (53)83 (53)
Vilkoite et al28400 (196:204)50.1 ± 15.451.2 ± 14.591 (47)102 (50)
Aniwan et al27622 (312:310)62.8 ± 6.8262.0 ± 6.82133 (43)133 (43)
Ahmad et al24614 (308:306)66.2 ± 5.466.4 ± 5.4110 (36)98 (32)
Glissen Brown et al25223 (113:110)61.18 ± 9.8360.51 ± 8.4554 (48)68 (62)

AI, artificial intelligence; C, control; SD, standard deviation..



Figure 2. Risk of bias summary plot.

Figure 3. Risk of bias traffic light plot.

ADR and PDR

Based on data from the 16 included studies, the overall ADR was significantly higher in the AIAC group compared to the control group (2,753/6,820 [40.4%] vs. 2,188/6,865 [31.9%]; relative risk [RR] = 1.26; 95% confidence interval [CI], 1.19–1.33; P < 0.01). All but four of the included studies reported a significantly greater ADR with AIAC. Moderate heterogeneity (I2 = 38%) was observed in the magnitude of effect (Fig. 4).1429 Across 11 studies reporting PDR, this metric was also significantly higher in the AIAC group compared to the control group (2,068/3,913 [52.9%] vs. 1,581/3,946 [40.1%]; RR = 1.30; 95% CI, 1.16–1.44; P < 0.01). Only three of these studies reported no significant increase in PDR. A high level of heterogeneity (I2 = 83%) was present across the 11 studies (Fig. 5).1429 No small-study effect was observed upon examination using funnel plots.

Figure 4. Comparative effectiveness of artificial intelligence (AI)-assisted colonoscopy versus standard colonoscopy (SC) on adenoma detection rate (ADR; heterogeneity: tau2 = 0.01; chi2 = 24.20, df = 15 [P = 0.06]; I2 = 38%; test for overall effect: Z = 7.74 [P < 0.001]). MH, Mantel-Haenszel; CI, confidence interval; CADe, computer-aided detection.

Figure 5. Comparative effectiveness of artificial intelligence (AI)-assisted colonoscopy versus standard colonoscopy (SC) on polyp detection rate (PDR; heterogeneity: tau2 = 0.03; chi2 = 58.31, df = 10 [P < 0.001]; I2 = 83%; test for overall effect: Z = 4.69 [P < 0.001]). MH, Mantel-Haenszel; CI, confidence interval; CADe, computer-aided detection.

APC

Fifteen studies compared the APC between standard colonoscopy and AIAC. The APC was significantly higher with AIAC compared to standard white-light colonoscopy (odds ratio = 1.44; 95% CI, 1.35–1.54; P < 0.001, I2 = 15%). Overall, a 44% greater APC was observed with the use of CADe systems (Fig. 6).1429 However, four studies within this analysis did not report a significant increase in APC.

Figure 6. Comparative effectiveness of artificial intelligence (AI)-assisted colonoscopy versus standard colonoscopy (SC) in adenomas per colonoscopy (APC). CI, confidence interval; CADe, computer-aided detection.

Quality of evidence

The quality of evidence was evaluated using the GRADE methodology. The evidence level for the RCTs was downgraded due to the moderate quality of the trials, variability among endoscopists, differences in indications for colonoscopy, and diversity of primary outcomes assessed across the studies.

The objective of this study was to assess the impact of AIAC on adenoma detection. A comprehensive analysis of 16 RCTs that met the inclusion criteria for the meta-analysis revealed significantly superior ADR, PDR, and APC with the use of AI. The findings indicated a 26% relative increase in ADR, reflecting a substantial improvement. AIAC was also associated with a 30% higher PDR. These outcomes support the integration of AIAC into clinical practice. Importantly, ADR serves as a crucial predictor of interval CRC following screening colonoscopy. All studies included in this review reported an ADR exceeding 15%, which aligns with the recommendations of the British Society of Gastroenterology (BSG). The BSG suggests a minimum ADR of 15% and an aspirational ADR of 20%, which is associated with a reduced incidence of interval CRC.30 Notably, all but one study failed to achieve a 15% ADR in the standard colonoscopy group.

Notably, the indication for colonoscopy in these studies was not consistent. Most of the patients did not undergo colonoscopy for screening purposes. Additionally, some of the included studies were conducted in tandem settings, making it difficult to attribute the detection rates solely to failures in recognizing lesions. The higher ADR associated with AIAC in this review may be due to the increased capacity to detect previously missed or unrecognized lesions through AI. This underscores the potential of AI as a valuable adjunct for colonoscopy, particularly for less experienced endoscopists.10 The observed results for ADR and PDR suggest that AIAC improves the identification of precancerous lesions and polyps, facilitating earlier diagnosis and intervention. This represents a major development in CRC prevention, as it could reduce the incidence of advanced-stage CRC and ultimately save lives. The improved diagnostic accuracy and efficiency provided by AI technology enable endoscopists to make more informed decisions during the procedure, improving patient outcomes and reducing the burden on healthcare systems. Furthermore, the impact of AI on ADR extends beyond merely increasing the number of adenomas detected during colonoscopy. The capacity of AI algorithms to assist endoscopists in targeting specific areas of interest may decrease the likelihood of overlooking adenomas. This leads to more thorough examinations and an increase in the overall yield of APC.

The pooled estimates from this study indicated a 44% increase in APC, which may be due to the superior detection of small or miniature lesions by AIAC. Compared to ADR, APC offers a slightly better assessment of the quality of examination of the entire colon and provides a degree of differentiation among endoscopists. However, a potential downside is the increased cost, particularly if endoscopists are required to remove all polyps during the procedure.6 Still, these potential benefits have been previously documented and are being closely considered in colonoscopy quality improvement programs, where APC could signify superior quality of endoscopic examinations.31

The impact of AI on APC is closely linked to its effects on ADR and PDR. By improving the detection of adenomas and polyps, AI technology can increase the overall adenoma yield during colonoscopy, as reflected by APC. This improved APC metric is essential in CRC prevention, as it reflects the effectiveness of colonoscopy in identifying and removing precancerous lesions. A higher APC indicates a more thorough and successful colon examination, reducing the risk of missed adenomas and improving patient outcomes.

This study contributes to the growing body of literature suggesting that the quality metrics of colonoscopy can be enhanced by incorporating CADe devices. Despite limitations, previous research has demonstrated the impact of AIAC. These studies have consistently reported a benefit to ADR associated with AIAC, although the extent of this difference has varied. Some of these meta-analyses incorporated a relatively small sample size, employed different analytical methods, and included retrospective studies, which generally provide a lower level of evidence.3234

The interest in AI within the field of medicine has grown, presenting substantial potential for application. In endoscopic practice, the utility of this technology is potentially immense, with the potential to influence clinically relevant outcomes. This systematic review and meta-analysis offers evidence that the additional use of AI in standard colonoscopy may improve lesion detection. Such advancements could decrease the incidence of CRC and refine clinical practice. Future studies should evaluate the impact of the overdiagnosis of smaller lesions, including evaluations across diverse populations, and examine the differential effects on both high and low detectors. Moreover, the more frequent implementation of tandem colonoscopy could provide a more accurate determination of the rate of missed lesions. Overall, the existing evidence is promising and underscores the considerable impact of AI on the practice of colonoscopy.

Strengths of the study

The present study has several strengths. It exclusively incorporated RCTs, thereby minimizing the risk of bias. A total of 16 trials were selected for analysis, representing a valuable addition to the existing research and providing a larger and more diverse aggregate sample size for evaluation and comparison. This study is among the first to use solely randomized data to demonstrate the impact of AI on the practice of colonoscopy. Our review adhered to the Cochrane and GRADE guidelines and included an extensive systematic search of multiple databases.

Limitations of the study

Despite the potential benefits of AIAC, challenges and limitations exist in its widespread implementation. Technical issues, such as false positives and false negatives, require ongoing refinement to improve diagnostic accuracy. Moreover, integrating AI into clinical practice necessitates appropriate training for endoscopists to effectively interpret AI-generated data during colonoscopy procedures. Furthermore, the present meta-analysis has several limitations. Notably, seven of the 16 included trials were conducted in China, which limits the generalizability of the findings due to varying epidemiological patterns. The per-polyp analysis does not account for additional patient and lesion characteristics, and some trials lacked essential data, potentially introducing bias. The absence of information regarding outcome areas, screening proportions, and baseline parameters further complicates the analysis. Furthermore, factors that could influence ADR, such as patient-related or image-related variables, were not considered, potentially impacting this measurement.

Clinical implications and future directions

The consistent improvements in ADR, PDR, and APC observed with AIAC have meaningful clinical implications for gastroenterology and CRC screening. First, AI algorithms are valuable tools for endoscopists, providing real-time CADe and CADx, alerting them to suspicious regions, and increasing confidence in identifying adenomas and polyps during colonoscopy. Second, the superior ADR and APC suggest that AI assistance can contribute to the early detection and intervention of precancerous lesions, potentially reducing the incidence and mortality associated with CRC. The role of AI in improving CRC screening highlights its importance in public health initiatives aimed at combating this common and preventable form of cancer. Furthermore, the increased PDR achieved with AI support may lead to more frequent and effective surveillance of patients at high risk, facilitating targeted and personalized strategies in the detection of various types of polyps.8,35

We are grateful to the staff at the Faculty of Life Science and Education, University of South Wales, for their support.

All data generated or analyzed during this study are included in this article. The datasets used and/or analyzed in this study are available from the corresponding author upon reasonable request.

No potential conflict of interest relevant to this article was reported.

  1. Sharma R, Abbasi-Kangevari M, Abd-Rabu R, Abidi H, Abu-Gharbieh E, Acuna JM, et al; GBD 2019 Colorectal Cancer Collaborators. Global, regional, and national burden of colorectal cancer and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease study 2019. Lancet Gastroenterol Hepatol. 2022;7:627-47.
  2. Kanth P, Inadomi JM. Screening and prevention of colorectal cancer. BMJ. 2021;374:n1855.
    Pubmed CrossRef
  3. Rutter MD, Beintaris I, Valori R, Chiu HM, Corley DA, Cuatrecasas M, et al. World Endoscopy Organization consensus statements on post-colonoscopy and post-imaging colorectal cancer. Gastroenterology. 2018;155:909-25.e3.
    Pubmed CrossRef
  4. Zhao S, Wang S, Pan P, Xia T, Chang X, Yang X, et al. Magnitude, risk factors, and factors associated with adenoma miss rate of tandem colonoscopy: a systematic review and meta-analysis. Gastroenterology. 2019;156:1661-74.e11.
    Pubmed CrossRef
  5. Corley DA, Jensen CD, Marks AR, Zhao WK, Lee JK, Doubeni CA, et al. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med. 2014;370:1298-306.
    Pubmed KoreaMed CrossRef
  6. Rex DK, Schoenfeld PS, Cohen J, Pike IM, Adler DG, Fennerty MB, et al. Quality indicators for colonoscopy. Am J Gastroenterol. 2015;110:72-90.
    Pubmed CrossRef
  7. Joseph J, LePage EM, Cheney CP, Pawa R. Artificial intelligence in colonoscopy. World J Gastroenterol. 2021;27:4802-17.
    Pubmed KoreaMed CrossRef
  8. Sinagra E, Badalamenti M, Maida M, Spadaccini M, Maselli R, Rossi F, et al. Use of artificial intelligence in improving adenoma detection rate during colonoscopy: might both endoscopists and pathologists be further helped. World J Gastroenterol. 2020;26:5911-8.
    Pubmed KoreaMed CrossRef
  9. Areia M, Mori Y, Correale L, Repici A, Bretthauer M, Sharma P, et al. Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study. Lancet Digit Health. 2022;4:e436-44.
    Pubmed CrossRef
  10. Hann A, Troya J, Fitting D. Current status and limitations of artificial intelligence in colonoscopy. United European Gastroenterol J. 2021;9:527-33.
    Pubmed KoreaMed CrossRef
  11. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.
    Pubmed KoreaMed CrossRef
  12. Grant J, Hunter A. Measuring inconsistency in knowledgebases. J Intell Inf Syst. 2006;27:159-84.
    CrossRef
  13. Granholm A, Alhazzani W, Møller MH. Use of the GRADE approach in systematic reviews and guidelines. Br J Anaesth. 2019;123:554-9.
    Pubmed CrossRef
  14. Liu WN, Zhang YY, Bian XQ, Wang LJ, Yang Q, Zhang XD, et al. Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy. Saudi J Gastroenterol. 2020;26:13-9.
    Pubmed KoreaMed CrossRef
  15. Liu P, Wang P, Glissen Brown JR, Berzin TM, Zhou G, Liu W, et al. The single-monitor trial: an embedded CADe system increased adenoma detection during colonoscopy: a prospective randomized study. Therap Adv Gastroenterol. 2020;13:1756284820979165.
    Pubmed KoreaMed CrossRef
  16. Yao L, Zhang L, Liu J, Zhou W, He C, Zhang J, et al. Effect of an artificial intelligence-based quality improvement system on efficacy of a computer-aided detection system in colonoscopy: a four-group parallel study. Endoscopy. 2022;54:757-68.
    Pubmed CrossRef
  17. Wang P, Liu X, Berzin TM, Glissen Brown JR, Liu P, Zhou C, et al. Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol Hepatol. 2020;5:343-51.
    Pubmed CrossRef
  18. Wang P, Berzin TM, Glissen Brown JR, Bharadwaj S, Becq A, Xiao X, et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut. 2019;68:1813-9.
    Pubmed KoreaMed CrossRef
  19. Wang P, Liu XG, Kang M, Peng X, Shu ML, Zhou GY, et al. Artificial intelligence empowers the second-observer strategy for colonoscopy: a randomized clinical trial. Gastroenterol Rep (Oxf). 2023;11:goac081.
    Pubmed KoreaMed CrossRef
  20. Xu H, Tang RSY, Lam TYT, Zhao G, Lau JYW, Liu Y, et al. Artificial intelligence-assisted colonoscopy for colorectal cancer screening: a multicenter randomized controlled trial. Clin Gastroenterol Hepatol. 2023;21:337-46.e3.
    Pubmed CrossRef
  21. Rondonotti E, Di Paolo D, Rizzotto ER, Alvisi C, Buscarini E, Spadaccini M, et al; AIFIT Study Group. Efficacy of a computer-aided detection system in a fecal immunochemical test-based organized colorectal cancer screening program: a randomized controlled trial (AIFIT study). Endoscopy. 2022;54:1171-9.
    Pubmed CrossRef
  22. Repici A, Spadaccini M, Antonelli G, Correale L, Maselli R, Galtieri PA, et al. Artificial intelligence and colonoscopy experience: lessons from two randomised trials. Gut. 2022;71:757-65.
    Pubmed CrossRef
  23. Repici A, Badalamenti M, Maselli R, Correale L, Radaelli F, Rondonotti E, et al. Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial. Gastroenterology. 2020;159:512-20.e7.
    Pubmed CrossRef
  24. Ahmad A, Wilson A, Haycock A, Humphries A, Monahan K, Suzuki N, et al. Evaluation of a real-time computer-aided polyp detection system during screening colonoscopy: AI-detect study. Endoscopy. 2023;55:313-9.
    Pubmed CrossRef
  25. Glissen Brown JR, Mansour NM, Wang P, Chuchuca MA, Minchenberg SB, Chandnani M, et al. Deep learning computer-aided polyp detection reduces adenoma miss rate: a United States multi-center randomized tandem colonoscopy study (CADeT-CS trial). Clin Gastroenterol Hepatol. 2022;20:1499-507.e4.
    Pubmed CrossRef
  26. Lachter J, Schlachter SC, Plowman RS, Goldenberg R, Raz Y, Rabani N, et al. Novel artificial intelligence-enabled deep learning system to enhance adenoma detection: a prospective randomized controlled study. iGIE. 2023;2:52-8.
    CrossRef
  27. Aniwan S, Mekritthikrai K, Kerr SJ, Tiankanon K, Vandaungden K, itunyarat Y Sr, et al. Computer-aided detection, mucosal exposure device, their combination, and standard colonoscopy for adenoma detection: a randomized controlled trial. Gastrointest Endosc. 2023;97:507-16.
    Pubmed CrossRef
  28. Vilkoite I, Tolmanis I, Meri HA, Polaka I, Mezmale L, Anarkulova L, et al. The role of an artificial intelligence method of improving the diagnosis of neoplasms by colonoscopy. Diagnostics (Basel). 2023;13:701.
    Pubmed KoreaMed CrossRef
  29. Gimeno-García AZ, Hernández Negrin D, Hernández A, Nicolás-Pérez D, Rodríguez E, Montesdeoca C, et al. Usefulness of a novel computer-aided detection system for colorectal neoplasia: a randomized controlled trial. Gastrointest Endosc. 2023;97:528-36.e1.
    Pubmed CrossRef
  30. Rees CJ, Thomas Gibson S, Rutter MD, Baragwanath P, Pullan R, Feeney M, et al; British Society of Gastroenterology, the Joint Advisory Group on GI Endoscopy, the Association of Coloproctology of Great Britain and Ireland. UK key performance indicators and quality assurance standards for colonoscopy. Gut. 2016;65:1923-9.
    Pubmed KoreaMed CrossRef
  31. Wang HS, Pisegna J, Modi R, Liang LJ, Atia M, Nguyen M, et al. Adenoma detection rate is necessary but insufficient for distinguishing high versus low endoscopist performance. Gastrointest Endosc. 2013;77:71-8.
    Pubmed CrossRef
  32. Barua I, Vinsard DG, Jodal HC, Løberg M, Kalager M, Holme Ø, et al. Artificial intelligence for polyp detection during colonoscopy: a systematic review and meta-analysis. Endoscopy. 2021;53:277-84.
    Pubmed CrossRef
  33. Nazarian S, Glover B, Ashrafian H, Darzi A, Teare J. Diagnostic accuracy of artificial intelligence and computer-aided diagnosis for the detection and characterization of colorectal polyps: systematic review and meta-analysis. J Med Internet Res. 2021;23:e27370.
    Pubmed KoreaMed CrossRef
  34. Shao L, Yan X, Liu C, Guo C, Cai B. Effects of ai-assisted colonoscopy on adenoma miss rate/adenoma detection rate: a protocol for systematic review and meta-analysis. Medicine (Baltimore). 2022;101:e31945.
    Pubmed KoreaMed CrossRef
  35. Larsen SLV, Mori Y. Artificial intelligence in colonoscopy: a review on the current status. DEN Open. 2022;2:e109.
    Pubmed KoreaMed CrossRef