Improve Adenoma Detection Rate Research Articles

The role of artificial intelligence in colorectal cancer and polyp detection: A systematic review.

47 Background: Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide. Early detection and accurate diagnosis are essential for improving survival rates and optimizing therapeutic strategies. Recently, artificial intelligence (AI) technologies, such as machine learning algorithms, convolutional neural networks (CNNs), and computer-assisted diagnostic (CAD) systems, have significantly enhanced traditional diagnostic tools like colonoscopy and histopathology. This systematic review evaluates the current role of AI in CRC management, focusing on diagnostic improvements, predictive modeling, and outcome prediction. Methods: A comprehensive literature review was conducted using PubMed, Google Scholar, and MEDLINE using MeSH term to identify studies utilizing AI in CRC diagnosis and management, with a focus on diagnostic imaging, histopathological analysis, and predictive modeling. Studies were evaluated for the accuracy of AI-driven diagnostic systems and the predictive performance of AI models in clinical outcomes. Results: Out of 147 studies identified, 37 met the eligibility criteria for this review. AI tools such as CNNs, CAD systems, and EndoBRAIN showed high accuracy in CRC detection and polyp classification. Kudo et al. (2020) reported 98% CRC detection accuracy using EndoBRAIN. Blanes-Vidal et al. (2019) achieved 96.4% accuracy in polyp detection via DCNN. Additionally, Wang et al. (2019, 2020) demonstrated improved Adenoma Detection Rates (ADR) of 34.1% and 29.1%, respectively, using AI over traditional methods. For outcome prediction, AI models, including CNNs, predicted patient prognosis with reasonable accuracy, as highlighted by Skrede et al. (2020) with 76% accuracy in prognosis prediction. Conclusions: The systematic review and analysis of AI-assisted diagnostic modalities in colorectal cancer and polyp detection reveal promising results, with most models demonstrating high sensitivity, specificity, and accuracy. CNN-based models and CAD systems, in particular, show strong potential for improving detection rates and clinical outcomes in CRC screening. The studies included in this review demonstrate that AI can enhance diagnostic precision, particularly in identifying polyps and predicting clinical outcomes, with high validation rates. However, more external validation and long-term clinical studies are needed to fully establish the robustness of these AI tools in routine clinical practice.

Online patient endoscopy education platform improves outpatient bowel preparation quality: Retrospective observational study.

Background and study aims High-quality bowel preparation is integral to high-quality colonoscopy and adenoma detection. Studies evaluating the effect of pre-colonoscopy educational videos on bowel preparation quality have been variable. We investigated whether augmenting bowel preparation education using our professionally produced, patient-oriented, online educational video series would improve preparation quality, reduce need for repeat procedures, and improve adenoma detection rate (ADR). Patients and methods We conducted a pilot, retrospective, single-center observational study using endoscopy data from a tertiary hospital. Colonoscopy outcomes were compared between two discrete 6-month study periods, before (control group) and after (video group) implementation of the online video intervention. All patients received standard-of-care written and verbal instructions. The video group received a link providing access to the video platform. Primary outcome was adequacy of bowel preparation (defined by the Aronchick Scale). Secondary outcomes included rate of repeat colonoscopy due to inadequate preparation, ADR, and sessile serrated lesion (SSL) detection rate. Results The video intervention group had a lower rate of inadequate bowel preparation compared with the control group (6.3% vs 9.8%, P = 0.018). There was no difference between groups in rate of repeat colonoscopies due to inadequate preparation ( P = 0.62), ADR ( P = 0.11), or SSL detection rate ( P = 0.94). Multivariable analysis did not reveal any independent predictors of bowel preparation quality. Conclusions Our study supports the addition of a novel patient-oriented online educational video resource as an effective tool in enhancing bowel preparation adequacy while maintaining provision of high-quality colonoscopy.

A prospective multicenter randomized controlled trial on artificial intelligence assisted colonoscopy for enhanced polyp detection

Colon polyp detection and removal via colonoscopy are essential for colorectal cancer screening and prevention. This study aimed to develop a colon polyp detection program based on the RetinaNet algorithm and verify its clinical utility. To develop the AI-assisted program, the dataset was fully anonymized and divided into 10 folds for 10-fold cross-validation. Each fold consisted of 9,639 training images and 1,070 validation images. Video data from 56 patients were used for model training, and transfer learning was performed using the developed still image-based model. The final model was developed as a real-time polyp-detection program for endoscopy. To evaluate the model’s performance, a prospective randomized controlled trial was conducted at six institutions to compare the polyp detection rates (PDR). A total of 805 patients were included. The group that utilized the AI model showed significantly higher PDR and adenoma detection rate (ADR) than the group that underwent colonoscopy without AI assistance. Multivariate analysis revealed an OR of 1.50 for cases where polyps were detected. The AI-assisted polyp-detection program is clinically beneficial for detecting polyps during colonoscopy. By utilizing this AI-assisted program, clinicians can improve adenoma detection rates, ultimately leading to enhanced cancer prevention.

Additional 30-second observation of the right-sided colon for missed polyp detection with linked color imaging compared with narrow band imaging

Abstract Background and study aims We previously demonstrated the efficacy of an additional-30-seconds (Add-30s) observation with linked color imaging (LCI) or narrow band imaging (NBI) of the cecum and ascending colon (right-sided colon) after white light imaging (WLI) observation for improving adenoma detection rate (ADR) by 3% to 10%. We herein compared Add-30s LCI with Add-30s NBI in a large number of cases. Patients and methods We retrospectively collected 1023 and 1011 cases with Add-30s LCI and NBI observation for right-sided colon in 11 affiliated institutions from 2018 to 2022 and propensity score matching was performed. Add-30s observation was as follows. First observation: WLI observation of the right-sided colon as first observation. Second observation: Reobservation of right-sided colon by Add-30s LCI or NBI. The comparison of the mean numbers of adenoma+sessile serrated lesions (SSLs) and adenomas per patient (MASP and MUTYH-associated polyposis) were analyzed in the Add-30s LCI/NBI groups. The increase in right-sided ADR was also analyzed in the groups. Results Among 748 matched cases in the Add-30s LCI/NBI groups, the MASP and MAP were 0.18/0.19 (P = 0.54) and 0.14/0.15 (P = 0.70). Among experts, they were 0.17/0.22 (P = 0.16) and 0.15/0.21 (P = 0.08). Among non-experts, they were 0.13/0.12 (P = 0.71) and 0.12/0.07 (P = 0.04). The right-sided ADRs of the first+second observations in the LCI and NBI groups were 32.2% and 28.9% (P = 0.16) and the increase of ADRs were 7.5% and 7.2% (P = 0.84). Conclusions In right-sided colon, the detection of adenoma/SSL did not differ between Add-30s LCI and NBI. Both of them significantly increased ADR.

S895 Time and Experience Do Not Lead to Improved Adenoma Detection Rate With Artificial Intelligence-Assisted Colonoscopy: An 11-Month Implementation Trial

S895 Time and Experience Do Not Lead to Improved Adenoma Detection Rate With Artificial Intelligence-Assisted Colonoscopy: An 11-Month Implementation Trial

Long-term impact of artificial intelligence on colorectal adenoma detection in high-risk colonoscopy.

Improved adenoma detection rate (ADR) has been demonstrated with artificial intelligence (AI)-assisted colonoscopy. However, data on the real-world application of AI and its effect on colorectal cancer (CRC) screening outcomes is limited. To analyze the long-term impact of AI on a diverse at-risk patient population undergoing diagnostic colonoscopy for positive CRC screening tests or symptoms. AI software (GI Genius, Medtronic) was implemented into the standard procedure protocol in November 2022. Data was collected on patient demographics, procedure indication, polyp size, location, and pathology. CRC screening outcomes were evaluated before and at different intervals after AI introduction with one year of follow-up. We evaluated 1008 colonoscopies (278 pre-AI, 255 early post-AI, 285 established post-AI, and 190 late post-AI). The ADR was 38.1% pre-AI, 42.0% early post-AI (P = 0.77), 40.0% established post-AI (P = 0.44), and 39.5% late post-AI (P = 0.77). There were no significant differences in polyp detection rate (PDR, baseline 59.7%), advanced ADR (baseline 16.2%), and non-neoplastic PDR (baseline 30.0%) before and after AI introduction. In patients with an increased pre-test probability of having an abnormal colonoscopy, the current generation of AI did not yield enhanced CRC screening metrics over high-quality colonoscopy. Although the potential of AI in colonoscopy is undisputed, current AI technology may not universally elevate screening metrics across all situations and patient populations. Future studies that analyze different AI systems across various patient populations are needed to determine the most effective role of AI in optimizing CRC screening in clinical practice.

307 IMPROVING ADENOMA DETECTION RATE THROUGH FEEDBACK, EDUCATION, AND INCENTIVE - A QUALITY IMPROVEMENT INITIATIVE

307 IMPROVING ADENOMA DETECTION RATE THROUGH FEEDBACK, EDUCATION, AND INCENTIVE - A QUALITY IMPROVEMENT INITIATIVE

Improved Adenoma Detection Rate Using a Novel Colonoscopic Distal Attachment: A Multicenter Randomized Controlled Trial.

To evaluate the effect of Embrella, a novel-designed colonoscopic distal attachment, on adenoma detection rate (ADR) and adenoma per colonoscopy (APC), compared with standard colonoscopy in routine practice. All consecutive participants who underwent routine colonoscopic examinations at 3 endoscopy centers in China were enrolled. Participants were randomly assigned in a 1:1 ratio to the Embrella-assisted colonoscopy (EAC) or standard colonoscopy (SC) groups. ADR, APC, inspection time, pain scores, and adverse events were recorded. Overall, 1,179 participants were randomized into the EAC (n = 593) and SC groups (n = 586). EAC increased the overall ADR from 24.6% to 34.2% ( P < 0.001) and improved APC from 0.44 to 0.64 ( P = 0.002). Subgroup analyses indicated that EAC significantly improved ADR for adenomas < 10 mm (13.8% vs 8.5%, P = 0.004 for 5-9 mm and 27.0% vs 17.2%, P < 0.001 for < 5 mm), nonpedunculated adenomas (26.6% vs 18.8%, P < 0.001), and adenomas in the transverse (10.8% vs 6.1%, P = 0.004) and left colon (21.6% vs 13.7%, P < 0.001). APC in the subgroup analyses was consistent with ADR. The mean inspection time was shorter with EAC (6.52 vs 6.68 minutes, P = 0.046), with no significant impact on participants' pain scores ( P = 0.377). Moreover, no EAC-related adverse events occurred. EAC significantly increased ADR and APC compared with SC, particularly for adenomas <10 mm, nonpedunculated adenomas, and adenomas in the transverse and left colon.

Endocuff With or Without Artificial Intelligence-Assisted Colonoscopy in Detection of Colorectal Adenoma: A Randomized Colonoscopy Trial.

Both artificial intelligence (AI) and distal attachment devices have been shown to improve adenoma detection rate and reduce miss rate during colonoscopy. We studied the combined effect of Endocuff and AI on enhancing detection rates of various colonic lesions. This was a 3-arm prospective randomized colonoscopy study involving patients aged 40 years or older. Participants were randomly assigned in a 1:1:1 ratio to undergo Endocuff with AI, AI alone, or standard high-definition (HD) colonoscopy. The primary outcome was adenoma detection rate (ADR) between the Endocuff-AI and AI groups while secondary outcomes included detection rates of polyp (PDR), sessile serrated lesion (sessile detection rate [SDR]), and advanced adenoma (advanced adenoma detection rate) between the 2 groups. A total of 682 patients were included (mean age 65.4 years, 52.3% male), with 53.7% undergoing diagnostic colonoscopy. The ADR for the Endocuff-AI, AI, and HD groups was 58.7%, 53.8%, and 46.3%, respectively, while the corresponding PDR was 77.0%, 74.0%, and 61.2%. A significant increase in ADR, PDR, and SDR was observed between the Endocuff-AI and AI groups (ADR difference: 4.9%, 95% CI: 1.4%-8.2%, P = 0.03; PDR difference: 3.0%, 95% CI: 0.4%-5.8%, P = 0.04; SDR difference: 6.4%, 95% CI: 3.4%-9.7%, P < 0.01). Both Endocuff-AI and AI groups had a higher ADR, PDR, SDR, and advanced adenoma detection rate than the HD group (all P < 0.01). Endocuff in combination with AI further improves various colonic lesion detection rates when compared with AI alone.

Detection of sessile serrated adenoma using artificial intelligence-enhanced endoscopy: an Asian perspective.

As the serrated pathway has gained prominence as an alternative colorectal carcinogenesis pathway, sessile serrated adenomas or polyps (SSA/P) have been highlighted as lesions to rule out during colonoscopy. These lesions are however morphologically difficult to detect on endoscopy and can be mistaken for hyperplastic polyps due to similar endoscopic features. With the underlying nature of rapid progression and malignant transformation, interval cancer is a likely consequence of undetected or overlooked SSA/P. Real-time artificial intelligence (AI)-assisted colonoscopy via the computer-assisted detection system (CADe) is an increasingly useful tool in improving adenoma detection rate by providing a second eye during the procedure. In this article, we describe a guide through a video to illustrate the detection of SSA/P during AI-assisted colonoscopy. Consultant-grade endoscopists utilized real-time AI-assisted colonoscopy device, as part of a larger prospective study, to detect suspicious lesions which were later histopathologically confirmed to be SSA/P. All lesions were picked up by the CADe where a real-time green box highlighted suspicious polyps to the clinician. Three SSA/P of varying morphology are described with reference to classical SSA/P features and with comparison to the features of the hyperplastic polyp found in our study. All three SSA/P observed are in keeping with the JNET Classification (Type 1). In conclusion, CADe is a most useful aid to clinicians during endoscopy in the detection of SSA/P but must be complemented with factors such as good endoscopy skill and bowel prep for effective detection, and biopsy coupled with subsequent accurate histological diagnosis.

Artificial Intelligence-Assisted Colonoscopy in Real-World Clinical Practice: A Systematic Review and Meta-Analysis.

Artificial intelligence (AI) could minimize the operator-dependent variation in colonoscopy quality. Computer-aided detection (CADe) has improved adenoma detection rate (ADR) and adenomas per colonoscopy (APC) in randomized controlled trials. There is a need to assess the impact of CADe in real-world settings. We searched MEDLINE, EMBASE, and Web of Science for nonrandomized real-world studies of CADe in colonoscopy. Random-effects meta-analyses were performed to examine the effect of CADe on ADR and APC. The study is registered under PROSPERO (CRD42023424037). There was no funding for this study. Twelve of 1,314 studies met inclusion criteria. Overall, ADR was statistically significantly higher with vs without CADe (36.3% vs 35.8%, risk ratio [RR] 1.13, 95% confidence interval [CI] 1.01-1.28). This difference remained significant in subgroup analyses evaluating 6 prospective (37.3% vs 35.2%, RR 1.15, 95% CI 1.01-1.32) but not 6 retrospective (35.7% vs 36.2%, RR 1.12, 95% CI 0.92-1.36) studies. Among 6 studies with APC data, APC rate ratio with vs without CADe was 1.12 (95% CI 0.95-1.33). In 4 studies with GI Genius (Medtronic), there was no difference in ADR with vs without CADe (RR 0.96, 95% CI 0.85-1.07). ADR, but not APC, was slightly higher with vs without CADe among all available real-world studies. This difference was attributed to the results of prospective but not retrospective studies. The discrepancies between these findings and those of randomized controlled trials call for future research on the true impact of current AI technology on colonoscopy quality and the subtleties of human-AI interactions.

Exam 1: Endoscopy Unit Level Interventions to Improve Adenoma Detection Rate: A Systematic Review and Meta-Analysis

Exam 1: Endoscopy Unit Level Interventions to Improve Adenoma Detection Rate: A Systematic Review and Meta-Analysis

The adoption of artificial intelligence assisted endoscopy in the Middle East: challenges and future potential.

The use of artificial intelligence (AI) in endoscopy has shown immense potential to enhance diagnostic accuracy, streamline procedures, and improve patient outcomes. There are potential uses in every field of endoscopy, from improving adenoma detection rate (ADR) in colonoscopy to reducing read time in capsule endoscopy or minimizing blind spots in gastroscopy. Indeed, some of these systems are already licensed and in commercial use across the world. In the Middle East, where healthcare systems are rapidly evolving, there is a growing interest in adopting AI technologies to revolutionise endoscopic practices. This article provides an overview of the advancements, potential opportunities and challenges associated with the implementation of AI in endoscopy within the Middle East region. Our aim is to contribute to the ongoing dialogue surrounding the implementation of AI in endoscopy and consider some of the factors that are particularly relevant in the Middle Eastern context, including the need to train the models for local populations, cost and training, as well as trying to ensure equity of access for patients. It provides valuable insights for healthcare professionals, policymakers, and researchers interested in leveraging AI to enhance endoscopic procedures, improve patient care, and address the unique healthcare needs of the Middle East population.

S347 The Use of Artificial Intelligence to Improve Adenoma Detection Rate in a Community Practice

S347 The Use of Artificial Intelligence to Improve Adenoma Detection Rate in a Community Practice

S706 Artificial Intelligence-Assisted Colonoscopy Improves Adenoma Detection Rate (ADR) in Both Low and High ADR Endoscopists: A Meta-Analysis

S706 Artificial Intelligence-Assisted Colonoscopy Improves Adenoma Detection Rate (ADR) in Both Low and High ADR Endoscopists: A Meta-Analysis

Comparison of Artificial Intelligence with other interventions to improve Adenoma Detection Rate for Colonoscopy:

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Does I-Scan Improve Adenoma Detection Rate Compared to High-Definition Colonoscopy?

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A125 TANDEM STUDY DESIGN IS LESS LIKELY TO DEMONSTRATE IMPROVED ADENOMA DETECTION RATE THAN PARALLEL STUDY DESIGN IN THE ASSESSMENT OF ARTIFICIAL INTELLIGENCE-ASSISTED COLONOSCOPY

Abstract Background Randomized controlled trials have reported that artificial intelligence (AI) improves adenoma detection rate (ADR). Different methodologies, namely parallel and tandem study designs, have been employed to evaluate the efficacy of AI-assisted colonoscopy in randomized controlled trials. In systematic reviews and meta-analyses, a pooled effect that includes both study designs have been reported. However, it is unclear whether there are inconsistencies in the reported results of these two designs. Purpose To determine if there are differences in ADR using AI-aided technologies during colonoscopy between parallel and tandem study designs Method A systematic search of Ovid MEDLINE (1946 to October 2022) and EMBASE (1947 to October 2022) for randomized controlled trials comparing AI-assisted colonoscopy with routine high-definition colonoscopy in polyp detection was conducted. Reference lists of systematic reviews were searched for additional studies. The publications were divided based on trial design: parallel vs. tandem. Analysis was conducted using Review Manager 5.4.1 using a random effects model. Result(s) The search identified 540 articles. After screening the title and abstract for relevance, 19 randomized controlled trials involving a total of 14 657 patients were included for full-text review. Fourteen were parallel studies (14 136 patients) and 5 were tandem studies (521 patients). ADR was reported in 17 studies, and there was overall improvement in ADR with AI-assisted colonoscopy (risk ratio [RR] 1.33, 95% CI 1.22-1.44; p&amp;lt;.0001). Based on a separate pooled analyses of 13 parallel studies and 4 tandem studies, ADR significantly improved with AI assistance compared to routine colonoscopy, regardless of study design (RR 1.35, 95% CI 1.24-1.47 and p&amp;lt;.0001; RR 1.15, 95% CI 1.03-1.28; p=0.02, respectively). A significant increase in ADR with AI assistance were found in 84.6% (11/13) of parallel design studies, but in only 25% (1/4) of tandem studies. Image Conclusion(s) AI assistance during colonoscopy significantly increased ADR in both parallel and tandem studies; however, improvement in ADR was less likely to be seen in tandem compared to parallel studies. It remains unclear why this difference exists, but some hypotheses include smaller sample sizes in the tandem studies, significant heterogeneity in the tandem design, and differences in operator bias depending on study design. Better understanding the differences in these study designs will inform future studies of new endoscopic technologies. Disclosure of Interest None Declared

P267 Real-world artificial intelligence-aided colonoscopy does not improve adenoma detection rates in patients with Inflammatory Bowel Disease

Abstract Background Recently, the implementation of artificial intelligence-aided colonoscopy (AIAC) has resulted in improved performance of colorectal cancer (CRC) screening. The adenoma detection rate (ADR) is a key quality indicator in screening colonoscopies and achieving high ADRs is crucial for optimal prevention of CRC. Guidelines suggest that ADRs of 20% in women and 30% in males are indicative of adequate performance. Patients with inflammatory bowel disease (IBD) may be at increased risk for CRC based on disease extent and duration. However, ADR in patients with IBD can be generally lower than in average-risk patients, mainly due to an age gap and the presence of dysplasia associated lesions as opposed to sporadic adenomas. Indeed, there is no consensus for an accepted ADR target in these patients. In addition, the impact of AIAC on ADR in these patients has yet to be described. We aimed to explore the ADR of patients with IBD in a large-volume endoscopic center and evaluate the effect of AIAC on ADR. Methods This was a retrospective study conducted at a high-volume gastroenterology department where all endoscopy suites were equipped with an AIA device (GI Genius, Medtronic, Ireland) starting from July 1st 2021. Data was collected on all colonoscopies performed in the 11 months before the mentioned date and compared to a 15-month period afterwards. We excluded patients that performed colonoscopy due to evaluation of IBD severity, for known or suspected malignancy, therapeutic endoscopies, incomplete colonoscopies and colonoscopies with inadequate preparation. The primary outcome of the study was ADR. Results Our study included 996 colonoscopies (237 pre-AIAC, 759 AIAC). The groups were similar in age (median 43.8 years, interquartile ratio (IQR) 28.7-61.2 vs 44.5 years, IQR 30.7-59.1, p=0.76) and gender distribution (55.3% vs 54.3% males, p=0.82). In both groups, there were more Crohn’s disease (62.9% pre-AIAC, 57.2% AIAC, p=0.13) than ulcerative colitis patients. The ADR in the pre-AIAC group tended to be higher in comparison to AIAC (6.3% vs 4%, p=0.15); When limiting to experienced gastroenterologists only (≥ 5 years), ADR results were significant higher in the pre-AIAC group (7.6% vs 3.8%, p=0.035). In addition, total procedure time (considered the time from procedure start and end as recorded by the endoscopy nurse) was significantly shorter in the AIAC group (21 minutes, IQR 17-28 vs 25 minutes, IQR 19-37, p&amp;lt;0.0001). Conclusion In a large-volume tertiary center cohort, the introduction of AIAC to real-world colonoscopies did not improve ADR in patients with IBD, questioning the integration of AIAC in routine practice.

How does artificial intelligence-aided colonoscopy help to improve adenoma detection rates?

How does artificial intelligence-aided colonoscopy help to improve adenoma detection rates?