To assess the impact of bowel cleansing quality on polyp detection rate (PDR) and adenoma detection rate (ADR) and explore predictors of lesion detection rate in patients undergoing colonoscopy.

This is a post-hoc analysis of a multicenter randomized controlled trial (RCT) comparing 1L polyethylene glycol plus ascorbate (1L PEG+ASC) vs. 4L PEG as bowel preparation for colonoscopy.

PDR was significantly higher (35.6% vs. 18.5%, p = 0.013), and ADR was higher even if not significantly (25.6% vs. 16.7%, p = 0.153) in patients with Boston Bowel Preparation Scale (BBPS) ≥6 over BBPS <6. Comparing patients with BBPS = 9 over BBPS = 7-8, no significant differences were found in PDR (34.5% vs. 38.4%, p = 0.483) nor ADR (24.1% vs. 27.2%, p = 0.553). At multivariable regression analysis, older age (OR = 1.042, 95%CI = 1.021-1.063; p < 0.001), shorter intubation time (OR = 0.891, 95%CI = 0.816-0.972; p = 0.010), higher withdrawal time (OR = 1.171, 95%CI = 1.094-1.253; p < 0.001) and full consumption of the first dose (OR = 8.368, 95%CI = 1.025-68.331; p = 0.047) were independently associated with ADR.

This post-hoc analysis of a RCT showed that excellent cleansing (BBPS = 9) over high-quality cleansing (BBPS = 7-8) does not significantly improve PDR or ADR. Neither cleansing success nor preparation types were independently associated with ADR. Compliance with bowel preparation, timing of colonoscopy and withdrawal time are key elements for adequate ADR with potential implications for reducing interval colorectal cancer.

The adenoma detection rate is higher among endoscopists who spend more time observing screen edges during colonoscopies. Nonetheless, eye movement parameters related to lesion detection remain unknown. This study aimed to determine the specific eye movement parameters related to colorectal adenoma detection, including the gaze rate in a particular area and eye movement speed.

This study was a post hoc analysis of a randomized controlled trial investigating the effect of modifying eye movements of endoscopists on colorectal adenoma detection. Gaze rate at a specific area and eye movement speed were calculated based on endoscopist gaze coordinates in each examination. Time required for observation and treatment of polyps was excluded. The lower peripheral area was defined as the bottom row when the screen was divided into 6×6 sections. These parameters were compared between patients with and without adenomas.

Five physicians performed 158 colonoscopies. The adenoma detection group exhibited a lower peripheral gaze rate (13.7% vs. 9.5%, P = 0.004) and smaller average eye movement distance (29.9 pixels/30 ms vs. 33.3 pixels/30 ms, P = 0.022). Logistic regression analysis showed that a lower peripheral gaze rate > 13.0% and an average eye movement distance <30 pixels/30 ms were increased independent predictors of adenoma detection ( P = 0.024, odds ratio [OR] 2.53, 95% confidence interval [CI] 1.71-3.28; P = 0.045, OR 4.57, 95% CI 1.03-20.2), whereas age, sex, and withdrawal time were not.

Lower peripheral gaze rate and slow eye movement are associated with colorectal adenoma detection.

Quality of gastrointestinal endoscopy is a major determinant of its effectiveness. Artificial intelligence (AI) has the potential to enhance quality monitoring and improve endoscopy outcomes. This article reviews the current literature on AI algorithms that have been developed for endoscopy quality assessment.

It remains unclear if the increased colorectal neoplasia detection rate in inflammatory bowel disease (IBD) by high-definition (HD) dye-based chromoendoscopy compared with HD white-light endoscopy is due to enhanced contrast or increased inspection times. Longer withdrawal times may yield similar neoplasia detection rates as found by HD chromoendoscopy.

To compare colorectal neoplasia detection rates for HD white-light endoscopy with segmental re-inspection and HD chromoendoscopy, using single-pass HD white-light endoscopy as an additional control group.

In a multicentre, randomised controlled trial, IBD patients aged ≥18 years without active disease and scheduled for endoscopic surveillance were included. Patients were 2:2:1 randomised to HD white-light endoscopy with segmental re-inspection of each colonic segment (double pass), HD chromoendoscopy or single-pass HD white-light endoscopy. The primary outcome was colorectal neoplasia detection rate. Assuming equal colorectal neoplasia rates (non-inferiority margin of 10%) between segmental re-inspection and chromoendoscopy and superiority of segmental re-inspection vs single-pass HD white-light endoscopy, a sample size of 566 patients was required.

In total, 563 patients were analysed per-protocol. Colorectal neoplasia detection rates were 10.3% (n=24/234) for HD white-light endoscopy with segmental re-inspection and 13.1% (n=28/214) for HD chromoendoscopy. This confirmed non-inferiority to HD chromoendoscopy (Δ-2.8%, lower limit 95% CI -7.8, p<0.01). In addition, the number of detected colorectal neoplasia per 10 min of withdrawal time was similar between HD white-light endoscopy with segmental re-inspection and HD chromoendoscopy (0.062 vs 0.058, p=0.83). Single-pass HD white-light endoscopy yielded a lower colorectal neoplasia rate (6.1%; n=7/115) than segmental re-inspection but this was not statistically significant (Δ4.1%, 95% CI -2.2:9.6%, p=0.19).

HD white-light endoscopy with segmental re-inspection was non-inferior to HD chromoendoscopy for colorectal neoplasia detection in IBD patients. It can therefore be assumed that the benefit of HD chromoendoscopy may be explained by the longer withdrawal time and not necessarily the enhanced contrast. However, re-inspection per se did not lead to a significantly higher colorectal neoplasia rate than single-pass HD white-light endoscopy alone.

Visual gaze pattern (VGP) analysis quantifies endoscopists' specific eye movements. VGP during colonoscopy may be associated with polyp detection. However, the optimal VGP to maximize detection performance remains unclear. This study evaluated the optimal endoscopic VGP that enabled the highest colorectal adenoma detection rate.

This randomized controlled trial was conducted between July and December 2023. We developed an eye-tracking and feedback (ETF) system that instructed endoscopists to correct their gaze toward the periphery of an endoscope screen with an audible alert. Patients who underwent colonoscopy were randomly assigned to 4 groups: 3 intervention groups, in which the endoscopist's gaze was instructed to a different level of the peripheral screen area using the ETF system (the periphery of 4 × 4, 5 × 5, and 6 × 6 divisions of the screen), and a control group in which the endoscopist did not receive instructions. The primary outcome was the number of adenomas detected per colonoscopy (APC).

In total, 189 patients were enrolled. The APC and adenoma detection rate were significantly higher in the 6 × 6 group than in the control group (1.82 ± 2.41 vs 0.59 ± 1.17, P = .002; 68.9% vs 30.8%, P = .002). The APC and the number of screen divisions were positively correlated (R = 0.985, P = .0152). The rate at which the endoscopist gazed at the periphery of the screen was positively correlated with the number of divisions (R = 0.964, P = .0363).

Colorectal adenoma detection was improved by correcting the endoscopist's gaze to the periphery of the screen, especially by dividing the screen into 6 × 6 segments.

Sessile serrated lesions (SSLs) are associated with an increased risk of colorectal cancer. Data on the prevalence of SSLs in Asia are limited. We performed this study to estimate the prevalence of SSLs in Asia and to explore endoscopic factors that are associated with SSL detection.

This is a post-hoc analysis of a multicenter randomized controlled trial from four Asian countries/regions that compared adenoma detection rates using linked-color imaging (LCI) and white-light imaging. Colonoscopies were performed in an average-risk population for screening, diagnostic examination, or polyp surveillance. Patients with SSLs were compared against those without SSLs to evaluate for possible predictors of SSL detection using Firth's logistic regression.

2898 participants (mean age 64.5 years) were included in the analysis. The estimated prevalence of SSLs was 4.0% (95%CI 3.4%-4.8%), with no sex or age group differences. On multivariable analysis, use of LCI (adjusted odds ratio [aOR] 1.63, 95%CI 1.10-2.41), experienced endoscopists (aOR 1.94, 95%CI 1.25-3.00), use of transparent cap (aOR 1.75, 95%CI 1.09-2.81), and longer withdrawal time (aOR 1.06, 95%CI 1.03-1.10) were independently associated with SSL detection. Synchronous adenoma detection (aOR 1.89, 95%CI 1.20-2.99) was also predictive of SSL detection.

The prevalence of SSLs in Asia is 4.0%. Use of LCI or a transparent cap, greater endoscopist experience, and longer withdrawal time were all associated with increased SSL detection.

The study aims to introduce a novel indicator, effective withdrawal time (WTS), which measures the time spent actively searching for suspicious lesions during colonoscopy and to compare WTS and the conventional withdrawal time (WT).

Colonoscopy video data from 472 patients across two hospitals were retrospectively analyzed. WTS was computed through a combination of artificial intelligence (AI) and manual verification. The results obtained through WTS were compared with those generated by the AI system. Patients were categorized into four groups based on the presence of polyps and whether resections or biopsies were performed. Bland Altman plots were utilized to compare AI-computed WTS with manually verified WTS. Scatterplots were used to illustrate WTS within the four groups, among different hospitals, and across various physicians. A parallel box plot was employed to depict the proportions of WTS relative to WT within each of the four groups.

The study included 472 patients, with a median age of 55 years, and 57.8% were male. A significant correlation with manually verified WTS (r = 0.918) was observed in AI-computed WTS. Significant differences in WTS/WT among the four groups were revealed by the parallel box plot (P < 0.001). The group with no detected polyps had the highest WTS/WT, with a median of 0.69 (interquartile range: 0.40, 0.97). WTS patterns were found to be varied between the two hospitals and among senior and junior physicians.

A promising alternative to traditional WT for quality control and training assessment in colonoscopy is offered by AI-assisted computation of WTS.

The incidence and mortality of colorectal cancer (CRC) have decreased through regular screening colonoscopy, surveillance, and endoscopic treatment. However, CRC can still be diagnosed after negative colonoscopy. Such CRC is called interval CRC and accounts for 1.8-9.0% of all CRC cases. Most cases of interval CRC originate from missed lesions and incompletely resected lesions. Interval CRC can be minimized by improving the quality of colonoscopy. This has led to a growing interest in and demand for high-quality colonoscopy. It is important to reduce the risk of CRC and its associated mortality by improving the quality of colonoscopy. In this review article, we provide an overview of colonoscopy quality indicators, including bowel preparation adequacy, the cecal intubation rate, the adenoma detection rate, the colonoscopy withdrawal time, appropriate polypectomy, and complication of the procedure. Because colonoscopy is a highly endoscopist-dependent procedure, colonoscopists should be well-acquainted with quality indicators and strive to apply them in daily clinical practice for the prevention of CRC.

The metabolic signature identification of colorectal cancer is critical for its early diagnosis and therapeutic approaches that will significantly block cancer progression and improve patient survival. Here, we combined an untargeted metabolic analysis strategy based on internal extractive electrospray ionization mass spectrometry and the machine learning approach to analyze metabolites in 173 pairs of cancer samples and matched normal tissue samples to build robust metabolic signature models for diagnostic purposes. Screening and independent validation of metabolic signatures from colorectal cancers via machine learning methods (Logistic Regression_L1 for feature selection and eXtreme Gradient Boosting for classification) was performed to generate a panel of seven signatures with good diagnostic performance (the accuracy of 87.74%, sensitivity of 85.82%, and specificity of 89.66%). Moreover, seven signatures were evaluated according to their ability to distinguish between cancer and normal tissues, with the metabolic molecule PC (30:0) showing good diagnostic performance. In addition, genes associated with PC (30:0) were identified by multiomics analysis (combining metabolic data with transcriptomic data analysis) and our results showed that PC (30:0) could promote the proliferation of colorectal cancer cell SW480, revealing the correlation between genetic changes and metabolic dysregulation in cancer. Overall, our results reveal potential determinants affecting metabolite dysregulation, paving the way for a mechanistic understanding of altered tissue metabolites in colorectal cancer and design interventions for manipulating the levels of circulating metabolites.

One goal of colorectal cancer (CRC) screening is to prevent CRC incidence by removing precancerous colonic polyps, which are detected in up to 50% of screening examinations. Yet, the lifetime risk of CRC is 3.9%-4.3%, so it is clear that most of these individuals with polyps would not develop CRC in their lifetime. It is, therefore, a challenge to determine which individuals with polyps will benefit from follow-up, and at what intervals. There is some evidence that individuals with advanced polyps, based on size and histology, benefit from intensive surveillance. However, a large proportion of individuals will have small polyps without advanced histologic features (ie, "nonadvanced"), where the benefits of surveillance are uncertain and controversial. Demand for surveillance will further increase as more polyps are detected due to increased screening uptake, recent United States recommendations to expand screening to younger individuals, and emergence of polyp detection technology. We review the current understanding and clinical implications of the natural history, biology, and outcomes associated with various categories of colon polyps based on size, histology, and number. Our aims are to highlight key knowledge gaps, specifically focusing on certain categories of polyps that may not be associated with future CRC risk, and to provide insights to inform research priorities and potential management strategies. Optimization of CRC prevention programs based on updated knowledge about the future risks associated with various colon polyps is essential to ensure cost-effective screening and surveillance, wise use of resources, and inform efforts to personalize recommendations.

We investigated the clinical practice patterns of post-polypectomy colonoscopic surveillance among Korean endoscopists.

In a web-based survey conducted between September and November 2021, participants were asked about their preferred surveillance intervals and the patient age at which surveillance was discontinued. Adherence to the recent guidelines of the U.S. Multi-Society Task Force on Colorectal Cancer (USMSTF) was also analyzed.

In total, 196 endoscopists completed the survey. The most preferred first surveillance intervals were: a 5-year interval after the removal of 1-2 tubular adenomas < 10 mm; a 3-year interval after the removal of 3-10 tubular adenomas < 10 mm, adenomas ≥ 10 mm, tubulovillous or villous adenomas, ≤ 20 hyperplastic polyps < 10 mm, 1-4 sessile serrated lesions (SSLs) < 10 mm, hyperplastic polyps or SSLs ≥ 10 mm, and traditional serrated adenomas; and a 1-year interval after the removal of adenomas with highgrade dysplasia, >10 adenomas, 5-10 SSLs, and SSLs with dysplasia. In piecemeal resections of large polyps ( > 20 mm), surveillance colonoscopy was mostly preferred after 1 year for adenomas and 6 months for SSLs. The mean USMSTF guideline adherence rate was 30.7%. The largest proportion of respondents (40.8%-55.1%) discontinued the surveillance at the patient age of 80-84 years.

A significant discrepancy was observed between the preferred post-polypectomy surveillance intervals and recent international guidelines. Individualized measures are required to increase adherence to the guidelines.

The importance of withdrawal time during colonoscopy cannot be overstated in mitigating the risk of missed lesions and postcolonoscopy colorectal cancer. We evaluated a novel colonoscopy quality metric called the effective withdrawal time (EWT), which is an artificial intelligence (AI)-derived quantitative measure of quality withdrawal time, and its association with various colonic lesion detection rates as compared with standard withdrawal time (SWT).

Three hundred fifty video recordings of colonoscopy withdrawal (from the cecum to the anus) were assessed by the new AI model. The primary outcome was adenoma detection rate (ADR) according to different quintiles of EWT. Multivariate logistic regression, adjusting for baseline covariates, was used to determine the adjusted odd ratios (ORs) for EWT on lesion detection rates, with the lowest quintile as reference. The area under the receiver-operating characteristic curve of EWT was compared with SWT.

The crude ADR in different quintiles of EWT, from lowest to highest, was 10.0%, 31.4%, 33.3%, 53.5%, and 85.7%. The ORs of detecting adenomas and polyps were significantly higher in all top 4 quintiles when compared with the lowest quintile. Each minute increase in EWT was associated with a 49% increase in ADR (aOR, 1.49; 95% confidence interval [CI], 1.36-1.65). The area under the receiver-operating characteristic curve of EWT was also significantly higher than SWT on adenoma detection (.80 [95% CI, .75-.84] vs .70 [95% CI, .64-.74], P < .01).

AI-derived monitoring of EWT is a promising novel quality indicator for colonoscopy, which is more associated with ADR than SWT.

Colonoscopy is widely used as a colorectal cancer (CRC) screening tool. The effectiveness of a screening colonoscopy is associated with a decreased risk of CRC. However, colonoscopy is an operator-dependent procedure, and endoscopists' quality performance varies widely. This article reviewed the priority metrics and practices that contribute to high-quality screening colonoscopy in real-world clinical practice. With growing evidence, quality indicators have been subject to intense research and associated with reducing postcolonoscopy CRC incidence and mortality. Some quality metrics can reflect an endoscopy unit-based practice (i.e. quality of bowel preparation and withdrawal time). Other quality indicators primarily reflect individuals' skill and knowledge (i.e. cecal intubation rate, adenoma detection rate, and appropriately assigned follow-up colonoscopy interval). Measurement and improvement of priority quality indicators for colonoscopy should be made at both the endoscopist and unit levels. Substantial evidence supports the impact of high-quality colonoscopy in reducing the incidence of postcolonoscopy CRC.

The efficacy and safety of colonoscopy performed by artificial intelligence (AI)-assisted novices remain unknown. The aim of this study was to compare the lesion detection capability of novices, AI-assisted novices, and experts.

This multicenter, randomized, noninferiority tandem study was conducted across 3 hospitals in China from May 1, 2022, to November 11, 2022. Eligible patients were randomized into 1 of 3 groups: the CN group (control novice group, withdrawal performed by a novice independently), the AN group (AI-assisted novice group, withdrawal performed by a novice with AI assistance), or the CE group (control expert group, withdrawal performed by an expert independently). Participants underwent a repeat colonoscopy conducted by an AI-assisted expert to evaluate the lesion miss rate and ensure lesion detection. The primary outcome was the adenoma miss rate (AMR).

A total of 685 eligible patients were analyzed: 229 in the CN group, 227 in the AN group, and 229 in the CE group. Both AMR and polyp miss rate were lower in the AN group than in the CN group (18.82% vs 43.69% [P < .001] and 21.23% vs 35.38% [P < .001], respectively). The noninferiority margin was met between the AN and CE groups of both AMR and polyp miss rate (18.82% vs 26.97% [P = .202] and 21.23% vs 24.10% [P < .249]).

AI-assisted colonoscopy lowered the AMR of novices, making them noninferior to experts. The withdrawal technique of new endoscopists can be enhanced by AI-assisted colonoscopy. (Clinical trial registration number: NCT05323279.).

Colonoscopy withdrawal times are associated with the adenoma detection rate (ADR). However, the relationship between ADR and cecal insertion time has been inadequately characterized. We aimed to evaluate endoscopist-related factors involved in the ADR, including the average individual colonoscopy insertion and withdrawal times.

This observational study used a colonoscopy database with pathology data from routine clinical practice in Japanese institutions. The odds ratios (OR) of endoscopist-related factors related to ADRs were examined using a generalized linear mixed model.

Of the 186,293 colonoscopies performed during the study period, 47,705 colonoscopies by 189 endoscopists in four hospitals were analyzed for ADR. The overall ADR was 38.3% (95% confidence interval [CI] 37.8, 38.7). Compared to endoscopists with mean cecal insertion times of <5 min, the OR of ADR for those with mean cecal insertion times of 5-9, 10-14, and ≥15 min were 0.84 (95% CI 0.71, 0.99), 0.68 (95% CI 0.52, 0.90), and 0.45 (95% CI 0.25, 0.78), respectively. Compared to endoscopists with mean withdrawal times of <6 min, the OR of ADR for those with mean withdrawal times of 6-9, 10-14, and ≥15 min were 1.38 (95% CI 1.03, 1.85), 1.48 (95% CI 1.09, 2.02), and 1.68 (95% CI 1.04, 2.61), respectively. There were no significant differences in ADRs by endoscopist specialty, gender, or the total number of examinations performed.

Individual mean colonoscopy insertion time was associated with ADR and might be considered as a colonoscopy quality indicator as well as withdrawal time.

BACKGROUND : Reliable documentation is essential for maintaining quality standards in endoscopy; however, in clinical practice, report quality varies. We developed an artificial intelligence (AI)-based prototype for the measurement of withdrawal and intervention times, and automatic photodocumentation. METHOD:  A multiclass deep learning algorithm distinguishing different endoscopic image content was trained with 10 557 images (1300 examinations, nine centers, four processors). Consecutively, the algorithm was used to calculate withdrawal time (AI prediction) and extract relevant images. Validation was performed on 100 colonoscopy videos (five centers). The reported and AI-predicted withdrawal times were compared with video-based measurement; photodocumentation was compared for documented polypectomies. RESULTS:  Video-based measurement in 100 colonoscopies revealed a median absolute difference of 2.0 minutes between the measured and reported withdrawal times, compared with 0.4 minutes for AI predictions. The original photodocumentation represented the cecum in 88 examinations compared with 98/100 examinations for the AI-generated documentation. For 39/104 polypectomies, the examiners' photographs included the instrument, compared with 68 for the AI images. Lastly, we demonstrated real-time capability (10 colonoscopies). CONCLUSION : Our AI system calculates withdrawal time, provides an image report, and is real-time ready. After further validation, the system may improve standardized reporting, while decreasing the workload created by routine documentation.

Colonoscopy is crucial in preventing colorectal cancer (CRC) and reducing associated mortality. This comprehensive review examines the importance of high-quality colonoscopy and associated quality indicators, including bowel preparation, cecal intubation rate, withdrawal time, adenoma detection rate (ADR), complete resection, specimen retrieval, complication rates, and patient satisfaction, while also discussing other ADR-related metrics. Additionally, the review draws attention to often overlooked quality aspects, such as nonpolypoid lesion detection, as well as insertion and withdrawal skills. Moreover, it explores the potential of artificial intelligence in enhancing colonoscopy quality and highlights specific considerations for organized screening programs. The review also emphasizes the implications of organized screening programs and the need for continuous quality improvement. A high-quality colonoscopy is crucial for preventing postcolonoscopy CRC- and CRC-related deaths. Health-care professionals must develop a thorough understanding of colonoscopy quality components, including technical quality, patient safety, and patient experience. By prioritizing ongoing evaluation and refinement of these quality indicators, health-care providers can contribute to improved patient outcomes and develop more effective CRC screening programs.

Based on published evidence and our expert experience, we provide recommendations to maximize the efficacy, safety, efficiency, and cost-effectiveness of routine colonoscopy. High-quality colonoscopy begins with colon preparation using a split or same-day dose and preferably a low-volume regimen for optimal patient tolerance and compliance. Successful cecal intubation can be achieved by choosing the correct colonoscope and using techniques to facilitate navigation through challenges such as severe angulations and redundant colons. Safety is a primary goal, and complications such as perforation and splenic rupture can be prevented by avoiding pushing through fixed resistance and avoiding loops in proximal colon. Furthermore, barotrauma can be avoided by converting to water filling only (no gas insufflation) in every patient with a narrowed, angulated sigmoid. Optimal polyp detection relies primarily on compulsive attention to inspection as manifested by adequate inspection time, vigorous probing of the spaces between haustral folds, washing and removing residual debris, and achieving full distention. Achieving minimum recommended adenoma detection rate thresholds (30% in men and 20% in women) is mandatory, and colonoscopists should aspire to adenoma detection rate approaching 50% in screening patients. Distal attachments can improve mucosal exposure and increase detection while shortening withdrawal times. Complete resection of polyps complements polyp detection in preventing colorectal cancer. Cold resection is the preferred method for all polyps < 10 mm. For effective cold resection, an adequate rim of normal tissue should be captured in the snare. Finally, cost-effective high-quality colonoscopy requires the procedure not be overused, as demonstrated by following updated United States Multi Society Task Force on Colorectal Cancer postpolypectomy surveillance recommendations.

Adenoma detection rate (ADR) is a quality metric that has been emphasized by multiple societies as improved ADR leads to reduced interval colorectal cancer (CRC). It is postulated that increased withdrawal time (WT) can lead to higher ADR. Multiple randomized controlled trials (RCTs) were undertaken to evaluate this. We performed a systematic review and meta-analysis of RCTs to analyze the impact of higher WT on ADR during colonoscopy.

The following databases were comprehensively searched through November 8, 2022: Embase, MEDLINE, Cochrane, Web of Science, and Google Scholar. Only RCTs were eligible for inclusion. We applied the random effects model using the DerSimonian Laird approach and calculated risk ratio (RR) for binary outcomes and mean difference (MD) for continuous outcomes. 95% CI and P values were generated.

A total of 3 RCTs with 2159 patients were included of which 1136 patients were included in the 9-minute withdrawal group (9WT) and 1023 patients in the 6-minute withdrawal group (6WT). The mean age range was 53.6 to 56.8 years and the male gender was 50.7%. The overall ADR was significantly higher for 9WT (RR=1.23; 95% CI, 1.09-1.40; P <0.001). The overall adenoma per colonoscopy (APC) was also higher for the 9WT group (MD: 0.14; 95% CI, 0.04-0.25; P =0.008).

The 9-minute withdrawal time improved ADR and APC compared with the 6-minute withdrawal. Given the high-quality evidence, we recommend that clinicians at least perform a 9-minute withdrawal to achieve higher quality metrics including ADR to reduce interval CRC.

Background and study aims Upper gastrointestinal (UGI) endoscopy lacks established quality indicators. We conducted an umbrella systematic review of potential quality indicators for the detection of UGI cancer and dysplasia. Methods Bibliographic databases were searched up to December 2021 for systematic reviews and primary studies. Studies reporting diagnostic accuracy, detection rates or the association of endoscopy or endoscopist-related factors with UGI cancer or dysplasia detection were included. AMSTAR2 and JBI checklists were used to assess systematic review and primary study quality. Clinical heterogeneity precluded meta-analysis and findings are summarized narratively. Results Eight systematic reviews and nine primary studies were included. Image enhancement, especially narrow band imaging, had high diagnostic accuracy for dysplasia and early gastric cancer (pooled sensitivity 0.87 (95% CI 0.84-0.89) and specificity 0.97 (0.97-0.98)). Higher detection rates with longer endoscopy examination times were reported in three studies, but no difference was observed in one study. Endoscopist biopsy rate was associated with increased gastric cancer detection (odds ratio 2.5; 95% confidence interval [CI] 2.1-2.9). Early esophageal cancer (0.17% vs 0.14%, P =0.04) and gastric cancer (0.16% vs 0.12%, P =0.02) detection rates were higher with propofol sedation compared to no sedation. Endoscopies performed by trained endoscopists on dedicated Barrett's surveillance lists had higher detection rates (8% vs 3%, P <0.001). The neoplasia detection rate during diagnostic endoscopies for Barrett's esophagus was 7% (95% CI 4%-10%). Conclusions Image enhancement use, longer examination times, biopsy rate and propofol sedation are potential quality indicators for UGI endoscopy. Neoplasia detection rate and dedicated endoscopy lists are additional potential quality indicators for Barrett's esophagus.

High-quality colonoscopy is paramount for colorectal cancer prevention. Since 2009, endoscopists at our institution have received quarterly report cards summarizing individual colonoscopy quality indicators. We have previously shown that implementing this intervention was associated with short-term improvement in adenoma detection rate (ADR). However, the long-term effect of continued monitoring on colonoscopy quality is unclear.

We conducted a retrospective study of prospectively administered quarterly colonoscopy quality report cards at the Roudebush Veteran's Affairs Medical Center between April 1, 2012, and August 31, 2019. The anonymized reports included individual endoscopists' ADRs, cecal intubation rates, and withdrawal times. Analyses were performed to determine slopes over time for each quality metric by physician and assess for differences based on whether ADRs were calculated quarterly or yearly.

Data from the report cards of 17 endoscopists who had performed 24,361 colonoscopies were included. The mean quarterly ADR (±SD) was 51.7% (±11.7%) and mean yearly ADR was 47.2% (±13.8%). There was a small increase in overall ADR based on quarterly and yearly measurements (slope + 0.6%, P = 0.02; and slope +2.7%, P < 0.001, respectively), but no significant change in individual ADRs, cecal intubation rates, or withdrawal times. Analysis of SD of ADRs showed no significant difference between yearly and quarterly measurements ( P = 0.064). Individual endoscopists' ADR SD differences between yearly and quarterly measurements ranged from -4.7% to +6.8%.

Long-term colonoscopy quality monitoring paralleled stable improvements in overall ADR. For endoscopists with baseline high ADR, frequent monitoring and reporting of colonoscopy quality metrics may not be necessary.

Artificial intelligence shows promise for clinical research in inflammatory bowel disease endoscopy. Accurate assessment of endoscopic activity is important in clinical practice and inflammatory bowel disease clinical trials. Emerging artificial intelligence technologies can increase efficiency and accuracy of assessing the baseline endoscopic appearance in patients with inflammatory bowel disease and the impact that therapeutic interventions may have on mucosal healing in both of these contexts. In this review, state-of-the-art endoscopic assessment of mucosal disease activity in inflammatory bowel disease clinical trials is described, covering the potential for artificial intelligence to transform the current paradigm, its limitations, and suggested next steps. Site-based artificial intelligence quality evaluation and inclusion of patients in clinical trials without the need for a central reader is proposed; for following patient progress, a second reading using AI alongside a central reader with expedited reading is proposed. Artificial intelligence will support precision endoscopy in inflammatory bowel disease and is on the threshold of advancing inflammatory bowel disease clinical trial recruitment.

The introduction of widespread colonoscopy screening programs has helped in decreasing the incidence of Colorectal Cancer (CRC). However, 'back-to-back' colonoscopies revealed relevant percentage of missed adenomas. Quality indicators were created to further homogenize detection performances and decrease the incidence of post-colonoscopy CRC. Among them, the Adenoma Detection Rate (ADR), defined as the percentage obtained by dividing the number of endoscopic procedures in which at least one adenoma was resected, by the total number of procedures, was found to be inversely associated with the risks of interval colorectal cancer, advanced-stage interval cancer, and fatal interval cancer.

In this paper, we performed a comprehensive review of the literature focusing on promising new devices and technologies, which are meant to positively affect the endoscopist performance in detecting adenomas, therefore increasing ADR.

Considering the current knowledge, although several devices and technologies have been proposed with this intent, the recent implementation of AI ranked over all of the other strategies and it is likely to become the new standard within few years. However, the combination of different device/technologies need to be investigated in the future aiming at even further increasing of endoscopist detection performances.

There are limited data regarding the morphology, histopathology, and anatomical distribution of sporadic colorectal polyps in Chinese patients. We evaluated these characteristics of sporadic polyps to guide the endoscopic detection and excision of colorectal polyps.

This was a retrospective single-center observational study involving 7381 patients with sporadic colorectal polyps. All polyps were removed endoscopically. The morphology and histopathology of polyps were evaluated according to the Paris classification and the World Health Organization classification, respectively.

A total of 22,174 polyps removed endoscopically from 7322 patients were included. In the sigmoid colon, 24.70% of colorectal polyps occurred, followed by the transverse colon (18.58%) in frequency. 0-Is type polyps accounted for 60.60% of all sporadic colorectal polyps. Polyps with 0-Ip, 0-Isp, and 0-IIa types were frequently found in the sigmoid colon, but laterally spreading lesions usually occurred in the ascending colon (24.61%) and rectum (20.51%). Irrespective of the Paris classification and anatomical location, as the polyps enlarge, the proportion of adenomatous polyps gradually increases while the proportion of serrated lesions decreases. Polyps with size ≥1 cm located in the left-sided colon were more likely to have villous/tubulovillous or high-grade dysplasia histology than those located in the right-sided colon, and about 1% of them were demonstrated with adenocarcinoma.

Sigmoid colon should be detected adequately during colonoscopy, and polyps with size ≥1 cm should be treated carefully, especially in the left-sided colon.

Meticulous inspection of the mucosa during colonoscopy, represents a lengthier withdrawal time, but has been shown to increase adenoma detection rate (ADR). We investigated if artificial intelligence-aided speed monitoring can improve suboptimal withdrawal time.

We evaluated the implementation of a computer-aided speed monitoring device during colonoscopy at a large academic endoscopy center. After informed consent, patients ≥18 years undergoing colonoscopy between 5 March and 29 April 2021 were examined without the use of the speedometer, and with the speedometer between 29 April and 30 June 2021. All colonoscopies were recorded, and withdrawal time was assessed based on the recordings in a blinded fashion. We compared mean withdrawal time, percentage of withdrawal time ≥6 min, and ADR with and without the speedometer.

One hundred sixty-six patients in each group were eligible for analyses. Mean withdrawal time was 9 min and 6.6 s (95% CI: 8 min and 34.8 s to 9 min and 39 s) without the use of the speedometer, and 9 min and 9 s (95% CI: 8 min and 45 s to 9 min and 33.6 s) with the speedometer; difference 2.3 s (95% CI: -42.3-37.7, p = 0.91). The ADRs were 45.2% (95% CI: 37.6-52.8) without the speedometer as compared to 45.8% (95% CI: 38.2-53.4) with the speedometer (p = 0.91). The proportion of colonoscopies with withdrawal time ≥6 min without the speedometer was 85.5% (95% CI: 80.2-90.9) versus 86.7% (95% CI: 81.6-91.9) with the speedometer (p = 0.75).

Use of speed monitoring during withdrawal did not increase withdrawal time or ADR in colonoscopy.

NCT04710251.

In order to reduce the incidence and mortality of colorectal cancer, improving the quality of colonoscopy is the top priority. At present, the adenoma detection rate is the most used index to evaluate the quality of colonoscopy. So, we further verified the relevant factors influencing the quality of colonoscopy and found out the novel quality indicators by studying the relationship between the influencing factors and the adenoma detection rate.

The study included 3824 cases of colonoscopy from January to December 2020. We retrospectively recorded the age and sex of the subjects; the number, size, and histological features of lesions; withdrawal time and the number of images acquired during colonoscopy. We analyzed the associated factors affecting adenoma and polyp detection, and verified their effectiveness with both univariate and multivariate logistic regression analyses.

Logistic regression analyses showed that gender, age, withdrawal time and the number of images acquired during colonoscopy could serve as independent predictors of adenoma/polyp detection rate. In addition, adenoma detection rate (25.36% vs. 14.29%) and polyp detection rate (53.99% vs. 34.42%) showed a marked increase when the number of images taken during colonoscopy was ≥29 (P<0.001).

Gender, age, withdrawal time and the number of images acquired during colonoscopy are influencing factors for the detection of colorectal adenomas and polyps. And we can gain higher adenoma/polyp detection rate when endoscopists capture more colonoscopic images.

Growing evidence indicates that artificial intelligence (AI) applied to medicine is here to stay. In gastroenterology, AI computer vision applications have been stated as a research priority. The two main AI system categories are computer-aided polyp detection (CADe) and computer-assisted diagnosis (CADx). However, other fields of expansion are those related to colonoscopy quality, such as methods to objectively assess colon cleansing during the colonoscopy, as well as devices to automatically predict and improve bowel cleansing before the examination, predict deep submucosal invasion, obtain a reliable measurement of colorectal polyps and accurately locate colorectal lesions in the colon. Although growing evidence indicates that AI systems could improve some of these quality metrics, there are concerns regarding cost-effectiveness, and large and multicentric randomized studies with strong outcomes, such as post-colonoscopy colorectal cancer incidence and mortality, are lacking. The integration of all these tasks into one quality-improvement device could facilitate the incorporation of AI systems in clinical practice. In this manuscript, the current status of the role of AI in colonoscopy is reviewed, as well as its current applications, drawbacks and areas for improvement.

The aim was to investigate the adenoma detection rate (ADR) of endoscopists who have used full-spectrum endoscopy (Fuse) for 3 years and revert back to traditional forward-viewing endoscopes (R-TFV) at an ambulatory surgical center.

Traditional forward viewing (TFV) endoscopes have 1 camera and provide an angle of view of 140 to 170 degrees, whereas Fuse provides a 330 degrees view through the addition of 2 side cameras. It has previously been reported that Fuse increased the ADR by 5.4% when compared with previous rates using TFV. Fuse is no longer commercially available. The ADR of endoscopists who revert back to TFV is unknown.

We conducted a retrospective analysis of data examining the ADR from average risk screening colonoscopies at a 5-room ambulatory surgical center where endoscopists transitioned from TFV to Fuse in April 2014 and then reverted back to TFV in 2016. The primary outcome was ADR. Secondary outcomes were ADR for advanced and right-sided adenomas.

A total of 6110 procedures were reviewed. The ADR was 23.70% for TFV, 29.02% for Fuse and 28.88% for R-TFV. The ADR for advanced adenomas was 3.8% for TFV, 6.0% for Fuse and 7.3% for R-TFV. The ADR for right-sided adenomas was 13.0% for TFV, 16.7% for Fuse and 16.0% for R-TFV. The results for all 3 categories showed a statistical difference between TFV and Fuse as well as between TFV and R-TFV. There were no statistical differences between the ADR of Fuse compared with R-TFV.

During R-TFV, endoscopists are able to maintain their increased ability to detect adenomas. This would suggest that there was a change in behavior in endoscopists using Fuse that was durable.

Colorectal cancer (CRC) is one of the common malignant tumors in the world. Colonoscopy is the crucial examination technique in CRC screening programs for the early detection of precursor lesions, and treatment of early colorectal cancer, which can reduce the morbidity and mortality of CRC significantly. However, pooled polyp miss rates during colonoscopic examination are as high as 22%. Artificial intelligence (AI) provides a promising way to improve the colonoscopic adenoma detection rate (ADR). It might assist endoscopists in avoiding missing polyps and offer an accurate optical diagnosis of suspected lesions. Herein, we described some of the milestone studies in using AI for colonoscopy, and the future application directions of AI in improving colonoscopic ADR.

Performing a high-quality colonoscopy is critical for optimizing the adenoma detection rate (ADR). Colonoscopy withdrawal time (a surrogate measure) of ≥6 minutes is recommended; however, a threshold of a high-quality withdrawal and its impact on ADR are not known.

We examined withdrawal time (excluding polyp resection and bowel cleaning time) of subjects undergoing screening and/or surveillance colonoscopy in a prospective, multicenter, randomized controlled trial. We examined the relationship of withdrawal time in 1-minute increments on ADR and reported odds ratio (OR) with 95% confidence intervals. Linear regression analysis was performed to assess the maximal inspection time threshold that impacts the ADR.

A total of 1142 subjects (age, 62.3 ± 8.9 years; 80.5% men) underwent screening (45.9%) or surveillance (53.6%) colonoscopy. The screening group had a median withdrawal time of 9.0 minutes (interquartile range [IQR], 3.3) with an ADR of 49.6%, whereas the surveillance group had a median withdrawal time of 9.3 minutes (IQR, 4.3) with an ADR of 63.9%. ADR correspondingly increased for a withdrawal time of 6 minutes to 13 minutes, beyond which ADR did not increase (50.4% vs 76.6%, P < .01). For every 1-minute increase in withdrawal time, there was 6% higher odds of detecting an additional subject with an adenoma (OR, 1.06; 95% confidence interval, 1.02-1.10; P = .004).

Results from this multicenter, randomized controlled trial underscore the importance of a high-quality examination and efforts required to achieve this with an incremental yield in ADR based on withdrawal time. (Clinical trial registration number: NCT03952611.).

Withdrawal time of sufficient length is a quality indicator for colonoscopies. Nonetheless, whether extending the withdrawal time contributes to patient discomfort remains unknown. This study aimed to clarify the relationship between colonoscopy withdrawal time and patient discomfort.

A cohort of consecutive patients who underwent colonoscopy at a single institution from October 2018 to January 2020 was retrospectively analyzed. Initially, the relationship between the mean withdrawal time for each colonoscopist in no-finding examinations and polyp detection rate was investigated in 2,043 patients. Subsequently, the primary outcome of association between withdrawal time and patient discomfort, as determined by patient questionnaire, was assessed for each examination in 481 patients from the initial cohort.

The mean withdrawal time was strongly correlated with polyp detection rate (correlation coefficient, 0.72; P<0.001). In contrast, longer withdrawal time was not associated with increased discomfort; however, there was a weak inverse correlation between patient discomfort and longer withdrawal time (correlation coefficient, -0.25; P<0.001). Similarly, multiple regression analysis adjusted for confounding variables revealed that longer withdrawal time was not associated with increased patient discomfort (regression coefficient, -0.04 for each 1-minute increase in the length of withdrawal time; P=0.45).

This study showed for the first time that longer withdrawal times did not result in increased discomfort, indicating that withdrawal time can be extended to sufficient length for optimal patient examination and polyp detection.

Background and study aims High-quality is crucial for the effectiveness of colonoscopy and can be achieved by high-quality training and verified with assessment of key performance indicators (KPIs) for colonoscopy such as cecum intubation rate (CIR), adenoma detection rate (ADR) and adequate polyp resection. Typically, trainees achieve adequate CIR after 275 procedures, but little is known about learning curves for KPIs after initial training. Methods This cross-sectional study includes work-up colonoscopies after a positive screening test with fecal occult blood testing (FIT) or sigmoidoscopy, performed by either trainees after 300 training colonoscopies or by consultants. Outcome measures were KPIs. We assessed inter-endoscopist variation in trainees and learning curves for trainees as a group. We also compared KPIs for trainees and consultants as a group.  Results Data from 6,655 colonoscopies performed by 21 trainees and 921 colonoscopies performed by 17 consultants were included. Most trainees achieved target standards for main KPIs. With time, trainees shortened cecum intubation time and withdrawal time without decreasing their ADR, reduced the proportion of painful colonoscopies, and increased the adequate polyp resection rate (all P  < 0.01). Compared to consultants, trainees had higher CIR (97.7 % vs. 96.3 %, P  = 0.02), ADR after positive FIT (57.6 % vs. 50.3 %, P  < 0.01), and proximal ADR after sigmoidoscopy screening (41.1 % vs. 29.8 %; P  < 0.01), higher adequate polyp resection rate (94.9 % vs. 93.1 %, P  = 0.01) and fewer serious adverse events (0.65 % vs. 1.41 %, P  = 0.02). Conclusions Trainees performed high-quality colonoscopies and achieved international target standards. Several KPIs continuously improved after initial training. Trainees outperformed consultants on several KPIs.

Colonoscopy is a valuable tool for preventing and reducing the incidence and mortality of colorectal cancer. Although several computer-aided colorectal polyp detection and diagnosis systems have been proposed for clinical application, many remain susceptible to interference problems, including low image clarity, unevenness, and low accuracy for the analysis of dynamic images; these drawbacks affect the robustness and practicality of these systems. This study proposed an intraprocedure alert system for colonoscopy examination developed on the basis of deep learning. The proposed system features blurred image detection, foreign body detection, and polyp detection modules facilitated by convolutional neural networks. The training and validation datasets included high-quality images and low-quality images, including blurred images and those containing folds, fecal matter, and opaque water. For the detection of blurred images and images containing folds, fecal matter, and opaque water, the accuracy rate was 96.2%. Furthermore, the study results indicated a per-polyp detection accuracy of 100% when the system was applied to video images. The recall rates for high-quality image frames and polyp image frames were 95.7% and 92%, respectively. The overall alert accuracy rate and the false-positive rate of low quality for video images obtained through per-frame analysis were 95.3% and 0.18%, respectively. The proposed system can be used to alert colonoscopists to the need to slow their procedural speed or to perform flush or lumen inflation in cases where the colonoscope is being moved too rapidly, where fecal residue is present in the intestinal tract, or where the colon has been inadequately distended.