Studies aimed to evaluate the expected impact of alternative screening strategies are essential for optimizing colorectal cancer (CRC) screening offers, but such studies are lacking in Germany, where two screening colonoscopies (CS) 10 years apart are offered for men from age 50 and women from age 55. Our aim was to explore whether and to what extent the efficacy of utilizing two CS could be enhanced by alternative starting ages and screening intervals. We modeled the expected numbers of CRC cases, CRC deaths, years of potential life lost (YPLL), and disability-adjusted life years (DALYs) due to CRC in hypothetical cohorts of 100,000 men and women aged 45-85 using COSIMO, a validated Markov-based multi-state simulation model. Modeled strategies included combinations of starting ages (45/50/55/60) and CS (10/15/20 years). For men, CRC deaths could be slightly reduced by extending the interval to 15 years, with a second CS at 65. YPLL and DALYs would be reduced by decreasing starting age to 45 when combined with a 15-year screening interval. For women, use of two CS at ages 50 and 65 would reduce all CRC burden parameters compared to the current earliest-use offer at 55 and 65 years. Our results suggest that lowering the starting age of screening colonoscopy to 45 for men and 50 for women, combined with extending the CS screening interval to 15 years would have the potential to enable significant reductions in years of potential life lost, and disability-adjusted life years compared to current screening offers in Germany.

Serrated polyps can progress to colorectal cancer (CRC), through a pathway that is distinct from the conventional adenoma-carcinoma sequence. This pathway includes hyperplastic polyps (HPs), sessile serrated polyps (SSPs), and traditional serrated adenomas (TSAs).

Our review includes the histology and pathological challenges, carcinogenesis, risk factors, detection, emerging technologies, resection, and surveillance.

Serrated polyp management presents many detection, diagnosis, resection, and surveillance challenges. Missed serrated polyps contribute to preventable CRCs. A new SSP detection rate benchmark will guide endoscopists with a goal when improving detection. Furthermore, new SSP-specific surveillance strategies may also aid in reducing CRC burden. Histologic differentiation remains a challenge, underscoring the need for standardized pathology practices and exploring novel ways to stratify risk independent of histology, given interobserver variation. Moreover, the clinical significance of proximal HPs requires further clarification. Which HPs < 1 cm require closer surveillance intervals? Molecular profiling may help identify markers that separate proximal low risk from high-risk HP. The best approach for resection of serrated polyps also needs to be clarified. There is also a lack of robust longitudinal outcome data to guide surveillance recommendations since current guidelines are based on low quality of evidence.

Colonoscopy is an essential diagnostic and therapeutic tool in gastroenterology, significantly impacting colorectal cancer (CRC) detection and management. Effective bowel preparation is critical for optimal visualization, directly influencing colonoscopy accuracy and patient outcomes. However, diabetic patients frequently encounter challenges achieving adequate bowel preparation, primarily due to gastroparesis, autonomic neuropathy, altered colonic motility, fluid-electrolyte imbalances, and complexities related to antihyperglycemic medication adjustments. This review aims to evaluate the current literature on bowel preparation efficacy in diabetic patients undergoing colonoscopy, assess existing guidelines from leading gastroenterological societies, and highlight the necessity for detailed, diabetes-specific recommendations. We conducted a comprehensive PubMed search identifying 20 pertinent studies, including randomized controlled trials, meta-analyses, multicenter studies, cohort studies, and reviews. The findings consistently indicate diabetes as an independent predictor of inadequate bowel preparation. Furthermore, an evaluation of guidelines from the European Society of Gastrointestinal Endoscopy (ESGE), the US Multi-Society Task Force, and the Canadian Association of Gastroenterology revealed either absent or insufficiently detailed diabetes-specific recommendations. Given the rising global prevalence of diabetes and CRC, inadequate bowel preparation significantly impacts the quality of colonoscopy, adenoma detection rates, patient safety, and healthcare costs. This review underscores the urgent need for additional research focusing on tailored bowel preparation strategies for diabetic patients. Ultimately, the implementation of standardized, evidence-based protocols designed explicitly for this high-risk group is essential to enhance diagnostic efficacy, improve patient outcomes, and reduce CRC-related morbidity and mortality.

With the assistance of ENDOANGEL, a study was conducted at Hainan General Hospital to evaluate the effect of artificial intelligence (AI) system on the detection of gastrointestinal precancerous lesions.

The prospective, randomized, pretest and post-test, single-center clinical trial compared the detection rates of gastric precancerous lesions and intestinal adenomas between baseline and post-intervention phase among traditional digestive endoscopy (control groups i and ii, and experimental group i) and AI-assisted endoscopy (experimental group ii). Additionally, the effect of AI on the detection rate of different seniority physicians was analyzed.

AI assistance significantly increased the detection rates of intestinal metaplasia (experimental group ii vs control group ii: 14.23 % vs 9.15 %, P = 0.013), atrophy (experimental group ii vs control group ii: 22.76 % vs 17.28 %, P = 0.031) and intestinal adenomas (experimental group ii vs control group ii: 48.52 % vs 24.58 %, P < 0.001). The improvement was particularly notable among junior doctors, with significant enhancements in the detection rates of intestinal metaplasia (experimental group ii vs control group ii: 14.39 % vs 9.09 %, P = 0.008), atrophy (experimental group ii vs control group ii: 22.04 % vs 15.31 %, P = 0.004), and intestinal adenomas (experimental group ii vs control group ii: 45.18 % vs 29.27 %, P = 0.002).

AI systems have the potential to significantly improve the detection rates of precancerous conditions, particularly among less experienced endoscopists. This advancement can lead to more accurate and appropriate follow-up and review strategies for patients, ultimately reducing the risk of missed early cancer diagnoses.

Accurate judgment and identification of polyp size is crucial in endoscopic diagnosis. However, the indistinct boundaries of polyps lead to missegmentation and missed cancer diagnoses. In this paper, a prompt-based polyp segmentation method (PPSM) is proposed to assist in early-stage cancer diagnosis during endoscopy. It combines endoscopists' experience and artificial intelligence technology. Firstly, a prompt-based polyp segmentation network (PPSN) is presented, which contains the prompt encoding module (PEM), the feature extraction encoding module (FEEM), and the mask decoding module (MDM). The PEM encodes prompts to guide the FEEM for feature extracting and the MDM for mask generating. So that PPSN can segment polyps efficiently. Secondly, endoscopists' ocular attention data (gazes) are used as prompts, which can enhance PPSN's accuracy for segmenting polyps and obtain prompt data effectively in real-world. To reinforce the PPSN's stability, non-uniform dot matrix prompts are generated to compensate for frame loss during the eye-tracking. Moreover, a data augmentation method based on the segment anything model (SAM) is introduced to enrich the prompt dataset and improve the PPSN's adaptability. Experiments demonstrate the PPSM's accuracy and real-time capability. The results from cross-training and cross-testing on four datasets show the PPSM's generalization. Based on the research results, a disposable electronic endoscope with the real-time auxiliary diagnosis function for early cancer and an image processor have been developed. Part of the code and the method for generating the prompts dataset are available at https://github.com/XinZhenRen/PPSM.

Adenoma detection rate (ADR) is a key quality metric in colonoscopy, reflecting the ability to detect adenomas. However, concerns remain regarding the robustness of ADR as a benchmark. In particular, the "one and done" phenomenon may exist in which physicians are less motivated to find additional adenomas after discovery of the first adenoma. To investigate this topic further, our goal was to understand the relationship between single and multiple adenoma detection.

A retrospective cohort study was conducted by using data from 89,587 screening colonoscopies performed by 32 endoscopists within the Sutter Health-Palo Alto Medical Foundation between 2015 and 2020. ADR, single adenoma detection rate (ADR1), and multiple adenoma detection rate (ADR2+) were analyzed by using multivariate logistic regression and linear regression models. Endoscopists were then fit into 4 categories based on median ADR1 and ADR2+ (low ADR1, high ADR2+ ["all or none"], low ADR1, low ADR2+ ["none and done"], high ADR1, high ADR2+ ["all and done"], or high ADR1, low ADR2+ ["one and done"]).

The overall ADR was 45.4%. ADR, ADR1, and ADR2+ were significantly associated with similar factors, including older age, male sex, higher body mass index, smoking status, high-risk colonoscopies, Medicare insured, use of mucosal assist devices, longer withdrawal times, adequate preparation, and procedures performed by high-volume, female endoscopists with a longer duration in practice. The median ADR1 and ADR2+ were 23.95% and 21.29%, respectively. ADR1 and ADR2+ were positively correlated (Pearson correlation coefficient [r], .701; P < .001). Only 4 of 32 endoscopists fit our "one and done" category.

These findings suggest that ADR1 correlates with ADR2+. Despite concerns, the "one and done" phenomenon is not commonly seen in clinical practice. ADR remains a good surrogate marker for multiple adenoma detection.

Colonoscopy remains the most commonly used colorectal cancer screening test in the United States. A substantial portion of the screening population value the high sensitivity of colonoscopy for precancerous colorectal lesions of all sizes, which allows it to be performed at 10 year intervals in average-risk persons with negative examinations. Emerging evidence supports the eventual endorsement of 15 year intervals for patients with normal examinations. Considerable evidence supports an impact of colonoscopy on colorectal cancer incidence and mortality, including a randomized controlled trial of colonoscopy vs. no screening, numerous case-control and cohort studies, an impact of fecal blood testing on cancer incidence, and the impact of one randomized controlled trial of flexible sigmoidoscopy on proximal colon cancer incidence. Colonoscopy is the gold standard for detection of colorectal precancerous lesions, and continues to evolve with regard to sensitivity for precancerous lesions and the effectiveness and safety of precancerous lesion resection. Gains in detection of precancerous lesions have followed from a robust movement to improve colonoscopy quality, and development of non-device techniques such as patient rotation during withdrawal, water exchange colonoscopy, and double examination of one or more colonic segments. Further, development of devices to improve mucosal exposure during withdrawal (e.g. Endocuff Vision, distal cap attachment, and Computer-Aided Quality), and devices that highlight flat lesions (e.g. chromoendoscopy, electronic chromoendoscopy, and Computer-Aided Detection) have created opportunities to achieve very high levels of detection and thereby increase the protective benefits of colonoscopy. Further, colonoscopy resection safety has improved via the widespread use of cold resection for lesions < 10 mm in size, as well as sessile serrated lesions of all sizes. Colonoscopy can be offered to patients as one of multiple options for screening, or as the test of choice for patients with the highest pre-screening probability of cancer and precancerous lesions, or as the first test offered followed by offers of other screening tests if colonoscopy is declined (sequential offers of screening). Sequential offers of screening result in overall adherence to screening similar to offering multiple options, but with a higher fraction of patients undergoing colonoscopy. Given the long-lasting protective effects of colonoscopy and its improving effectiveness and safety, colonoscopy remains a useful option for primary average-risk colorectal cancer screening.

PurposeThe 2023 American College of Physicians (ACP) guidelines for colorectal cancer (CRC) screening are at odds with the United States Preventive Task Force (USPSTF) guidelines, with the former recommending screening starting at age 50 y and the latter at age 45 y. This article "stress tests" CRC colonoscopy screening strategies to investigate their robustness to uncertainties stemming from the natural history of disease and sensitivity of colonoscopy.MethodsThis study uses the CRC-SPIN microsimulation model to project the life-years gained (LYG) under several colonoscopy CRC screening strategies. The model was extended to include birth cohort effects on adenoma risk. We estimated natural history parameters under 2 different assumptions about the youngest age of adenoma initiation. For each, we generated 500 parameter sets to reflect uncertainty in the natural history parameters. We simulated 26 colonoscopy screening strategies and examined 4 different colonoscopy sensitivity assumptions, encompassing the range of sensitivities consistent with prior tandem colonoscopy studies. Across this set of scenarios, we identify efficient screening strategies and report posterior credible intervals for benefits of screening (LYG), burden (number of colonoscopies), and incremental burden-effectiveness ratios.ResultsProjected absolute screening benefits varied widely based on assumptions, but strategies starting at age 45 y were consistently in the efficiency frontier. Strategies in which screening starts at age 50 y with 10-y intervals were never efficient, saving fewer life-years than starting screening at age 45 y and performing colonoscopies every 15 y while requiring more colonoscopies per person.ConclusionsDecennial colonoscopy screening initiation at age 45 y remained a robust recommendation. Colonoscopy screening with a 10-y interval starting at age 50 y did not result in an efficient use of colonoscopies in any of the scenarios evaluated.HighlightsColorectal cancer colonoscopy screening strategies initiated at age 45 y were projected to yield more life-years gained while requiring the least number of colonoscopies across different model assumptions about disease natural history and colonoscopy sensitivity.Colonoscopy screening starting at age 50 y with a 10-y interval consistently underperformed strategies that started at age 45 y.

Over the past 5 decades, artificial intelligence (AI) has evolved rapidly. Moving from basic models to advanced machine learning and deep learning systems, the impact of AI on various fields, including medicine, has been profound. In gastroenterology, AI-driven computer-aided detection and computer-aided diagnosis systems have revolutionized endoscopy, imaging, and pathology detection. The future promises further advancements in diagnostic precision, personalized treatment, and clinical research. However, challenges such as transparency, liability, and ethical concerns must be addressed. By fostering collaboration, robust governance and development of quality metrics, AI can be leveraged to enhance patient care and advance scientific knowledge.

Artificial intelligence (AI) is transforming gastroenterology, particularly in endoscopy, which has a direct impact on trainees and their education. AI can serve as a valuable resource, providing real-time feedback and aiding in tasks like polyp detection and lesion differentiation, which are challenging for trainees. However, its implementation raises concerns about cognitive overload, overreliance, and even access disparities, which could affect training outcomes. Beyond endoscopy, AI shows promise in clinical management and interpreting diagnostic studies such as motility testing. Thoughtful adoption of AI can optimize training and prepare future trainees for the modern healthcare landscape.

The adenoma detection rate is a key colonoscopy quality indicator, but the adenoma miss rate (AMR) is more strongly linked to post-colonoscopy colorectal cancer risk. While studies examined high-definition colonoscopy and AMR, no studies have assessed its impact on consecutive polypectomy.

This study aimed to identify adenomas missed in screening or surveillance colonoscopy and determine if the endoscopic system affects the miss rate.

This retrospective study analyzed patients referred to Dong-A University Hospital for polypectomy after polyps were detected during screening or surveillance colonoscopy at 24 healthcare institutions. Endoscopic systems used in these colonoscopies were classified as FHD (FHD) or non-FHD. Consecutive polypectomies were performed by a single expert between March 2020 and February 2022 using the FHD system. The AMR was compared and analyzed for screening or surveillance colonoscopies performed using FHD endoscopic systems and those using non-FHD endoscopic systems.

Of 542 polyps, 186 were missed (miss rate: 25.22%). Miss rates for adenoma and advanced neoplasia were 27.34% and 14.69%. Univariate analysis identified age, adenoma count, and endoscopic system as significant factors. However, only the endoscopic system remained significant in the multivariate analysis. In screening or surveillance colonoscopy, the use of FHD endoscopic systems demonstrated a lower AMR compared to non-FHD systems (21.86% vs. 31.41%, P = 0.014).

The use of FHD endoscopic systems reduced AMR compared to non-FHD systems.

The broad use of artificial intelligence (AI) and its various applications have already shown significant impact in medicine and in everyday life. In gastroenterology, the most studied AI tools at present are computer-aided detection (CADe) and computer-aided diagnosis (CADx). These tools have been mainly assessed during colonoscopy for the detection of polyps and for the prediction of their histology based on their appearance. Their use aims to improve colonoscopy quality, standardize procedures, and potentially reduce costs. Data on CADe demonstrate clear benefits that are applicable to clinical practice. While CADx shows good diagnostic performance, its additional benefits in assisting endoscopists remain unclear.

This American Gastroenterological Association (AGA) guideline is intended to provide an overview of the evidence and support endoscopists and patients on the use of computer-aided detection (CADe) systems for the detection of colorectal polyps during colonoscopy.

A multidisciplinary panel of content experts and guideline methodologists used the Grading of Recommendations Assessment, Development and Evaluation framework and relied on the following sources of evidence: (1) a systematic review examining the desirable and undesirable effects (ie, benefits and harms) of CADe-assisted colonoscopy, (2) a microsimulation study estimating the effects of CADe on longer-term patient-important outcomes, (3) a systematic search of evidence evaluating the values and preferences of patients undergoing colonoscopy, and (4) a systematic review of studies evaluating health care providers' trust in artificial intelligence technology in gastroenterology.

The panel reached the conclusion that no recommendation could be made for or against the use of CADe-assisted colonoscopy in light of very low certainty of evidence for the critical outcomes, desirable and undesirable (11 fewer colorectal cancers per 10,000 individuals and 2 fewer colorectal cancer deaths per 10,000 individuals), increased burden of more intensive surveillance colonoscopies (635 more per 10,000 individuals), and cost and resource implications. The panel acknowledged the 8% (95% CI, 6%-10%) increase in adenoma detection rate and 2% (95% CI, 0%-4%) increase in advanced adenoma and/or sessile serrated lesion detection rate.

This guideline highlights the close tradeoff between desirable and undesirable effects and the limitations in the current evidence to support a recommendation. The panel acknowledged the potential for CADe to continually improve as an iterative artificial intelligence application. Ongoing publications providing evidence for critical outcomes will help inform a future recommendation.

A newly launched endoscopy system (EVIS X1, CV-1500; Olympus) is equipped with texture and color enhancement imaging (TXI). We aimed to investigate the efficacy of TXI for the visibility and diagnostic accuracy of non-polypoid colorectal lesions.

We examined 100 non-polypoid lesions in 42 patients from the same position, angle, and distance of the view in three modes: white light imaging (WLI), narrow-band imaging (NBI), and TXI. The primary outcome was to compare polyp visibility in the three modes using subjective polyp visibility score and objective color difference values. The secondary outcome was to compare the diagnostic accuracy without magnification.

Overall, the visibility score of TXI was significantly higher than that of WLI (3.7 ± 1.1 vs. 3.6 ± 1.1; p = 0.008) and lower than that of NBI (3.7 ± 1.1 vs. 3.8 ± 1.1; p = 0.013). Color difference values of TXI were higher than those of WLI (11.5 ± 6.9 vs. 9.1 ± 5.4; p < 0.001) and lower than those of NBI (11.5 ± 6.9 vs. 13.1 ± 7.7; p = 0.002). No significant differences in TXI and NBI (visibility score: 3.7 ± 1.0 vs. 3.8 ± 1.1; p = 0.833, color difference values: 11.6 ± 7.1 vs. 12.9 ± 8.3; p = 0.099) were observed for neoplastic lesions. Moreover, the diagnostic accuracy of TXI was significantly higher than that of NBI (65.5% vs. 57.6%, p = 0.012) for neoplastic lesions.

TXI demonstrated higher visibility than that of WLI and lower than that of NBI. Further investigations are warranted to validate the performance of the TXI mode comprehensively.

Computer-aided detection software (CADe) has shown promising results in real-time polyp detection, but a limited head-to-head comparison of the available CADe systems has been performed. Moreover, such systems have not been compared to endoscopists using standardized videos. This study aims to compare the performance of three CADe systems in detecting polyps, employing a novel standardized methodology.

Videos from 300 colonoscopies conducted at Oslo University Hospital were analyzed. Short video clips (20-45 s) presenting normal mucosa or polyps were randomly selected. These videos were then streamed through each CADe system from Medtronic, Olympus, and Augere Medical. Each system featured diverse configurations, resulting in a total of six software settings. Sensitivity and false positivity (FP) were assessed by comparing the CADe systems to both the mean of the systems and pairwise between them. Furthermore, the systems' performance was compared to the performance of five endoscopists.

CADe systems' sensitivity ranged between 84.9% and 98.7%, with statistically significant differences observed between the systems, both in comparison to the mean and to each other. FP rates ranged between 1.2% and 5.6%, also differing statistically significantly between the systems. The CADe systems achieving the highest sensitivity also exhibited the highest FP. Statistically significant differences in the alert delay were observed between different CADe systems and endoscopists.

This study highlights significant differences between commercially available CADe software regarding sensitivity and FP, but a superior performance compared to endoscopists. The software with the highest sensitivity also exhibited the highest FP, highlighting the need for further refinement.

The aim of this study was to assess the cost-effectiveness of an intelligent endoscopy module for computer-assisted detection and characterization (CADe/CADx) compared with standard practice, from a Spanish National Health System perspective.

A Markov model was designed to estimate total costs, life years gained (LYG), and quality-adjusted life years (QALYs) over a lifetime horizon with annual cycles. A hypothetical cohort of 1,000 patients eligible for colonoscopy (mean age 61.32 years) was distributed between Markov states according to polyp size, location, and histology based on national screening program data. CADe/CADx efficacy was determined based on adenoma miss rates and natural disease evolution was simulated according to annual transition probabilities. Detected polyp management involved polypectomy and histopathology in standard practice, whereas with CADe/CADx leave-in-situ strategy was applied for ≤ 5 mm rectosigmoid non-adenomas and resect-and-discard strategy for the rest of ≤ 5mm polyps. Unit costs (€,2024) included the diagnostic procedure and polyp and colorectal cancer (CRC) management. A 3% annual discount rate was applied to costs and outcomes. Model inputs were validated by an expert panel.

CADe/CADx was more effective (16.37 LYG and 14.32 QALYs) than standard practice (16.33 LYG and 14.27 QALYs) over a lifetime horizon. Total cost per patient was €2,300.76 with CADe/CADx and €2,508.75 with colonoscopy alone. In a hypothetical cohort of 1,000 patients, CADe/CADx avoided 173 polypectomies, 370 histopathologies, and 7 CRC cases. Sensitivity analyses confirmed model robustness.

The results of this analysis suggest that CADe/CADx would result in a dominant strategy versus standard practice in patients undergoing colonoscopy in Spain.

Aggressive colorectal cancer (CRC) frequently originates from serrated lesions (SLs), particularly in the proximal colon, which are challenging to detect using standard screening colonoscopy. Although duplicate examinations or chromocolonoscopies are recommended for detecting proximal SLs, evidence from randomized trials is limited. We evaluated the effectiveness of tandem colonoscopy with an acetic acid-indigo carmine mixture (AIM) for detecting SLs in the proximal colon compared with white-light imaging (WLI) and indigo carmine (IC).

This 3-arm, multicenter, randomized controlled trial involving 9 institutions enrolled patients undergoing colonoscopy and assigned them randomly to the WLI, IC, or AIM group. The primary outcomes were the SL-detection rate (SDR) of proximal lesions during the second examination (SDR 2nd ) and SL additional rate (SAR). Secondary outcomes included the detection and additional rates of other polyps, factors contributing to SAR, and complications.

Between 2021 and 2024, 1,319 participants with 1,267 polyps were included in the analysis. With AIM, the SDR 2nd and SAR were significantly higher compared with WLI or IC (WLI vs AIM: 2.7% vs 14.0%, P < 0.001; IC vs AIM: 7.9% vs 14.0%, P = 0.002, and WLI vs AIM: 22.4% vs 69.3%, P < 0.001; IC vs AIM: 45.8% vs 69.3%, P = 0.001). AIM conferred a higher adenoma detection rate 2nd than with WLI (10.5% vs 24.7%; P < 0.001) and was an independent factor for SAR (odds ratio [95% confidence interval]: 7.79 [3.76-17.08]). No major adverse events were observed.

AIM significantly improved proximal colon SDRs and outperformed WLI and IC. The relationship between SDR and CRC incidence warrants further investigation.

White light (conventional) colonoscopy (WLC) is widely used for colorectal cancer screening, diagnosis and surveillance but endoscopists may fail to detect adenomas. Our goal was to assess and synthesize overall and subgroup-specific adenoma miss rates (AMR) of WLC in daily practice.

We conducted a systematic review in MEDLINE, EMBASE, Cochrane Library, and grey literature on studies evaluating diagnostic WLC accuracy in tandem studies with novel-colonoscopic technologies (NCT) in subjects undergoing screening, diagnostic or surveillance colonoscopy. Information on study design, AMR overall and specific for adenoma size, histology, location, morphology and further outcomes were extracted and reported in standardized evidence tables. Study quality was assessed using the QUADAS-2 tool. Random-effects meta-analyses and meta-regression were performed to estimate pooled estimates for AMR with 95% confidence intervals (95% CI) and to explain heterogeneity.

Out of 5,963 identified studies, we included sixteen studies with 4,101 individuals in our meta-analysis. One in three adenomas (34%; 95% CI: 30-38%) was missed by WLC in daily practice individuals. Subgroup analyses showed significant AMR differences by size (36%, adenomas 1-5 mm; 27%, adenomas 6-9 mm; 12%, adenomas ≥ 10 mm), histology (non-advanced: 42%, advanced: 21%), morphology (flat: 50%, polypoid: 27%), but not by location (distal: 36%, proximal: 36%).

Based on our meta-analysis, one in three adenomas could be missed by WLC. This may significantly contribute to interval cancers. Our results should be considered in health technology assessment when interpreting sensitivity of fecal occult blood or other screening tests derived from studies using WLC as "gold standard".

Poor bowel preparation has been a great problem of endoscopists for a long time. Low-residue diet (LRD) is often recommended on the day before colonoscopy. However, the quality of bowel preparation with LRD alone is suboptimal. The aim of our study was to examine whether patients who consumed LRD with oral nutritional supplements (ONSs) had higher quality of bowel preparation than patients who consumed LRD alone before colonoscopy.

An endoscopist-blinded, randomized controlled trial was conducted in inpatients who scheduled for colonoscopy with sedation. Patients were randomized to the non-ONS group and the ONS group. The primary outcomes were the rates of adequate bowel preparation measured by the Boston Bowel Preparation Scale (BBPS), the Ottawa Bowel Preparation Scale (OBPS) and the Colon Endoscopic Bubble Scale (CEBuS). The secondary outcomes were the adenoma detection rate (ADR), polyp detection rate (PDR), intubation time, withdrawal time and rates of adverse events.

In both groups, 100 patients were analyzed. The rates of adequate bowel preparation measured by the BBPS and OBPS and PDR were significantly higher in the ONS group than in the non-ONS group (all P < 0.05). However, no significant differences were observed in the CEBuS scores, ADR, intubation time and rates of adverse events between the two groups (all P > 0.05). The withdrawal time was longer in the non-ONS group than the in the ONS group (P < 0.05).

LRD with ONSs helps increase the quality of bowel preparation. To help with colorectal cancer (CRC) screening, further studies are warrant.

[Chinese Clinical Trial Registry], [ChiCTR2400091660], [31/10/2024], [Retrospectively registered].

Colonoscopy, a crucial procedure for detecting and removing colorectal polyps, has seen transformative advancements through the integration of artificial intelligence, specifically in computer-aided detection (CADe) and diagnosis (CADx). These tools enhance real-time detection and characterization of lesions, potentially reducing human error, and standardizing the quality of colonoscopy across endoscopists. CADe has proven effective in increasing adenoma detection rate, potentially reducing long-term colorectal cancer incidence. However, CADe's benefits are accompanied by challenges, such as potentially longer procedure times, increased non-neoplastic polyp resections, and a higher surveillance burden. CADx, although promising in differentiating neoplastic and non-neoplastic diminutive polyps, encounters limitations in accuracy, particularly in the proximal colon. Real-world data also revealed gaps between trial efficacy and practical outcomes, emphasizing the need for further research in uncontrolled settings. Moreover, CADx limited specificity and binary output underscore the necessity for explainable artificial intelligence to gain endoscopists' trust. This review aimed to explore the benefits, harms, and limitations of artificial intelligence for colon cancer screening, surveillance, and treatment focusing on CADe and CADx systems for lesion detection and characterization, respectively, while addressing challenges in integrating these technologies into clinical practice.

Several recent studies have found that the efficacy of computer-aided polyp detection (CADe) on the adenoma detection rate (ADR) diminished in real-world settings. The role of unmeasured factors in AI-human interaction, such as monitor approaches, remains unknown. This study aimed to validate the effectiveness of CADe in the real world and assess the impact of monitor approaches.

A retrospective propensity score-matched cohort study was conducted using routine data from a tertiary endoscopy center in China before and after the implementation of CADe. Four propensity score-matched cohorts were established: Cohort 1: pre-CADe matched with dual-monitor CADe-assisted group; Cohort 2: dual-monitor CADe-assisted with single-monitor CADe-assisted group; Cohort 3: pre-CADe with single-monitor CADe-assisted group; and Cohort 4: pre-CADe with CADe period. ADR was set as the primary outcome.

There were 5390, 6083, and 6131 eligible patients in the pre-CADe group, dual-monitor group, and single-monitor group, respectively. In the matched analysis, results indicated that regardless of the monitor setup, CADe-assisted groups showed a trend of increased ADR compared with the pre-CADe period (CADe period: OR 1.141, 95% CI 1.047-1.243; p = 0.003; dual-monitor: OR 1.178, 95% CI 1.069-1.299, p = 0.001; single-monitor: OR 1.094, 95% CI 0.998-1.200, p = 0.056). Moreover, no significant difference between different monitor approaches was observed, although dual-monitor setup showed an increasing tendency on ADR compared with single-monitor setup (OR 1.069, 95% CI 0.985-1.161, p = 0.109).

CADe shows great potential to improve ADR during colonoscopy in the real world. Meanwhile, changes in monitor setup do not significantly impact the assistance capability of CADe. Further research dedicated to evaluating the unmeasured elements in the AI-clinician hybrid for better implementation of CADe would be beneficial.

Background and Study Aim: Colonoscopy holds the highest volume of all endoscopic procedures, allowing for large colonoscopy databases to serve as valuable datasets for quality assurance. We aimed to build a comprehensive colonoscopy database for quality assurance and the training of future AIs. Materials and Methods: As part of a cluster-randomized controlled trial, a designated, onsite medical student was used to acquire procedural and patient-specific data, ensuring a high level of data integrity. The following data were thereby collected for all colonoscopies: full colonoscopy vides, colonoscope position (XYZ-coordinates), intraprocedural timestamps, pathological report, endoscopist description, endoscopist planning, and patient-reported discomfort. Results: A total of 1447 patients were included from the 1st of February 2022 to the 21st of November 2023; 1191 colonoscopies were registered as completed, 88 were stopped due to inadequate bowel cleansing, and 41 were stopped due to patient discomfort. Of the 1191 completed colonoscopies, 601 contained polypectomies (50.4%), and 590 did not (49.6%). Comparing colonoscopies with polypectomies to those without the withdrawal time (caecum to extubating the scope) was significantly longer for all parts of the colon (p values < 0.001), except the transverse colon (p value = 0.92). The database was used to train an AI, automatically and objectively evaluating bowel preparation. Conclusions: We established the most thorough database in colonoscopy with previously inaccessible information, indicating that the transverse colon differs from the other parts of the colon in terms of withdrawal time for procedures with polypectomies. To further explore these findings and reach the full potential of the database, an AI evaluating bowel preparation was developed. Several research partners have been identified to collaborate in the development of future AIs.

Colorectal cancer (CRC) prevention depends on effective colonoscopy; yet variability in adenoma detection rates (ADRs) and missed lesions remain significant hurdles. Artificial intelligence-powered computer-aided detection (CADe) systems offer promising advancements in enhancing polyp detection. This review examines the role of CADe in improving ADR and reducing adenoma miss rates (AMRs) while addressing its broader clinical implications. CADe has demonstrated consistent improvements in ADRs and AMRs; largely by detecting diminutive polyps, but shows limited efficacy in identifying advanced adenomas or sessile serrated lesions. Challenges such as operator deskilling and the need for enhanced algorithms persist. Combining CADe with adjunctive techniques has shown potential for further optimizing performance. While CADe has standardized detection quality; its long-term impact on CRC incidence and mortality remains inconclusive. Future research should focus on refining CADe technology and assessing its effectiveness in reducing the global burden of CRC.

Adenoma detection rate (ADR) is a key performance measure with variability among endoscopists. Artificial intelligence (AI) in colonoscopy could reduce this variability and has shown to improve ADR. This study assessed the impact of AI on ADR among Danish endoscopists of varying experience levels.

We conducted a prospective, quasi-randomized, controlled, multicenter trial involving patients aged 18 and older undergoing screening, surveillance, and diagnostic colonoscopy at four centers. Participants were assigned to AI-assisted colonoscopy (GI Genius, Medtronic) or conventional colonoscopy. Endoscopists were classified as experts (> 1000 colonoscopies) or non-experts (≤ 1000 colonoscopies). The primary outcome was ADR. We performed a subgroup analysis stratified on endoscopist experience and a subset analysis of the screening population.

A total of 795 patients were analyzed: 400 in the AI group and 395 in the control group. The AI group demonstrated a significantly higher ADR than the control group (59.1% vs. 46.6%, P < 0.001). The increase was significant among experts (59.9% vs. 47.3%, P < 0.002) but not among non-experts. AI assistance significantly improved ADR (74.4% vs. 58.1%, P = 0.003) in screening colonoscopies. Polyp detection rate (PDR) was also higher in the AI group (69.8% vs. 56.2%, P < 0.001). There was no significant difference in the non-neoplastic resection rate (NNRR) (15.1% vs. 17.1%, P = 0.542).

AI-assisted colonoscopy significantly increased ADR by 12.5% overall, with a notable 16.3% increase in the screening population. The unchanged NNRR indicates that the higher PDR was due to increased ADR, not unnecessary resections.

Background: PolyDeep is a computer-aided detection and characterization system that has demonstrated a high diagnostic yield for in vitro detection of colorectal polyps. Our objective is to compare the diagnostic performance of expert endoscopists and PolyDeep for colorectal polyp detection. Methods: PolyDeep Advance 1 (NCT05514301) is an unicentric diagnostic test study with a second observer design. Endoscopists performed colonoscopy blinded to PolyDeep's detection results. The main endpoint was the sensitivity for colorectal polyp (adenoma, serrated or hyperplastic lesion) detection. The secondary endpoints were the diagnostic performance for diminutive lesions (≤5 mm), neoplasia (adenoma, serrated lesion) and adenoma detection. Results: We included 205 patients (55.1% male, 63.0 ± 6.2 years of age) referred to colonoscopy (positive faecal immunochemical occult blood test = 60.5%, surveillance colonoscopy = 39.5%). We excluded eight patients due to incomplete colonoscopy. Endoscopists detected 384 lesions, of which 39 were not detected by PolyDeep. In contrast, PolyDeep predicted 410 possible additional lesions, 26 of these predictions confirmed by endoscopists as lesions, resulting in a potential 6.8% detection increase with respect to the 384 lesions detected by the endoscopists. In total, 410 lesions were detected, 20 were not retrieved, five were colorectal adenocarcinoma, 343 were colorectal polyps (231 adenomas, 39 serrated and 73 hyperplastic polyps), 42 were normal mucosa and 289 were ≤5 mm. We did not find statistically significant differences between endoscopists and PolyDeep for colorectal polyp detection (Sensitivity = 94.2%, 91.5%, p = 0.2; Specificity = 9.5%, 14.3%, p = 0.7), diminutive lesions (Sensitivity = 92.3%, 89.5%, p = 0.4; Specificity = 9.8%, 14.6%, p = 0.7), neoplasia (Sensitivity = 95.2%, 92.9%, p = 0.3; Specificity = 9.6%, 13.9%, p = 0.4) and adenoma detection (Sensitivity = 94.4%, 92.6%, p = 0.5; Specificity = 7.2%, 11.8%, p = 0.2). Conclusions: Expert endoscopists and PolyDeep have similar diagnostic performance for colorectal polyp detection.

Computer-aided detection (CADe) systems may increase adenoma detection rate (ADR) during colonoscopy. However, the variable results of CADe effects in different RCTs warrant investigation into factors influencing these results.

Investigate the different variables possibly affecting the impact of CADe-assisted colonoscopy and its effect on ADR.

We searched MEDLINE, EMBASE, and Scopus databases until July 2023 for RCTs reporting performance of CADe systems in the detection of colorectal neoplasia. The main outcome was pooled ADR. A random-effects meta-analysis was performed to obtain the pooled risk ratios (RR) with 95 % confidence intervals (CI)). To explore sources of heterogeneity, we conducted a meta-regression analysis using both univariable and multivariable mixed-effects models. Potential explanatory variables included factors influencing adenoma prevalence, such as patient gender, age, and colonoscopy indication. We also included both key (ADR), and minor (Withdrawal time) performance measures considered as quality indicators for colonoscopy.

Twenty-three randomized controlled trials (RCTs) on 19,077 patients were include. ADR was higher in the CADe group (46 % [95 % CI 39-52]) than in the standard colonoscopy group (38 % [95 % CI 31-46]) with a risk ratio of 1.22 [95 % CI 1.14-1.29]); and a substantial level of heterogeneity (I2 = 67.69 %). In the univariable meta-regression analysis, patient age, ADR in control arms, and withdrawal time were the strongest predictors of CADe effect on ADR (P < .001). In multivariable meta-regression, ADR in control arms, and withdrawal time were simultaneous significant predictors of the proportion of the CADe effect on ADR.

The substantial level of heterogeneity found appeared to be associated with variability in colonoscopy quality performances across the studies, namely ADR in control arm, and withdrawal time.

The remarkable recent developments in image-enhanced endoscopy (IEE) have significantly contributed to the advancement of diagnostic techniques. Linked color imaging (LCI) is an IEE technique in which color differences are expanded by processing image data to enhance short-wavelength narrow-band light. This feature of LCI causes reddish areas to appear redder and whitish areas to appear whiter. Because most colorectal lesions, such as neoplastic and inflammatory lesions, have a reddish tone, LCI is an effective tool for identifying colorectal lesions by clarifying the redder areas and distinguishing them from the surrounding normal mucosa. To date, eight randomized controlled trials have been conducted to evaluate the effectiveness of LCI in identifying colorectal adenomatous lesions. The results of a meta-analysis integrating these studies demonstrated that LCI was superior to white-light endoscopy for detecting colorectal adenomatous lesions. LCI also improves the detection of serrated lesions by enhancing their whiteness. Furthermore, accumulating evidence suggests that LCI is superior to white-light endoscopy for the diagnosis of the colonic mucosa in patients with ulcerative colitis. In this review, based on a comprehensive search of the current literature since the implementation of LCI, the utility of LCI in the detection and diagnosis of colorectal lesions is discussed. Additionally, the latest data, including attempts to combine artificial intelligence and LCI, are presented.

The detection rate of proximal sessile serrated lesion (PSSLDR) is linked to the incidence and mortality of colorectal cancer. However, research on second forward view (SFV) examinations for PSSLDR remains limited. This first randomized controlled trial assessed the impact of the proximal SFV on the PSSLDR.

Patients were randomized into 2 groups during proximal colonoscopy: standard colonoscopy (SC) and SFV. The SC group underwent a standard examination, whereas the SFV group underwent a second examination of the proximal colon (cecum to splenic flexure). The primary outcome was PSSLDR, with secondary outcomes, including the proximal polyp detection rate (PPDR), proximal adenoma detection rate (PADR), and lesion miss rate, compared between the 2 groups.

Among 246 patients (SC = 124; SFV = 122), SFV significantly improved the PSSLDR by 7.4% compared with SC (9.8% vs 2.4%, P = 0.017). SFV increased the PPDR by 20.2% (55.7% vs 35.5%, P = 0.002) and PADR by 12.7% (37.7% vs 25%, P = 0.039). Multivariate analysis revealed that sessile serrated lesions (odds ratio [OR] = 7.70, 95% confidence interval [CI] [1.58, 37.59]), inflammatory polyps (OR = 4.24, 95% CI [1.73, 10.39]), and lesion size (OR = 0.76, 95% CI [0.60, 0.96]) were associated with proximal missed lesions. The overall polyp miss rate was 52.9%, with miss rates of 61.0% for polyps <5 mm, 80% for sessile serrated lesions, and 42.2% for adenomas. Furthermore, 12.3% of patients experienced changes in surveillance intervals from SFV examination.

SFV examination of the proximal colon significantly improved the PSSLDR by 7.4%, PPDR by 20.2%, and PADR by 12.7%, while shortening the detection interval by 12.3%, making it a valuable and cost-effective addition to routine colonoscopy.

Colorectal cancer (CRC) is the third most prevalent cancer globally and poses a significant health threat, making early detection crucial. This review paper explored emerging detection methods for early screening of CRC, including gut microbiota, metabolites, genetic markers, and artificial intelligence (AI)-based technologies. Current screening methods have their respective advantages and limitations, particularly in detecting precursors. First, the importance of the gut microbiome in CRC progression is discussed, highlighting how specific microbial alterations can serve as biomarkers for early detection, potentially enhancing diagnostic accuracy when combined with traditional screening methods. Next, research on metabolic reprogramming illustrates the relationship between metabolic changes and CRC, with studies developing metabolite-based detection models that show good sensitivity for early diagnosis. In terms of genetic markers, methylated DNA markers like SEPTIN9 have demonstrated high sensitivity, although further validation across diverse populations is necessary. Lastly, AI technology has shown immense potential in improving adenoma detection rates, significantly enhancing the quality of colonoscopic examinations through image recognition techniques. This review aims to provide a comprehensive perspective on new strategies for CRC screening, emphasizing the potential of noninvasive detection technologies and the prospects of AI and genomics in clinical applications. Despite several challenges, this review advocates for future large-scale prospective studies to validate the effectiveness and cost-effectiveness of these new screening methods while promoting the implementation of screening protocols tailored to individual characteristics.

Studies have suggested that computer-aided polyp detection using artificial intelligence improves adenoma identification during colonoscopy. However, its real-world effectiveness remains unclear. Therefore, this study evaluated the usefulness of computer-aided detection during regular surveillance colonoscopy.

Consecutive patients who underwent surveillance colonoscopy with computer-aided detection between January and March 2023 and had undergone colonoscopy at least twice during the past 3 years were recruited. The clinicopathological findings of lesions identified using computer-aided detection were evaluated. The detection ability was sub-analyzed based on the expertise of the endoscopist and the presence of diminutive adenomas (size ≤5 mm).

A total of 78 patients were included. Computer-aided detection identified 46 adenomas in 28 patients; however, no carcinomas were identified. The mean withdrawal time was 824 ± 353 s, and the mean tumor diameter was 3.3 mm (range, 2-8 mm). The most common gross type was 0-Is (70%), followed by 0-Isp (17%) and 0-IIa (13%). The most common tumor locations were the ascending colon and sigmoid colon (28%), followed by the transverse colon (26%), cecum (7%), descending colon (7%), and rectum (4%). Overall, 34.1% and 38.2% of patients with untreated diminutive adenomas and those with no adenomas, respectively, had newly detected adenomas. Endoscopist expertise did not affect the results.

Computer-aided detection may help identify adenomas during surveillance colonoscopy for patients with untreated diminutive adenomas and those with a history of endoscopic resection.

Texture and color enhancement imaging (TXI) is a novel optical technology designed to improve visibility during endoscopy by highlighting subtle differences in morphology and color. This systematic review and meta-analysis aimed to determine whether TXI, compared with conventional white light imaging (WLI), can improve important colonoscopy quality indicators, specifically the adenoma detection rate (ADR) and adenomas per colonoscopy (APC).

We searched PubMed, EMBASE, and the Cochrane Central for studies comparing TXI to WLI in patients undergoing colonoscopy for any indication. Risk ratios (RRs) and mean differences (MDs) were computed using a random-effects model.

We included 1541 patients from three studies, of which two were randomized controlled trials (RCTs). TXI was used in 775 patients (50.3%). Indications for colonoscopy varied, including positive fecal immunochemical test (FIT), surveillance, and diagnostic workup for abdominal symptoms. In the pooled data, TXI significantly increased both ADR (57,8% versus 43.6%; RR 1.32; 95% confidence interval [CI] 1.20-1.46; P < 0.001; I 2 = 0%) and APC (MD 0.50; 95% CI 0.37-0.64; P < 0.001; I 2 = 0%), compared with WLI. Furthermore, TXI was more effective at detecting nonpolypoid/flat adenomas, proximal/right-sided adenomas, and adenomas ≥ 10 mm in size. Colonoscopies with TXI had shorter withdrawal times.

Our meta-analysis demonstrates that TXI significantly improves detection of colorectal adenomas in patients undergoing colonoscopy for various indications. TXI has the potential to improve overall quality of colonoscopy and contribute to colorectal cancer prevention.

One-fourth of colorectal neoplasia is missed at screening colonoscopy, representing the leading cause of interval colorectal cancer (I-CRC). This systematic review and meta-analysis summarizes the efficacy of computer-aided colonoscopy (CAC) compared to white-light colonoscopy (WLC) in reducing lesion miss rates.

Major databases were systematically searched through May 2024 for tandem-design RCTs comparing lesion miss rates in CAC-first followed by WLC vs WLC-first followed by CAC. The primary outcomes were adenoma miss rate (AMR) and polyp miss rate (PMR). The secondary outcomes were advanced AMR (aAMR) and sessile serrated lesion miss rate (SMR).

Six RCTs (1718 patients) were included. AMR was significantly lower for CAC compared to WLC (RR = 0.46; 95 %CI [0.38-0.55]; P < 0.001). PMR was also lower for CAC compared to WLC (RR = 0.44; 95 %CI [0.33-0.60]; P < 0.001). No significant difference in aAMR (RR = 1.28; 95 %CI [0.34-4.83]; P = 0.71) and SMR (RR = 0.44; 95 %CI [0.15-1.28]; P = 0.13) were observed. Sensitivity analysis including only RCTs performed in CRC screening and surveillance setting confirmed lower AMR (RR = 0.48; 95 %CI [0.39-0.58]; P < 0.001) and PMR (RR = 0.50; 95 %CI [0.37-0.66]; P < 0.001), also showing significantly lower SMR (RR = 0.28; 95 %CI [0.11-0.70]; P = 0.007) for CAC compared to WLC.

CAC results in significantly lower AMR and PMR compared to WLC overall, and significantly lower AMR, PMR and SMR in the screening/surveillance setting, potentially reducing the incidence of I-CRC.

The real-world effectiveness of computer-aided detection (CADe) systems during colonoscopies remains uncertain. We assessed the effectiveness of the novel CADe system, ENdoscopy as AI-powered Device (ENAD), in enhancing the adenoma detection rate (ADR) and other quality indicators in real-world clinical practice.

We enrolled patients who underwent elective colonoscopies between May 2022 and October 2022 at a tertiary healthcare center. Standard colonoscopy (SC) was compared to ENAD-assisted colonoscopy. Eight experienced endoscopists performed the procedures in randomly assigned CADe- and non-CADe-assisted rooms. The primary outcome was a comparison of ADR between the ENAD and SC groups.

A total of 1,758 sex- and age-matched patients were included and evenly distributed into two groups. The ENAD group had a significantly higher ADR (45.1% vs. 38.8%, p=0.010), higher sessile serrated lesion detection rate (SSLDR) (5.7% vs. 2.5%, p=0.001), higher mean number of adenomas per colonoscopy (APC) (0.78±1.17 vs. 0.61±0.99; incidence risk ratio, 1.27; 95% confidence interval, 1.13-1.42), and longer withdrawal time (9.0±3.4 vs. 8.3±3.1, p<0.001) than the SC group. However, the mean withdrawal times were not significantly different between the two groups in cases where no polyps were detected (6.9±1.7 vs. 6.7±1.7, p=0.058).

ENAD-assisted colonoscopy significantly improved the ADR, APC, and SSLDR in real-world clinical practice, particularly for smaller and nonpolypoid adenomas.

Non-invasive biomarkers may reduce post-colonoscopy colorectal cancer (CRC) rates and colonoscopy overuse in Lynch syndrome. Unlike faecal immunochemical test (FIT), faecal volatile organic compounds (VOCs) may accurately detect both advanced and non-advanced colorectal neoplasia.

The aim of this study was to evaluate the potential of faecal VOCs-separately and with FIT-to guide optimal colonoscopy intervals in Lynch syndrome.

Prospective longitudinal multicentre study in which individuals with Lynch syndrome collected faeces before and after high-quality surveillance colonoscopy. VOC-patterns were analysed using field asymmetric ion mobility spectrometry (FAIMS) and gas chromatography-ion mobility spectrometry (GC-IMS) followed by machine learning pipelines, and combined with FIT at 2.55 μg Hb/g faeces. Gas chromatography time-of-flight mass spectrometry analysed individual VOC abundance.

Among 200 included individuals (57% female, median 51 years), 62 had relevant neoplasia at colonoscopy: 3 CRC, 6 advanced adenoma (AA), 3 advanced serrated lesion (ASL), and 50 non-advanced adenoma (NAA). Respective sensitivity and negative predictive value for CRC and AA (and also ASL in case of FAIMS) were 100% and 100% using FAIMS (54% specificity), and 89% and 99% using GC-IMS (58% specificity). Respective sensitivity and specificity for any relevant neoplasia were 88% and 44% (FAIMS) and 84% and 28% (GC-IMS); accuracy did not significantly improve upon VOC-FIT. VOC-patterns differed before and after polypectomy (AUC 0.70). NAA showed decreased faecal abundance of butanal, 2-oxohexane, dimethyldisulphide and dimethyltrisulphide.

In Lynch syndrome, faecal VOCs may be a promising strategy for postponing colonoscopy and for follow-up after polypectomy. Our results serve as a stepping stone for large validation studies.

NL8749.