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.

Colonoscopy is the gold standard for detecting and resecting adenomas and early-stage cancers to reduce colorectal cancer (CRC) incidence and mortality rates. This study aimed to confirm the superiority of texture and color enhancement imaging (TXI) over white light imaging (WLI) in detecting colorectal lesions.

This randomized controlled trial was conducted at 8 Japanese institutions between March 2023 and October 2023. Participants aged 40 to 80 years old scheduled for CRC screening and nonscreening purposes, such as postpolypectomy surveillance, positive fecal occult blood test results, and abdominal symptoms, were included. We used only the latest model colonoscopes and performed observations in each arm of the TXI model and WLI. The primary end point was the mean number of adenomas detected per procedure. Secondary end points included the adenoma detection rate, polyp detection rate, flat polyp detection rate, and adverse events.

A total of 956 patients were enrolled and randomized. After patients who did not meet the eligibility criteria were excluded, 451 and 445 patients were included in the TXI and WLI arms, respectively. The mean number of adenomas detected per procedure was 1.4 and 1.5 and the adenoma detection rate was 57.2% and 56.0% in TXI and WLI, respectively, and there were no statistically significant differences between 2 arms. The polyp detection rate and flat polyp detection rate were significantly higher in TXI than in WLI, which were 82.5% vs 74.4% (P = .003), and 76.5% vs 70.3% (P = .036), respectively.

This study did not demonstrate the superiority of TXI over WLI in detecting neoplastic lesions. However, TXI may be effective in detecting flat polyps.

jRCT1032230089.

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.

Colorectal cancer prevention relies on surveillance colonoscopy, with the adenoma detection rate as a key factor in examination quality. Linked color imaging (LCI) enhances lesion contrast and improves the examination performance. This systematic review and meta-analysis aimed to evaluate the effect of LCI on adenoma detection rate in adults who underwent colonoscopy.

We searched the Medline, PubMed, BIREME, LILACS, and Scientific Electronic Library Online databases for randomized controlled trials comparing the use of LCI versus white light imaging (WLI), published up to March 2023. The outcomes included lesion characteristics, number of adenomas per patient, and the additional polyp detection rate.

Sixteen studies were included in the analysis, which showed that LCI was more accurate than WLI in detecting adenomas, with an increased number of adenomas detected per patient. Although LCI performed well in terms of lesion size, morphology, and location, the subgroup analyses did not reveal any statistically significant differences between LCI and WLI. The addition of LCI did not result in significant improvements in the detection of serrated lesions, and there were no differences in the withdrawal time between groups.

LCI has been shown to be effective in detecting colonic lesions, improving the number of adenomas detected per patient and improving polyp detection rate without negatively affecting other quality criteria in colonoscopy.

Diagnostic multiparametric MRI of the prostate has steadily evolved over the last three decades and can now reliably depict the dominant tumor in most men with prostate cancer. In response, several methods of targeted biopsy to direct tissue sampling of suspected tumor foci seen at multiparametric MRI have been developed and successfully tested in recent years, including software-assisted MRI-ultrasound (US) fusion biopsy and direct MRI-guided in-bore biopsy. These advances are leading to a sea change in the approach to prostate cancer diagnosis, with the traditional approach of blind systematic biopsy increasingly being replaced by MRI directed targeted biopsy. This review aims to describe the current status of targeted biopsy, with an emphasis on the relative accuracy of different techniques. The results of several critical large multicenter trials are presented, while unanswered questions that require more research are highlighted.

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.

Recent advancements in artificial intelligence (AI) have significantly impacted the field of gastrointestinal (GI) endoscopy, with applications spanning a wide range of clinical indications. The central goals for AI in GI endoscopy are to improve endoscopic procedural performance and quality assessment, optimize patient outcomes, and reduce administrative burden. Despite early progress, such as Food and Drug Administration approval of the first computer-aided polyp detection system in 2021, there are numerous important challenges to be faced on the path toward broader adoption of AI algorithms in clinical endoscopic practice.

Background: Colon capsule endoscopy (CCE) is becoming more widely available across Europe, but its uptake is slow due to the need for follow-up colonoscopy for therapeutic procedures and biopsies, which impacts its cost-effectiveness. One of the major factors driving the conversion to colonoscopy is the detection of excess polyps in CCE that cannot be matched during subsequent colonoscopy. The capsule's rocking motion, which can lead to duplicate reporting of the same polyp when viewed from different angles, is likely a key contributor. Objectives: This review aims to explore the types of polyp matching reported in the literature, assess matching techniques and matching accuracy, and evaluate the development of machine learning models to improve polyp matching in CCE and subsequent colonoscopy. Methods: A systematic literature search was conducted in EMBASE, MEDLINE, and PubMed. Due to the scarcity of research in this area, the search encompassed clinical trials, observational studies, reviews, case series, and editorial letters. Three directly related studies were included, and ten indirectly related studies were included for review. Results: Polyp matching in colon capsule endoscopy still needs to be developed, with only one study focused on creating criteria to match polyps within the same CCE video. Another study established that experienced CCE readers have greater accuracy, reducing interobserver variability. A machine learning algorithm was developed in one study to match polyps between initial CCE and subsequent colonoscopy. Only around 50% of polyps were successfully matched, requiring further optimisation. As Artificial Intelligence (AI) algorithms advance in CCE polyp detection, the risk of duplicate reporting may increase when clinicians are presented with polyp images or timestamps, potentially complicating the transition to AI-assisted CCE reading in the future. Conclusions: Polyp matching in CCE is a developing field with considerable challenges, especially in matching polyps within the same video. Although AI shows potential for decent accuracy, more research is needed to refine these techniques and make CCE a more reliable, non-invasive alternative to complement conventional colonoscopy for lower GI investigations.

Postcolonoscopy colorectal cancers primarily occur in the right-sided colon because of missed adenomas and serrated polyps (SPs). Water exchange (WE) improves cleanliness and visibility of the right-sided colon. We hypothesized that WE could reduce the right-sided colon adenoma (rAMR) and SP miss rate (rSPMR) compared with standard colonoscopy.

We randomly assigned 386 colonoscopy patients to insertion with either WE or CO 2 insufflation. During the first withdrawal, polypectomies were performed up to the hepatic flexure. A second endoscopist, blinded to the insertion technique, re-examined the right-sided colon. The miss rate was determined by dividing the number of additional adenomas or SPs by the total number detected in both examinations. The primary outcome was the combined rAMR and rSPMR.

WE significantly decreased the combined rAMR and rSPMR (22.2% vs 32.2%, P < 0.001) and rSPMR alone (22.5% vs 37.1%, P = 0.002) compared with CO 2 insufflation, but not rAMR (21.8% vs 29.8%, P = 0.079). In addition, WE significantly increased the detection of SP per colonoscopy (SP per colonoscopy) in the right-sided colon (0.95 ± 1.56 vs 0.50 ± 0.79, P < 0.001). Multivariate logistic regression analysis showed that ≥2 SPs in the right-sided colon were an independent predictor of rSPMR (odds ratio, 3.47; 95% confidence interval, 1.89─6.38), along with a higher right-sided colon Boston Bowel Preparation Scale score (odds ratio, 0.55; 95% confidence interval, 0.32─0.94).

The significant reduction in rSPMR and increase in right-sided colon SP per colonoscopy suggest that colonoscopy insertion using WE is a valid alternative to CO 2 insufflation (clinical trial registration number: NCT04124393).

Colonoscopy is the gold standard for detecting and resecting adenomas or early stage cancers to reduce the incidence and mortality rates of colorectal cancer. In a recent observational study, texture and color enhancement imaging (TXI) was reported to improve polyp detection during colonoscopy. This randomized controlled trial involving six Japanese institutions aims to confirm the superiority of TXI over standard white-light imaging (WLI) in detecting colorectal lesions during colonoscopy. During the 1-year study period, 960 patients will be enrolled, with 480 patients in the TXI and WLI groups. The primary endpoint is the mean number of adenomas detected per procedure. The secondary endpoints include adenoma detection rate, advanced adenoma detection rate, polyp detection rate, flat polyp detection rate, depressed lesion detection rate, mean polyps detected per procedure, sessile serrated lesion (SSL) detection rate, mean SSLs detected per procedure and adverse events.

This review underscores the critical role and challenges associated with the widespread adoption of artificial intelligence in cancer care to enhance disease management, streamline clinical processes, optimize data retrieval of health information, and generate and synthesize evidence.

Advancements in artificial intelligence models and the development of digital biomarkers and diagnostics are applicable across the cancer continuum from early detection to survivorship care. Additionally, generative artificial intelligence has promised to streamline clinical documentation and patient communications, generate structured data for clinical trial matching, automate cancer registries, and facilitate advanced clinical decision support. Widespread adoption of artificial intelligence has been slow because of concerns about data diversity and data shift, model reliability and algorithm bias, legal oversight, and high information technology and infrastructure costs.

Artificial intelligence models have significant potential to transform cancer care. Efforts are underway to deploy artificial intelligence models in the cancer practice, evaluate their clinical impact, and enhance their fairness and explainability. Standardized guidelines for the ethical integration of artificial intelligence models in cancer care pathways and clinical operations are needed. Clear governance and oversight will be necessary to gain trust in artificial intelligence-assisted cancer care by clinicians, scientists, and patients.

Lesions of early cancers often show flat, small, and isochromatic characteristics in medical endoscopy images, which are difficult to be captured. By analyzing the differences between the internal and external features of the lesion area, we propose a lesion-decoupling-based segmentation (LDS) network for assisting early cancer diagnosis. We introduce a plug-and-play module called self-sampling similar feature disentangling module (FDM) to obtain accurate lesion boundaries. Then, we propose a feature separation loss (FSL) function to separate pathological features from normal ones. Moreover, since physicians make diagnoses with multimodal data, we propose a multimodal cooperative segmentation network with two different modal images as input: white-light images (WLIs) and narrowband images (NBIs). Our FDM and FSL show a good performance for both single-modal and multimodal segmentations. Extensive experiments on five backbones prove that our FDM and FSL can be easily applied to different backbones for a significant lesion segmentation accuracy improvement, and the maximum increase of mean Intersection over Union (mIoU) is 4.58. For colonoscopy, we can achieve up to mIoU of 91.49 on our Dataset A and 84.41 on the three public datasets. For esophagoscopy, mIoU of 64.32 is best achieved on the WLI dataset and 66.31 on the NBI dataset.

Detection of colorectal cancer (CRC) in the early stages through available screening tests increases the patient's survival chances. Multimodal screening policies can benefit patients by providing more diverse screening options and balancing the risks and benefits of screening tests. We investigate the cost-effectiveness of a wide variety of multimodal CRC screening policies.

We developed a Monte Carlo simulation framework to model CRC dynamics. We proposed an innovative calibration process using machine learning models to estimate age- and size-specific adenomatous polyps' progression and regression rates. The proposed approach significantly expedites the model parameter space search.

Two multimodal proposed policies (i.e., 1] colonoscopy at 50 y and fecal occult blood test annually between 60 and 75 y and 2] colonoscopy at 50 and 60 y and fecal immunochemical test annually between 70 and 75 y) are identified as efficient frontier policies. Both policies are cost-effective at a willingness to pay of $50,000. Sensitivity analyses were performed to assess the sensitivity of results to a change in screening test costs as well as adherence behavior. The sensitivity analysis results suggest that the proposed policies are mostly robust to the considered changes in screening test costs, as there is a significant overlap between the efficient frontier policies of the baseline and the sensitivity analysis cases. However, the efficient frontier policies were more sensitive to changes in adherence behavior.

Generally, combining stool-based tests with visual tests will benefit patients with higher life expectancy and a lower expected cost compared with unimodal screening policies. Colonoscopy at younger ages (when the colonoscopy complication risk is lower) and stool-based tests at older ages are shown to be more effective.

We propose a detailed Markov model to capture the colorectal cancer (CRC) dynamics. The proposed Markov model presents the detailed dynamics of adenomas progression to CRC.We use more than 44,000 colonoscopy reports and available data in the literature to calibrate the proposed Markov model using an innovative approach that leverages machine learning models to expedite the calibration process.We investigate the cost-effectiveness of a wide variety of multimodal CRC screening policies and compare their performances with the current in-practice policies.

The aim of this study was to determine if utilization of artificial intelligence (AI) in the course of endoscopic procedures can significantly diminish both the adenoma miss rate (AMR) and the polyp miss rate (PMR) compared with standard endoscopy.

We performed an extensive search of various databases, encompassing PubMed, Embase, Cochrane Library, Web of Science, and Scopus, until June 2023. The search terms used were artificial intelligence, machine learning, deep learning, transfer machine learning, computer-assisted diagnosis, convolutional neural networks, gastrointestinal (GI) endoscopy, endoscopic image analysis, polyp, adenoma, and neoplasms. The main study aim was to explore the impact of AI on the AMR, PMR, and sessile serrated lesion miss rate.

A total of 7 randomized controlled trials were included in this meta-analysis. Pooled AMR was markedly lower in the AI group versus the non-AI group (pooled relative risk [RR], .46; 95% confidence interval [CI], .36-.59; P < .001). PMR was also reduced in the AI group in contrast with the non-AI control (pooled RR, .43; 95% CI, .27-.69; P < .001). The results showed that AI decreased the miss rate of sessile serrated lesions (pooled RR, .43; 95% CI, .20 to .92; P < .05) and diminutive adenomas (pooled RR, .49; 95% CI, .26-.93) during endoscopy, but no significant effect was observed for advanced adenomas (pooled RR, .48; 95% CI, .17-1.37; P = .17). The average number of polyps (Hedges' g = -.486; 95% CI, -.697 to -.274; P = .000) and adenomas (Hedges' g = -.312; 95% CI, -.551 to -.074; P = .01) detected during the second procedure also favored AI. However, AI implementation did not lead to a prolonged withdrawal time (P > .05).

This meta-analysis suggests that AI technology leads to significant reduction of miss rates for GI adenomas, polyps, and sessile serrated lesions during endoscopic surveillance. These results underscore the potential of AI to improve the accuracy and efficiency of GI endoscopic procedures.

Cap-assisted endoscopy refers to a procedure in which a short tube made of a polymer (mostly transparent) is attached to the distal tip of the endoscope to enhance its diagnostic and therapeutic capabilities. It is reported to be particularly useful in: (1) minimizing blind spots during screening colonoscopy, (2) providing a constant distance from a lesion for clear visualization during magnifying endoscopy, (3) accurately assessing the size of various gastrointestinal lesions, (4) preventing mucosal injury during foreign body removal, (5) securing adequate workspace in the submucosal space during endoscopic submucosal dissection or third space endoscopy, (6) providing an optimal approach angle to a target, and (7) suctioning mucosal and submucosal tissue with negative pressure for resection or approximation. Here, we review various applications of attachable caps in diagnostic and therapeutic endoscopy and their future implications.

Endocuff Vision (Olympus Europe, Hamburg, Germany) has been designed to enhance mucosal visualization, thereby improving detection of (pre-)malignant colorectal lesions. This multicenter, international, back-to-back, randomized colonoscopy trial compared the adenoma detection rate (ADR) and adenoma miss rate (AMR) between Endocuff Vision-assisted colonoscopy (EVC) and conventional colonoscopy (CC).

Patients aged 40 to 75 years referred for non-immunochemical fecal occult blood test-based screening, surveillance, or diagnostic colonoscopy were included at 10 hospitals and randomized into 4 groups: group 1, 2 × CC; group 2, CC followed by EVC; group 3, EVC followed by CC; and group 4, 2 × EVC. Primary outcomes included ADR and AMR.

A total of 717 patients were randomized, of whom 661 patients (92.2%) had 1 and 646 (90.1%) patients had 2 completed back-to-back colonoscopies. EVC did not significantly improve ADR compared to CC (41.1%; [95% confidence interval (CI), 36.1-46.3] vs 35.5% [95% CI, 30.7-40.6], respectively; P = .125), but EVC did reduce AMR by 11.7% (29.6% [95% CI, 23.6-36.5] vs 17.9% [95% CI, 12.5-23.5], respectively; P = .049). AMR of 2 × CC compared to 2 × EVC was also not significantly different (25.9% [95% CI, 19.3-33.9] vs 18.8% [95% CI, 13.9-24.8], respectively; P = .172). Only 3.7% of the polyps missed during the first procedures had advanced pathologic features. Factors affecting risk of missing adenomas were age (P = .002), Boston Bowel Preparation Scale (P = .008), and region where colonoscopy was performed (P < .001).

Our trial shows that EVC reduces the risk of missing adenomas but does not lead to a significantly improved ADR. Remarkably, 25% of adenomas are still missed during conventional colonoscopies, which is not different from miss rates reported 25 years ago; reassuringly, advanced features were only found in 3.7% of these missed lesions. (Clinical trial registration number: NCT03418948.).

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.

The efficacy of texture and color enhancement imaging (TXI) in the novel light-emitting diode endoscopic system for polyp detection has not been examined. We aimed to evaluate the noninferiority of the additional 30-second (Add-30-s) observation of the right-sided colon (cecum/ascending colon) with TXI compared with narrow band imaging (NBI) for detecting missed polyps.

We enrolled 381 patients ≥40 years old who underwent colonoscopy from September 2021 to June 2022 in 3 institutions and randomly assigned them to either the TXI or NBI groups. The right-sided colon was first observed with white light imaging in both groups. Second, after reinsertion from hepatic flexure to the cecum, the right-sided colon was observed with Add-30-s observation of either TXI or NBI. The primary endpoint was to examine the noninferiority of TXI to NBI using the mean number of adenomas and sessile serrated lesions per patient. The secondary ones were to examine adenoma detection rate, adenoma and sessile serrated lesions detection rates, and polyp detection rates in both groups.

The TXI and NBI groups consisted of 177 and 181 patients, respectively, and the noninferiorities of the mean number of adenomas and sessile serrated lesions per patients in the second observation were significant (TXI 0.29 [51/177] vs NBI 0.30 [54/181], P < 0.01). The change in adenoma detection rate, adenoma and sessile serrated lesions detection rate, and polyp detection rate for the right-sided colon between the TXI and NBI groups were not different (10.2%/10.5% [ P = 0.81], 13.0%/12.7% [ P = 0.71], and 15.3%/13.8% [ P = 0.71]), respectively.

Regarding Add-30-s observation of the right-sided colon, TXI was noninferior to NBI.

There are challenges for beginners to identify standard biliopancreatic system anatomical sites on endoscopic ultrasonography (EUS) images. Therefore, the authors aimed to develop a convolutional neural network (CNN)-based model to identify standard biliopancreatic system anatomical sites on EUS images.

The standard anatomical structures of the gastric and duodenal regions observed by EUS was divided into 14 sites. The authors used 6230 EUS images with standard anatomical sites selected from 1812 patients to train the CNN model, and then tested its diagnostic performance both in internal and external validations. Internal validation set tests were performed on 1569 EUS images of 47 patients from two centers. Externally validated datasets were retrospectively collected from 16 centers, and finally 131 patients with 85 322 EUS images were included. In the external validation, all EUS images were read by CNN model, beginners, and experts, respectively. The final decision made by the experts was considered as the gold standard, and the diagnostic performance between CNN model and beginners were compared.

In the internal test cohort, the accuracy of CNN model was 92.1-100.0% for 14 standard anatomical sites. In the external test cohort, the sensitivity and specificity of CNN model were 89.45-99.92% and 93.35-99.79%, respectively. Compared with beginners, CNN model had higher sensitivity and specificity for 11 sites, and was in good agreement with the experts (Kappa values 0.84-0.98).

The authors developed a CNN-based model to automatically identify standard anatomical sites on EUS images with excellent diagnostic performance, which may serve as a potentially powerful auxiliary tool in future clinical practice.

There is a significant relationship between intestinal polyps and colorectal cancer, and in recent years, research on intestinal polyps has been rapidly developing around the world. However, there is still a lack of adequate quantification and analysis of publications in this field. The aim of this study was to perform a comprehensive bibliometric analysis of publications related to intestinal polyps over the past 20 years. To enhance the understanding of current research hotspots and potential trends, and to point out the direction of future research. Publications related to intestinal polyps were retrieved from the Science Citation Index Expanded in Web of Science Core Collection. the Bibliometric online analysis platform (https://bibliometric.com/app), the Bibliometrix Package, and the CiteSpace are used for bibliometric analysis and visualization, including the overall range of annual output and annual citations, country-region analysis, author and institution analysis, core journal analysis, reference and keyword analysis. Prior to 2017, the amount of research on intestinal polyps was slow to grow, but it picked up speed after that year. In 1019 journals, 4280 papers on intestinal polyps were published in English. The journal with the highest productivity was Gastrointestinal Endoscopy (189, 4.42%). United States (1124, 26.26%), which is also the hub of collaboration in this subject, was the most productive nation. Mayo Clinic (n = 70, 1.64%) is the most productive institution. Intestinal microbiota, endoscopic mucosal resection, gut microbiota, deep learning, tea polyphenol, insulin resistance and artificial intelligence were current hot subjects in the field. Studies of intestinal polyps increased significantly after 2017. The United States contributed the largest number of publications. Countries and institutions were actively cooperating with one another. artificial intelligence is currently an emerging topic.

With an increasing number of patients with gastrointestinal cancer, effective and accurate early diagnostic clinical tools are required provide better health care for patients with gastrointestinal cancer. Recent studies have shown that artificial intelligence (AI) plays an important role in the diagnosis and treatment of patients with gastrointestinal tumors, which not only improves the efficiency of early tumor screening, but also significantly improves the survival rate of patients after treatment. With the aid of efficient learning and judgment abilities of AI, endoscopists can improve the accuracy of diagnosis and treatment through endoscopy and avoid incorrect descriptions or judgments of gastrointestinal lesions. The present article provides an overview of the application status of various artificial intelligence in gastric and colorectal cancers in recent years, and the direction of future research and clinical practice is clarified from a clinical perspective to provide a comprehensive theoretical basis for AI as a promising diagnostic and therapeutic tool for gastrointestinal cancer.

The use of artificial intelligence (AI) in detecting colorectal neoplasia during colonoscopy holds the potential to enhance adenoma detection rates (ADRs) and reduce adenoma miss rates (AMRs). However, varied outcomes have been observed across studies. Thus, this study aimed to evaluate the potential advantages and disadvantages of employing AI-aided systems during colonoscopy.

Using Medical Subject Headings (MeSH) terms and keywords, a comprehensive electronic literature search was performed of the Embase, Medline, and the Cochrane Library databases from the inception of each database until October 04, 2023, in order to identify randomized controlled trials (RCTs) comparing AI-assisted with standard colonoscopy for detecting colorectal neoplasia. Primary outcomes included AMR, ADR, and adenomas detected per colonoscopy (APC). Secondary outcomes comprised the poly missed detection rate (PMR), poly detection rate (PDR), and poly detected per colonoscopy (PPC). We utilized random-effects meta-analyses with Hartung-Knapp adjustment to consolidate results. The prediction interval (PI) and I2 statistics were utilized to quantify between-study heterogeneity. Moreover, meta-regression and subgroup analyses were performed to investigate the potential sources of heterogeneity. This systematic review and meta-analysis is registered with PROSPERO (CRD42023428658).

This study encompassed 33 trials involving 27,404 patients. Those undergoing AI-aided colonoscopy experienced a significant decrease in PMR (RR, 0.475; 95% CI, 0.294-0.768; I2 = 87.49%) and AMR (RR, 0.495; 95% CI, 0.390-0.627; I2 = 48.76%). Additionally, a significant increase in PDR (RR, 1.238; 95% CI, 1.158-1.323; I2 = 81.67%) and ADR (RR, 1.242; 95% CI, 1.159-1.332; I2 = 78.87%), along with a significant increase in the rates of PPC (IRR, 1.388; 95% CI, 1.270-1.517; I2 = 91.99%) and APC (IRR, 1.390; 95% CI, 1.277-1.513; I2 = 86.24%), was observed. This resulted in 0.271 more PPCs (95% CI, 0.144-0.259; I2 = 65.61%) and 0.202 more APCs (95% CI, 0.144-0.259; I2 = 68.15%).

AI-aided colonoscopy significantly enhanced the detection of colorectal neoplasia detection, likely by reducing the miss rate. However, future studies should focus on evaluating the cost-effectiveness and long-term benefits of AI-aided colonoscopy in reducing cancer incidence.

This work was supported by the Heilongjiang Provincial Natural Science Foundation of China (LH2023H096), the Postdoctoral research project in Heilongjiang Province (LBH-Z22210), the National Natural Science Foundation of China's General Program (82072640) and the Outstanding Youth Project of Heilongjiang Natural Science Foundation (YQ2021H023).

Background and study aims Artificial intelligence (AI)-assisted colonoscopy has proven to be effective compared with colonoscopy alone in an average-risk population. We aimed to evaluate the cost-utility of GI GENIUS, the first marketed real-time AI system in an Italian high-risk population. Methods A 1-year cycle cohort Markov model was developed to simulate the disease evolution of a cohort of Italian individuals positive on fecal immunochemical test (FIT), aged 50 years, undergoing colonoscopy with or without the AI system. Adenoma or colorectal cancer (CRC) were identified according to detection rates specific for each technique. Costs were estimated from the Italian National Health Service perspective. Results Colonoscopy+AI system was dominant with respect to standard colonoscopy. The GI GENIUS system prevented 155 CRC cases (-2.7%), 77 CRC-related deaths (-2.8%), and improved quality of life (+0.027 QALY) with respect to colonoscopy alone. The increase in screening cost (+€10.50) and care for adenoma (+€3.53) was offset by the savings in cost of care for CRC (-€28.37), leading to a total savings of €14.34 per patient. Probabilistic sensitivity analysis confirmed the cost-efficacy of the AI system (almost 80% probability). Conclusions The implementation of AI detection tools in colonoscopy after patients test FIT-positive seems to be a cost-saving strategy for preventing CRC incidence and mortality.

Increasing the adenoma detection rate (ADR) helps reduce the risk of post-colonoscopy colorectal cancer. Texture and Color Enhancement Imaging (TXI) improves ADR by enhancing the brightness and contrast of endoscopic images. Endocuff Vision (ECV) is a mucosal exposure device that helps flatten the colonic folds. The benefit of combining TXI with ECV has not been studied previously. Thus, we aimed to compare the ADR between using TXI combined with ECV and TXI alone.

We conducted a prospective randomized controlled trial recruiting patients aged ≥ 40 years who underwent colonoscopy for colorectal cancer screening or gastrointestinal symptoms. The participants were randomized in a 1:1 ratio into the TXI with ECV (TXI + ECV) and the TXI groups. Experienced endoscopists with ≥ 40% ADR performed all colonoscopies. The primary outcome was ADR.

We had 189 and 192 patients in the TXI + ECV and TXI groups, respectively. The baseline characteristics of both groups were comparable. The ADR was significantly higher in the TXI + ECV group than in the TXI group (65.6% vs. 52.1%, P = 0.007). Adenoma per colonoscopy (APC) was significantly greater in the TXI + ECV group than in the TXI group (1.6 vs. 1.2, P = 0.021), prominently proximal (1.0 vs. 0.7, P = 0.031), non-pedunculated (1.4 vs. 1.1, P = 0.035), and diminutive (1.3 vs. 1, P = 0.045) adenomas. Serrated lesion detection rate, insertion time, and withdrawal time did not differ between the groups.

Adding ECV to TXI significantly improves ADR and APC compared to using TXI alone.

Thai Clinical Trials Registry TCTR20220507004.

Colorectal cancer (CRC) is a leading cause of cancer. The detection of pre-malignant lesions by colonoscopy is associated with reduced CRC incidence and mortality. Narrow band imaging has shown promising but conflicting results for the detection of serrated lesions.

We performed a randomized clinical trial to compare the mean detection of serrated lesions and hyperplastic polyps ≥10 mm with NBI or high-definition white light (HD-WL) withdrawal. We also compared all sessile serrated lesions (SSLs), adenoma, and polyp prevalence and rates.

Overall, 782 patients were randomized (WL group 392 patients; NBI group 390 patients). The average number of serrated lesions and hyperplastic polyps ≥10 mm detected per colonoscopy (primary endpoint) was similar between the HD-WL and NBI group (0.118 vs. 0.156, p = 0.44). Likewise, the adenoma detection rate (55.2% vs. 53.2%, p = 0.58) and SSL detection rate (6.8% vs. 7.5%, p = 0.502) were not different between the two study groups. Withdrawal time was higher in the NBI group (10.88 vs. 9.47 min, p = 0.004), with a statistically nonsignificant higher total procedure time (20.97 vs. 19.30 min, p = 0.052).

The routine utilization of narrow band imaging does not improve the detection of serrated class lesions or any pre-malignant lesion and increases the withdrawal time.

Endoscopist adenoma detection rates (ADRs) vary widely and are associated with patients' risk of postcolonoscopy colorectal cancers (PCCRCs). However, few scalable physician-directed interventions demonstrably both improve ADR and reduce PCCRC risk.

Among patients undergoing colonoscopy, we evaluated the influence of a scalable online training on individual-level ADRs and PCCRC risk. The intervention was a 30-minute, interactive, online training, developed using behavior change theory, to address factors that potentially impede detection of adenomas. Analyses included interrupted time series analyses for pretraining versus posttraining individual-physician ADR changes (adjusted for temporal trends) and Cox regression for associations between ADR changes and patients' PCCRC risk.

Across 21 endoscopy centers and all 86 eligible endoscopists, ADRs increased immediately by an absolute 3.13% (95% confidence interval [CI], 1.31-4.94) in the 3-month quarter after training compared with .58% per quarter (95% CI, .40-.77) and 0.33% per quarter (95% CI, .16-.49) in the 3-year pretraining and posttraining periods, respectively. Posttraining ADR increases were higher among endoscopists with pretraining ADRs below the median. Among 146,786 posttraining colonoscopies (all indications), each 1% absolute increase in screening ADR posttraining was associated with a 4% decrease in their patients' PCCRC risk (hazard ratio, .96; 95% CI, .93-.99). An ADR increase of ≥10% versus <1% was associated with a 55% reduced risk of PCCRC (hazard ratio, .45; 95% CI, .24-.82).

A scalable, online behavior change training intervention focused on modifiable factors was associated with significant and sustained improvements in ADR, particularly among endoscopists with lower ADRs. These ADR changes were associated with substantial reductions in their patients' risk of PCCRC.

Colorectal cancer (CRC) is a rapidly escalating public health concern, which underlines the significance of its early detection and the need for the refinement of current screening methods. In this systematic review, we aimed to analyze the potential advantages and limitations of artificial intelligence (AI)-based computer-aided detection (CADe) systems as compared to routine colonoscopy. This review begins by shedding light on the global prevalence and mortality rates of CRC, highlighting the urgent need for effective screening techniques and early detection of this cancer type. It addresses the problems associated with undetected adenomas and polyps and the subsequent risk of interval CRC following colonoscopy. The incorporation of AI into diagnostics has been studied, specifically the use of CADe systems which are powered by deep learning. The review summarizes the findings from 13 randomized controlled trials (RCTs) (2019-2023), evaluating the impact of CADe on polyp and adenoma detection. The findings from the studies consistently show that CADe is superior to conventional colonoscopy procedures in terms of adenoma detection rate (ADR) and polyp detection rate (PDR), particularly with regard to small and flat lesions which are easily overlooked. The review acknowledges certain limitations of the included studies, such as potential performance bias and geographic limitations. The review ultimately concludes that AI-assisted colonoscopy can reduce missed lesion rates and improve CRC diagnosis. Collaboration between experts and clinicians is key for successful implementation. In summary, this review analyzes recent RCTs on AI-assisted colonoscopy for polyp and adenoma detection. It describes the likely benefits, limitations, and future implications of AI in enhancing colonoscopy procedures and lowering the incidence of CRC. More double-blinded trials and studies among diverse populations from different countries must be conducted to substantiate and expand upon the findings of this review.

Colonoscopy is a useful as a cancer screening test. However, in countries with limited medical resources, there are restrictions on the widespread use of endoscopy. Non-invasive screening methods to determine whether a patient requires a colonoscopy are thus desired. Here, we investigated whether artificial intelligence (AI) can predict colorectal neoplasia.

We used data from physical exams and blood analyses to determine the incidence of colorectal polyp. However, these features exhibit highly overlapping classes. The use of a kernel density estimator (KDE)-based transformation improved the separability of both classes.

Along with an adequate polyp size threshold, the optimal machine learning (ML) models' performance provided 0.37 and 0.39 Matthews correlation coefficient (MCC) for the datasets of men and women, respectively. The models exhibit a higher discrimination than fecal occult blood test with 0.047 and 0.074 MCC for men and women, respectively.

The ML model can be chosen according to the desired polyp size discrimination threshold, may suggest further colorectal screening, and possible adenoma size. The KDE feature transformation could serve to score each biomarker and background factors (health lifestyles) to suggest measures to be taken against colorectal adenoma growth. All the information that the AI model provides can lower the workload for healthcare providers and be implemented in health care systems with scarce resources. Furthermore, risk stratification may help us to optimize the efficiency of resources for screening colonoscopy.

Colorectal cancer (CRC) is the third most common malignancy and second leading cause of cancer-related mortality in the world. Adenoma detection rate (ADR), a quality indicator for colonoscopy, has gained prominence as it is inversely related to CRC incidence and mortality. As such, recent efforts have focused on developing novel colonoscopy devices and technologies to improve ADR.

The main objective of this paper is to provide an overview of advancements in the fields of colonoscopy mechanical attachments, artificial intelligence-assisted colonoscopy, and colonoscopy optical enhancements with respect to ADR. We accomplished this by performing a comprehensive search of multiple electronic databases from inception to September 2023. This review is intended to be an introduction to colonoscopy devices and technologies.

Numerous mechanical attachments and optical enhancements have been developed that have the potential to improve ADR and AI has gone from being an inaccessible concept to a feasible means for improving ADR. While these advances are exciting and portend a change in what will be considered standard colonoscopy, they continue to require refinement. Future studies should focus on combining modalities to further improve ADR and exploring the use of these technologies in other facets of colonoscopy.

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.

To assess whether MUC1 peptide vaccine produces an immune response and prevents subsequent colon adenoma formation.

Multicenter, double-blind, placebo-controlled randomized trial in individuals age 40 to 70 with diagnosis of an advanced adenoma ≤1 year from randomization. Vaccine was administered at 0, 2, and 10 weeks with a booster injection at week 53. Adenoma recurrence was assessed ≥1 year from randomization. The primary endpoint was vaccine immunogenicity at 12 weeks defined by anti-MUC1 ratio ≥2.0.

Fifty-three participants received the MUC1 vaccine and 50 placebo. Thirteen of 52 (25%) MUC1 vaccine recipients had a ≥2-fold increase in MUC1 IgG (range, 2.9-17.3) at week 12 versus 0/50 placebo recipients (one-sided Fisher exact P < 0.0001). Of 13 responders at week 12, 11 (84.6%) responded to a booster injection at week 52 with a ≥2-fold increase in MUC1 IgG measured at week 55. Recurrent adenoma was observed in 31 of 47 (66.0%) in the placebo group versus 27 of 48 (56.3%) in the MUC1 group [adjusted relative risk (aRR), 0.83; 95% confidence interval (CI), 0.60-1.14; P = 0.25]. Adenoma recurrence occurred in 3/11 (27.3%) immune responders at week 12 and week 55 (aRR, 0.41; 95% CI, 0.15-1.11; P = 0.08 compared with placebo). There was no difference in serious adverse events.

An immune response was observed only in vaccine recipients. Adenoma recurrence was not different than placebo, but a 38% absolute reduction in adenoma recurrence compared with placebo was observed in participants who had an immune response at week 12 and with the booster injection.

Colorectal cancer (CRC) diagnosed following a cancer-negative colonoscopy is termed as post-colonoscopy CRC (PCCRC). The World Endoscopy Organization has recently standardized the definition of PCCRC-3Y (CRC developing within 3 years of a cancer-negative colonoscopy). In the present study, we sought to assess PCCRC-3Y rate, perform root-cause analyses, and identify factors associated with development of PCCRC at a tertiary referral hospital in Australia.

All patients undergoing colonoscopy from 2011 to 2018 were matched to a population-based cancer register. PCCRC-3Y rate was assessed for years 2011-2015. All PCCRC cases that developed within 6-48 months after a cancer-negative colonoscopy underwent root-cause analyses. Descriptive statistics were used to summarize data.

Among 17 828 patients undergoing colonoscopy, 367 CRC cases were diagnosed during the study period. This included nine PCCRC cases, which developed at a median of 14 months (range 7-34 months) after cancer-negative colonoscopy. The PCCRC-3Y rate for years 2011-2015 was 2.16% (95% CI 0.91-5.15). All nine PCCRC cases were moderately or poorly differentiated adenocarcinomas; seven of nine were early-stage CRC (stages I and II) and six of nine probably represented missed lesions at index colonoscopy despite an apparently adequate examination. History of inflammatory bowel disease (IBD) (odds ratio [OR] 21.9, 95% confidence interval [CI] 4.6-103.7, P < 0.001) and diverticulosis (OR 5.4, 95% CI 1.4-20.5, P = 0.01) were significantly associated with development of missed CRC.

In our tertiary referral colonoscopy cohort, PCCRC-3Y rate was 2.16% (95% CI 0.91-5.15). IBD and diverticulosis were significantly associated with risk of PCCRC. The majority of PCCRC lesions were likely missed at index colonoscopy, despite an apparently adequate examination.

Colonic polyps can be detected and resected during a colonoscopy before cancer development. However, about 1/4th of the polyps could be missed due to their small size, location or human errors. An artificial intelligence (AI) system can improve polyp detection and reduce colorectal cancer incidence. We are developing an indigenous AI system to detect diminutive polyps in real-life scenarios that can be compatible with any high-definition colonoscopy and endoscopic video- capture software.

We trained a masked region-based convolutional neural network model to detect and localize colonic polyps. Three independent datasets of colonoscopy videos comprising 1,039 image frames were used and divided into a training dataset of 688 frames and a testing dataset of 351 frames. Of 1,039 image frames, 231 were from real-life colonoscopy videos from our centre. The rest were from publicly available image frames already modified to be directly utilizable for developing the AI system. The image frames of the testing dataset were also augmented by rotating and zooming the images to replicate real-life distortions of images seen during colonoscopy. The AI system was trained to localize the polyp by creating a 'bounding box'. It was then applied to the testing dataset to test its accuracy in detecting polyps automatically.

The AI system achieved a mean average precision (equivalent to specificity) of 88.63% for automatic polyp detection. All polyps in the testing were identified by AI, i.e., no false-negative result in the testing dataset (sensitivity of 100%). The mean polyp size in the study was 5 (± 4) mm. The mean processing time per image frame was 96.4 minutes.

This AI system, when applied to real-life colonoscopy images, having wide variations in bowel preparation and small polyp size, can detect colonic polyps with a high degree of accuracy.

The implications of extracellular nicotinamide phosphoribosyltransferase (eNAMPT), a cancer metabokine, in colonic polyps remain uncertain.

A 2-year prospective cohort study of patients who underwent colonoscopy was conducted. Biochemical parameters and serum eNAMPT levels were analyzed at baseline and every 24 weeks postpolypectomy. NAMPT-associated single-nucleotide polymorphisms (SNPs), including rs61330082, rs2302559, rs10953502, and rs23058539, were assayed.

Of 532 patients, 80 (15%) had prominent malignant potential (PMP) in colonic polyps, including villous adenomas (n = 18, 3.3%), adenomas with high-grade dysplasia (n = 33, 6.2%), and adenocarcinomas (n = 29, 5.5%). Baseline associations were as follows: colonic polyp pathology (p < 0.001), total cholesterol (p = 0.019), and neutrophil-to-lymphocyte ratio (p = 0.023) with eNAMPT levels; and age (p < 0.001), polyp size (p < 0.001), and eNAMPT levels (p < 0.001) with polyp pathology. Higher baseline eNAMPT levels were noted in patients harboring polyps with PMP than in patients without PMP (p < 0.001), and baseline eNAMPT levels significantly predicted PMP (cutoff: >4.238 ng/mL, p < 0.001). Proportions of eNAMPT-positive glandular and stromal cells were higher in polyps with PMP than in polyps without PMP (64.55 ± 11.94 vs. 14.82 ± 11.45%, p = 0.025). eNAMPT levels decreased within 48 weeks postpolypectomy (p = 0.01) and remained stable afterward regardless of PMP until 96 weeks postpolypectomy. However, those with PMP had a higher degree of eNAMPT decline within 24 weeks (p = 0.046). All investigated SNPs were in linkage disequilibrium with each other but were not associated with eNAMPT levels.

With a link to inflammation and lipid metabolism, along with its decreasing trend after polypectomy, serum eNAMPT may serve as a surrogate marker of PMP in colonic polyps. In situ probing of the NAMPT-associated pathway holds promise in attenuating PMP, as much of the eNAMPT likely originates from colonic polyps.

Multiple computer-aided techniques utilizing artificial intelligence (AI) have been created to improve the detection of polyps during colonoscopy and thereby reduce the incidence of colorectal cancer. While adenoma detection rates (ADR) and polyp detection rates (PDR) are important colonoscopy quality indicators, adenoma miss rates (AMR) may better quantify missed lesions, which can ultimately lead to interval colorectal cancer. The purpose of this systematic review and meta-analysis was to determine the efficacy of computer-aided colonoscopy (CAC) with respect to AMR, ADR, and PDR in randomized controlled trials.

A comprehensive, systematic literature search was performed across multiple databases in September of 2022 to identify randomized, controlled trials that compared CAC with traditional colonoscopy. Primary outcomes were AMR, ADR, and PDR.

Fourteen studies totaling 10 928 patients were included in the final analysis. There was a 65% reduction in the adenoma miss rate with CAC (OR, 0.35; 95% CI, 0.25-0.49, P < 0.001, I²  = 50%). There was a 78% reduction in the sessile serrated lesion miss rate with CAC (OR, 0.22; 95% CI, 0.08-0.65, P < 0.01, I²  = 0%). There was a 52% increase in ADR in the CAC group compared with the control group (OR, 1.52; 95% CI, 1.39-1.67, P = 0.04, I²  = 47%). There was 93% increase in the number of adenomas > 10 mm detected per colonoscopy with CAC (OR 1.93; 95% CI, 1.18-3.16, P < 0.01, I²  = 0%).

The results of the present study demonstrate the promise of CAC in improving AMR, ADR, PDR across a spectrum of size and morphological lesion characteristics.

Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is with a colonoscopy. During this procedure, the gastroenterologist searches for polyps. However, there is a potential risk of polyps being missed by the gastroenterologist. Automated detection of polyps helps to assist the gastroenterologist during a colonoscopy. There are already publications examining the problem of polyp detection in the literature. Nevertheless, most of these systems are only used in the research context and are not implemented for clinical application. Therefore, we introduce the first fully open-source automated polyp-detection system scoring best on current benchmark data and implementing it ready for clinical application. To create the polyp-detection system (ENDOMIND-Advanced), we combined our own collected data from different hospitals and practices in Germany with open-source datasets to create a dataset with over 500,000 annotated images. ENDOMIND-Advanced leverages a post-processing technique based on video detection to work in real-time with a stream of images. It is integrated into a prototype ready for application in clinical interventions. We achieve better performance compared to the best system in the literature and score a F1-score of 90.24% on the open-source CVC-VideoClinicDB benchmark.