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INTRODUCTION: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.METHODS: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.RESULTS: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.DISCUSSION:Regarding Add-30-s observation of the right-sided colon, TXI was noninferior to NBI.

There has been increasing interest in artificial intelligence in gastroenterology. To reduce miss rates during colonoscopy, there has been significant exploration in computer-aided detection (CADe) devices. In this study, we evaluate the use of CADe in colonoscopy in community-based, nonacademic practices. Between September 28, 2020, and September 24, 2021, a randomized controlled trial (AI-SEE) was performed evaluating the impact of CADe on polyp detection in 4 community-based endoscopy centers in the United States Patients were block-randomized to undergoing colonoscopy with or without CADe (EndoVigilant). Primary outcomes measured were adenomas per colonoscopy and adenomas per extraction (the percentage of polyps removed that are adenomas). Secondary end points included serrated polyps per colonoscopy; nonadenomatous, nonserrated polyps per colonoscopy; adenoma and serrated polyp detection rates; and procedural time. A total of 769 patients were enrolled (387 with CADe), with similar patient demographics between the 2 groups. There was no significant difference in adenomas per colonoscopy in the CADe and non-CADe groups (0.73 vs 0.67, P = 0.496). Although the use of CADe did not improve identification of serrated polyps per colonoscopy (0.08 vs 0.08, P = 0.965), the use of CADe increased identification of nonadenomatous, nonserrated polyps per colonoscopy (0.90 vs 0.51, P < 0.0001), resulting in detection of fewer adenomas per extraction in the CADe group. The adenoma detection rate (35.9 vs 37.2%, P = 0.774) and serrated polyp detection rate (6.5 vs 6.3%, P = 1.000) were similar in the CADe and non-CADe groups. Mean withdrawal time was longer in the CADe group compared with the non-CADe group (11.7 vs 10.7 minutes, P = 0.003). However, when no polyps were identified, there was similar mean withdrawal time (9.1 vs 8.8 minutes, P = 0.288). There were no adverse events. The use of CADe did not result in a statistically significant difference in the number of adenomas detected. Additional studies are needed to better understand why some endoscopists derive substantial benefits from CADe and others do not. ClinicalTrials.gov number: NCT04555135.

Background and aims Improving the rate of polyp detection is an important measure to prevent colorectal cancer (CRC). Real-time automatic polyp detection systems, through deep learning methods, can learn and perform specific endoscopic tasks previously performed by endoscopists. The purpose of this study was to explore whether a high-performance, real-time automatic polyp detection system could improve the polyp detection rate (PDR) in the actual clinical environment. Methods The selected patients underwent same-day, back-to-back colonoscopies in a random order, with either traditional colonoscopy or artificial intelligence (AI)-assisted colonoscopy performed first by different experienced endoscopists (> 3000 colonoscopies). The primary outcome was the PDR. It was registered with clinicaltrials.gov . (NCT047126265). Results In this study, we randomized 150 patients. The AI system significantly increased the PDR (34.0% vs 38.7%, p < 0.001). In addition, AI-assisted colonoscopy increased the detection of polyps smaller than 6 mm (69 vs 91, p < 0.001), but no difference was found with regard to larger lesions. Conclusions A real-time automatic polyp detection system can increase the PDR, primarily for diminutive polyps. However, a larger sample size is still needed in the follow-up study to further verify this conclusion. Trial Registration clinicaltrials.gov Identifier: NCT047126265

Linked color imaging (LCI) was developed to detect gastrointestinal neoplasms. The current study aimed to determine whether the use of LCI, compared with white-light imaging (WLI), can improve the miss rates of colorectal polyp. A multicenter, randomized back-to-back study was conducted in 16 Japanese endoscopy units. Patients were randomized according to examination: tandem colonoscopy with WLI followed by LCI (WLI-LCI group) and with LCI followed by WLI (LCI-WLI group). The detected polyps were evaluated according to location, size, morphology, and histopathological diagnosis. The primary outcome was polyp miss rate per patient (PMR-PP) in total colonoscopy. The secondary outcome was adenoma detection rate (ADR) during the first assessment in each group. The full analysis set comprised 327 participants, and 320 were included in either two groups. The PMR-PPs were 9.3% and 20.6% in the LCI-WLI and WLI-LCI groups, respectively. Regarding location, the PMR-PP of LCI was significantly lower than that of WLI in the transverse and descending colons and rectum. In terms of diminutive adenomas (< 5 mm), the ADR of LCI (38.2%) was significantly higher than that of WLI (29.1%). LCI was superior to WLI in terms of polyp miss rate particularly in the transverse and descending colons and rectum.

Introduction: Computer-aided diagnostic systems are emerging in the field of gastrointestinal endoscopy. In this study, we assessed the clinical performance of the computer-aided detection (CADe) of colonic adenomas using a new endoscopic artificial intelligence system. Methods: This was a single-center prospective randomized study including 415 participants allocated into the CADe group (n = 207) and control group (n = 208). All endoscopic examinations were performed by experienced endoscopists. The performance of the CADe was assessed based on the adenoma detection rate (ADR). Additionally, we compared the adenoma miss rate for the rectosigmoid colon (AMRrs) between the groups. Results: The basic demographic and procedural characteristics of the CADe and control groups were as follows: mean age, 54.9 and 55.9 years; male sex, 73.9% and 69.7% of participants; and mean withdrawal time, 411.8 and 399.0 s, respectively. The ADR was 59.4% in the CADe group and 47.6% in the control group (p = 0.018). The AMRrs was 11.9% in the CADe group and 26.0% in the control group (p = 0.037). Conclusion: The colonoscopy with the CADe system yielded an 11.8% higher ADR than that performed by experienced endoscopists alone. Moreover, there was no need to extend the examination time or request the assistance of additional medical staff to achieve this improved effectiveness. We believe that the novel CADe system can lead to considerable advances in colorectal cancer diagnosis.

ObjectiveDespite regular colonoscopy surveillance, colorectal cancers still occur in patients with Lynch syndrome. Thus, detection of all relevant precancerous lesions remains very important. The present study investigates Linked Colour imaging (LCI), an image-enhancing technique, as compared with high-definition white light endoscopy (HD-WLE) for the detection of polyps in this patient group.DesignThis prospective, randomised controlled trial was performed by 22 experienced endoscopists from eight centres in six countries. Consecutive Lynch syndrome patients ≥18 years undergoing surveillance colonoscopy were randomised (1:1) and stratified by centre for inspection with either LCI or HD-WLE. Primary outcome was the polyp detection rate (PDR).ResultsBetween January 2018 and March 2020, 357 patients were randomised and 332 patients analysed (160 LCI, 172 HD-WLE; 6 excluded due to incomplete colonoscopies and 19 due to insufficient bowel cleanliness). No significant difference was observed in PDR with LCI (44.4%; 95% CI 36.5% to 52.4%) compared with HD-WLE (36.0%; 95% CI 28.9% to 43.7%) (p=0.12). Of the secondary outcome parameters, more adenomas were found on a patient (adenoma detection rate 36.3%; vs 25.6%; p=0.04) and a colonoscopy basis (mean adenomas per colonoscopy 0.65 vs 0.42; p=0.04). The median withdrawal time was not statistically different between LCI and HD-WLE (12 vs 11 min; p=0.16).ConclusionLCI did not improve the PDR compared with HD-WLE in patients with Lynch syndrome undergoing surveillance. The relevance of findings more adenomas by LCI has to be examined further.Trial registration number NCT03344289.

Colonoscopy is the gold-standard test for investigation of symptoms suggestive of colorectal cancer; computed tomographic colonography (CTC) is an alternative, less invasive test. However, additional investigation after CTC is needed to confirm suspected colonic lesions, and this is an important factor in establishing the feasibility of CTC as an alternative to colonoscopy. We aimed to compare rates of additional colonic investigation after CTC or colonoscopy for detection of colorectal cancer or large (≥10 mm) polyps in symptomatic patients in clinical practice. This pragmatic multicentre randomised trial recruited patients with symptoms suggestive of colorectal cancer from 21 UK hospitals. Eligible patients were aged 55 years or older and regarded by their referring clinician as suitable for colonoscopy. Patients were randomly assigned (2:1) to colonoscopy or CTC by computer-generated random numbers, in blocks of six, stratified by trial centre and sex. We analysed the primary outcome—the rate of additional colonic investigation—by intention to treat. The trial is an International Standard Randomised Controlled Trial, number 95152621. 1610 patients were randomly assigned to receive either colonoscopy (n=1072) or CTC (n=538). 30 patients withdrew consent, leaving for analysis 1047 assigned to colonoscopy and 533 assigned to CTC. 160 (30·0%) patients in the CTC group had additional colonic investigation compared with 86 (8·2%) in the colonoscopy group (relative risk 3·65, 95% CI 2·87–4·65; p<0·0001). Almost half the referrals after CTC were for small (<10 mm) polyps or clinical uncertainty, with low predictive value for large polyps or cancer. Detection rates of colorectal cancer or large polyps in the trial cohort were 11% for both procedures. CTC missed 1 of 29 colorectal cancers and colonoscopy missed none (of 55). Serious adverse events were rare. Guidelines are needed to reduce the referral rate after CTC. For most patients, however, CTC provides a similarly sensitive, less invasive alternative to colonoscopy. NIHR Health Technology Assessment Programme, NIHR Biomedical Research Centres funding scheme, Cancer Research UK, EPSRC Multidisciplinary Assessment of Technology Centre for Healthcare, and NIHR Collaborations for Leadership in Applied Health Research and Care.

Background and aimsNew image-enhanced endoscopy (IEE), blue Light Imaging (LED-BLI) is launched in USA and Europe, whereas Blue Laser Imaging (Laser-BLI) is available only Asian and some countries. No studies have directly compared the diagnostic accuracy of narrow band imaging (NBI), Laser-BLI and LED-BLI for colorectal tumors. The present study aimed to compare the diagnostic accuracy of the three methods for colorectal tumor using the NBI international colorectal endoscopic (NICE) classification and the Japanese NBI Expert Team (JNET) classifications.MethodsThis was a multi-center evaluator-blinded, randomized control trial of patients who underwent endoscopic colorectal tumor resection. The patients were randomly assigned to NBI, Laser-BLI or LED-BLI. Cropped images were sent to blinded external evaluators and diagnosed according to NICE and JNET classifications. The diagnostic accuracy of each endoscopy system was compared with non-inferiority test.ResultsA total of 619 colonic tumors were resected from 230 patients and evaluated by external four evaluators. The diagnostic accuracy of NBI for NICE 1, NICE 2, NICE 3 was 90.6%, 90.3% and 99.5%, respectively and for JNET 1, JNET 2A, JNET 2B and JNET 3, it was 94.6%, 72.0%, 79.2% and 99.1%, respectively. In non-inferiority test, Laser-BLI and LED-BLI revealed non-inferiority to NBI in all NICE and JNET categories (p<0.001).ConclusionsLaser-BLI and LED-BLI had high diagnostic accuracy and non-inferiority of NBI, especially for hyperplastic polyp/sessile serrated lesion and low-grade dysplasia. This is first trial to compare the diagnostic accuracy with NBI, Laser-BLI and LED-BLI and useful to understand the position of each IEE. This trial was registered as UMIN000032107.

Objective To evaluate the effect of a novel-designed colonoscopic distal attachment on the diagnosis of polyps during colonoscopy. Method All consecutive patients who underwent routine colonoscopic examinations at Tongji Hospital endoscopy centers in China were enrolled. Participants were randomly assigned in a 1:1 ratio to the auxiliary devices colonoscopy (Experimental Group) or standard colonoscopy groups (Control Group). Adenoma detection rate (ADR), polyp detection rate (PDR), inspection time, duration of treatment and adverse events were recorded. Result A total of 264 patients were randomized into the experimental group (EG, n  = 132) and the control group (CG, n  = 132). The PDR in the EG was significantly higher than in the CG ( p  = 0.013), while no significant difference was found in the ADR between the two groups ( p  = 0.078). Insertion and withdrawal times for colonoscopy were showed no statistically significant difference ( p  = 0.096 and p  = 0.868, respectively). Analysis of polyp detection conditions revealed that the EG had a higher number of polyps detected( p  = 0.013), and detected more polyps ≤ 5 mm ( p  = 0.018), and there was no significant difference in the location distribution of detected polyps between the two groups ( p  = 0.950). Treatment times were showed statistically significant differences, with the EG having shorter treatment times ( p  = 0.004). Conclusion The use of auxiliary devices in colonoscopy significantly improves the PDR, espicialy no-adnoma or smaller polyp, and reduces treatment times compared to standard colonoscopy. Trial registration Chinese Clinical Trial Registry (ChiCTR), ChiCTR2500097582, February 21, 2025. Retrospectively registered.

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