Differences with experienced nurse assistance during colonoscopy in detecting polyp and adenoma: a randomized clinical trial

Single Versus Second Observer vs Artificial Intelligence to Increase the ADENOMA Detection Rate of Colonoscopy-A Network Analysis

BACKGROUND AND AIMS

Screening colonoscopy has significantly contributed to the reduction of the incidence of colorectal cancer (CRC) and its associated mortality, with adenoma detection rate (ADR) as the quality marker. To increase the ADR, various solutions have been proposed including the utilization of Artificial Intelligence (AI) and employing second observers during colonoscopies. In the interest of AI improving ADR independently, without a second observer, and the operational similarity between AI and second observer, this network meta-analysis aims at evaluating the effectiveness of AI, second observer, and a single observer in improving ADR.

METHODS

We searched the Medline, Embase, Cochrane, Web of Science Core Collection, Korean Citation Index, SciELO, Global Index Medicus, and Cochrane. A direct head-to-head comparator analysis and network meta-analysis were performed using the random-effects model. The odds ratio (OR) was calculated with a 95% confidence interval (CI) and p-value < 0.05 was considered statistically significant.

RESULTS

We analyzed 26 studies, involving 22,560 subjects. In the direct comparative analysis, AI demonstrated higher ADR (OR: 0.668, 95% CI 0.595-0.749, p < 0.001) than single observer. Dual observer demonstrated a higher ADR (OR: 0.771, 95% CI 0.688-0.865, p < 0.001) than single operator. In network meta-analysis, results were consistent on the network meta-analysis, maintaining consistency. No statistical difference was noted when comparing AI to second observer. (RR 1.1 (0.9-1.2, p = 0.3). Results were consistent when evaluating only RCTs. Net ranking provided higher score to AI followed by second observer followed by single observer.

CONCLUSION

Artificial Intelligence and second-observer colonoscopy showed superior success in Adenoma Detection Rate when compared to single-observer colonoscopy. Although not statistically significant, net ranking model favors the superiority of AI to the second observer.

The effect of nurse assisted colonoscopy on adenoma detection rates: A meta-analysis of randomized controlled trials

BACKGROUND

Adenoma's detection rates have been reported to vary with the participation status of endoscopic nurses during colonoscopy. This meta-analysis was conducted to determine whether the participation of endoscopy nurses during colonoscopy contributed to the improved detection rate of polyps and adenomas.

METHODS

We retrieved English original research from PubMed, Embase, Web of Science, and Cochrane library databases and Chinese original research from the CNKI Data database. We searched for randomized controlled trials (RCTs) comparing the effect of participation of endoscopy nurses during colonoscopy of colorectal polyps and adenomas on polyp detection rates to that of nonparticipation. RevMan5.4 software was used to perform the meta-analysis.

RESULTS

This meta-analysis included 11 randomized controlled trials involving 8278 patients. The results showed no significant difference between colonoscopies performed by nurses and endoscopists, but colonoscopies performed by two nurses significantly improved the detection rate of polyps and adenomas. In the random effects model, there was a significant difference in PDR between the single-observation and dual-observation groups (RR, 1.27; 95%CI, 1.05, 1.54; Z = 2.51; P = 0.01). The ADR difference between the single observation group and the double observation group was statistically significant (RR, 1.15; 95%CI, 1.05, 1.26; Z = 2.91; P = 0.004).

CONCLUSION

Endoscopy nurses' participation in colonoscopy can improve the detection rate of polyps and adenomas, However, more research is needed to confirm the results.

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

BACKGROUND & AIMS

Adenoma detection rate (ADR) is inversely correlated with the risk of interval colon cancer and is a key target for quality improvement in endoscopy units. We conducted a systematic review and meta-analysis to identify and evaluate the effectiveness of interventions that can be implemented at the endoscopy unit level to improve ADRs.

METHODS

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic search was conducted in MEDLINE, Embase, and Cochrane Central Register of Controlled Trials databases between January 1990 and December 2022 to identify relevant studies. Both randomized controlled trials and observational studies were eligible. Data for the primary outcome of ADR were analyzed and reported on the log-odds scale with 95% CIs using a random-effects meta-analysis model using the empiric Bayes estimator.

RESULTS

From 10,778 initial citations, 34 studies were included in the meta-analysis comprising 371,041 procedures and 1501 endoscopists. The provision of report cards (odds ratio [OR], 1.28; 95% CI, 1.13-1.45; P < .001) and the presence of an additional observer to identify polyps (OR, 1.25; 95% CI, 1.09-1.43; P = .002) were associated with significant increases in ADRs whereas multimodal interventions were borderline significant (OR, 1.18; 95% CI, 1.00-1.40; P = .05) and withdrawal time monitoring was not associated significantly with an increase in ADRs (OR, 1.35; 95% CI, 0.93-1.96; P = .11).

CONCLUSIONS

The provision of report cards and the presence of an additional observer to identify polyps are associated with improved ADRs and should be considered for implementation in endoscopy facilities.

Factors affecting colorectal adenoma detection rate

Colorectal cancer is a kind of malignant tumors that seriously threatens the health of Chinese people, and its morbidity and mortality rank third and fifth among malignant tumors in China, respectively. High-quality colonoscopy is an effective means of preventing colorectal cancer. Adenoma detection rate (ADR) is defined as the proportion of those who have adenomas in the total number of patients undergoing colonoscopy. With the increase of ADR, the incidence rate and mortality rate of colorectal cancer gradually decrease. Previous studies have found that ADR is affected by many factors. In this paper, we describe the factors that affect ADR from the aspects of colonoscopy, doctors, and equipment, with a purpose to strengthen the understanding of endoscopists on ADR and to increase ADR during colonoscopy.

Artificial Intelligence in Endoscopy

Background

Owing to their rapid development, artificial intelligence (AI) technologies offer a great promise for gastroenterology practice and research. At present, AI-guided image interpretation has already been used with success for endoscopic detection of early malignant lesions. Nonetheless, there are complex challenges and possible shortcomings that must be considered before full implementation can be realized.

Summary

In this review, the current status of AI in endoscopy is summarized. Future perspectives and open questions for further studies are stressed.

Key Messages

The usage of AI algorithms for polyp detection in screening colonoscopy results in a significant increase in the adenoma detection rate, mainly attributed to the identification of diminutive polyps. Computer-aided characterization of colorectal polyps accompanies the detection, but further studies are needed to evaluate the clinical benefit. In contrast to colonoscopy, usage of AI in gastroscopy is currently rather limited. Regarding other fields of endoscopic imaging, capsule endoscopy is the ideal imaging platform for AI, due to the potential of saving time in the video analysis.

Is artificial intelligence the final answer to missed polyps in colonoscopy?

Lesions missed by colonoscopy are one of the main reasons for post-colonoscopy colorectal cancer, which is usually associated with a worse prognosis. Because the adenoma miss rate could be as high as 26%, it has been noted that endoscopists with higher adenoma detection rates are usually associated with lower adenoma miss rates. Artificial intelligence (AI), particularly the deep learning model, is a promising innovation in colonoscopy. Recent studies have shown that AI is not only accurate in colorectal polyp detection but can also reduce the miss rate. Nevertheless, the application of AI in real-time detection has been hindered by heterogeneity of the AI models and study design as well as a lack of long-term outcomes. Herein, we discussed the principle of various AI models and systematically reviewed the current data on the use of AI on colorectal polyp detection and miss rates. The limitations and future prospects of AI on colorectal polyp detection are also discussed.

Gastrointestinal endoscopy nurse assistance during colonoscopy and polyp detection: A PRISMA-compliant meta-analysis of randomized control trials

BACKGROUND

Previous studies showed difference results about the effect of nurse in improvement of the colonoscopy detection rate. This meta-analysis aims to investigate whether nurse participation during colonoscopy can help in improving the detection rate of polyps and adenomas.

METHODS

Original studies in English were searched from the MEDLINE database, PubMed, Web of Science, and the Cochrane Library database. Randomized control trials (RCT) comparing colonoscopy with and without nurse participation for the detection of colorectal polyps and adenomas were identified. A meta-analysis was performed using Revman 5.3 software.

RESULTS

A total of 2268 patients from 4 RCTs were included in this meta-analysis. Outcomes of colonoscopy with nurse participation were compared with those of colonoscopy without nurse participation. The results showed that nurses' participation during colonoscopy could significantly increase both, polyp detection rate and adenoma detection rate.

CONCLUSION

Nurse assistance during colonoscopy can help improve the rate of detection of polyps and adenomas.

Deep learning to find colorectal polyps in colonoscopy: A systematic literature review

Colorectal cancer has a great incidence rate worldwide, but its early detection significantly increases the survival rate. Colonoscopy is the gold standard procedure for diagnosis and removal of colorectal lesions with potential to evolve into cancer and computer-aided detection systems can help gastroenterologists to increase the adenoma detection rate, one of the main indicators for colonoscopy quality and predictor for colorectal cancer prevention. The recent success of deep learning approaches in computer vision has also reached this field and has boosted the number of proposed methods for polyp detection, localization and segmentation. Through a systematic search, 35 works have been retrieved. The current systematic review provides an analysis of these methods, stating advantages and disadvantages for the different categories used; comments seven publicly available datasets of colonoscopy images; analyses the metrics used for reporting and identifies future challenges and recommendations. Convolutional neural networks are the most used architecture together with an important presence of data augmentation strategies, mainly based on image transformations and the use of patches. End-to-end methods are preferred over hybrid methods, with a rising tendency. As for detection and localization tasks, the most used metric for reporting is the recall, while Intersection over Union is highly used in segmentation. One of the major concerns is the difficulty for a fair comparison and reproducibility of methods. Even despite the organization of challenges, there is still a need for a common validation framework based on a large, annotated and publicly available database, which also includes the most convenient metrics to report results. Finally, it is also important to highlight that efforts should be focused in the future on proving the clinical value of the deep learning based methods, by increasing the adenoma detection rate.

Comparison of Outcomes of Colorectal Polypectomy Using Traditional Snare and Rotary Snare: A Prospective Randomized Controlled Trial

Background and Objective

The aim of the current study was to compare the efficacy and safety of polypectomy by using rotary snare vs. traditional snare during colonoscopy.

Methods

A single-center randomized controlled trial, which included consecutive participants who were ≥18 years old and detected with polyp(s) during routine colonoscopy between July and September 2018, was conducted. Participants with colorectal polyps were randomized to receive polypectomy using rotary snares or traditional snares. The primary outcome measure was the comparison of the average time of removing a polyp between those two groups. The secondary outcome measure was to compare the polyp resection time by using SMSA (size, morphology, site, and access) scores.

Results

A total of two hundred participants were included in this study. Of them, 100 participants were randomly assigned to the rotary snare group (214 polyps) and the other 100 participants were randomly assigned to the traditional group (232 polyps). The mean resection time was significantly shorter in the rotary snare group than in the traditional snare group (24.41 ± 18.14 seconds vs. 29.53 ± 25.74 seconds, P = 0.021). In the subgroup analysis, the resection time was also shorter in the rotary snare group than in the traditional snare group in SMSA level 1 (18.51 ± 8.26 seconds vs.23.84 ± 15.07 seconds, P = 0.013) and in SMSA level 2 (25.03 ± 15.32 seconds vs.29.15 ± 24.82 seconds, P = 0.009), respectively.

Conclusion

Colorectal polyps could be removed more efficient by using rotary snares than by using traditional snares in SMSA level 1 and SMSA level 2.