Detection Of Colorectal Polyps Research Articles

Linked colour imaging versus white-light colonoscopy for the detection of flat colorectal lesions: A randomized controlled trial.

Linked colour imaging is an image-enhanced endoscopy system that emphasizes the red portion of the mucosa's colour. Our aim was to compare the effectiveness of linked colour imaging with white-light colonoscopy for the detection of flat-type colorectal polyps. This was a single-centre, randomized controlled trial. Enrolled patients were those aged ≥50years undergoing cap-assisted colonoscopy for colorectal cancer screening. They were randomized in a 1:1 ratio for observation using linked colour imaging or white-light colonoscopy. All colorectal polyps detected were removed or biopsied. The primary outcome was the number of flat-type polyps per patient in patients in whom flat polyps were detected. Secondary outcomes included adenoma and polyp detection rates. There were 302 subjects randomized: 152 to linked colour imaging and 150 to white-light colonoscopy. There were no differences in the clinical features between the two arms. The number of flat polyps detected per patient using linked colour imaging was approximately twice that with white light (2.9±3.0 vs 1.2±1.6, p=0.045). Linked colour imaging also proved superior to white-light colonoscopy in terms of adenoma and polyp detection rates [adenomas 66% (101/152) vs 49% (73/150), p=0.0024; polyps 69% (105/152) vs 55% (82/150), p=0.013]. The ratio of polyps detected in the right colon compared with those detected in the left colon was significantly greater using linked colour than white-light imaging (168/64 vs 93/84; p<0.001). Compared with white-light colonoscopy, linked colour imaging improved adenoma and polyp detection rates, including detection of flat-type colorectal polyps.

Computed Tomography Colonography as a Sensible Option for Colorectal Cancer Screening: Evidence Based on Metanalysis

Abstract Objective This metanalysis aimed to evaluate the sensitivity and specificity of computed tomography colonography in colorectal polyp detection. Methods A literature search was performed in the PubMed and Web of Science databases. Results A total of 1,872 patients (males 57.2%, females 42.8%) aged 49 to 82 years old (mean age 59.7 ± 5.3 years) were included in this metanalysis. The estimated sensitivity of computed tomography colonography was 88.4% (46.3–95.7%, coefficient of variation [CV] = 28.5%) and the estimated specificity was 73.6% (47.4–100.0%, CV = 37.5%). For lesions up to 9 mm, the sensitivity was 82.5% (62.0–99.9%, CV = 25.1%) and the specificity was 79.2% (32.0–98.0%, CV = 22.9%). For lesions &gt; 9 mm, the sensitivity was 90.2% (64.0–100.0%, CV = 7.4%) and the specificity was 94.7% (80.0–100.0%, CV = 6.2%). No statistically significant differences in sensitivity according to the size of the lesion were found (p = 0.0958); however, the specificity was higher for lesions &gt; 9 mm (p &lt; 0.0001). Conclusions Most of the studies analyzed in the present work were conducted before 2010, which is about a decade after computed tomography colonography started being indicated as a screening method by European and American guidelines. Therefore, more studies aimed at analyzing the technique after further technological advancements are necessary, which could lead to the development of more modern devices.

Application of artificial intelligence for detection and classification of colon polyps

Colorectal cancer is one of the most common cancers worldwide, and colonoscopy has proven to be a preferable modality for screening and surveillance of colorectal cancer. This review discusses the clinical application of artificial intelligence (AI) and computer-aided diagnosis for automated colonoscopic detection and diagnosis of colorectal polyps for better understanding of the application of AI-based computer-aided diagnosis systems especially in terms of machine learning, deep learning and convolutional neural network for screening and surveillance of colorectal cancer.

A-DenseUNet: Adaptive Densely Connected UNet for Polyp Segmentation in Colonoscopy Images with Atrous Convolution.

Colon carcinoma is one of the leading causes of cancer-related death in both men and women. Automatic colorectal polyp segmentation and detection in colonoscopy videos help endoscopists to identify colorectal disease more easily, making it a promising method to prevent colon cancer. In this study, we developed a fully automated pixel-wise polyp segmentation model named A-DenseUNet. The proposed architecture adapts different datasets, adjusting for the unknown depth of the network by sharing multiscale encoding information to the different levels of the decoder side. We also used multiple dilated convolutions with various atrous rates to observe a large field of view without increasing the computational cost and prevent loss of spatial information, which would cause dimensionality reduction. We utilized an attention mechanism to remove noise and inappropriate information, leading to the comprehensive re-establishment of contextual features. Our experiments demonstrated that the proposed architecture achieved significant segmentation results on public datasets. A-DenseUNet achieved a 90% Dice coefficient score on the Kvasir-SEG dataset and a 91% Dice coefficient score on the CVC-612 dataset, both of which were higher than the scores of other deep learning models such as UNet++, ResUNet, U-Net, PraNet, and ResUNet++ for segmenting polyps in colonoscopy images.

Artificial Intelligence in Colorectal Polyp Detection and Characterization.

Background:Over the past 20 years, the advancement of artificial intelligence (AI) and deep learning (DL) has allowed for fast sorting and analysis of large sets of data. In the field of gastroenterology, colorectal screening procedures produces an abundance of data through video and imaging. With AI and DL, this information can be used to create systems where automatic polyp detection and characterization is possible. Convoluted Neural Networks (CNNs) have proven to be an effective way to increase polyp detection and ultimately adenoma detection rates. Different methods of polyp characterization of being hyperplastic vs. adenomatous or non-neoplastic vs. neoplastic has also been investigated showing promising results.Findings:The rate of missed polyps on colonoscopy can be as high as 25%. At the beginning of the 2000s, hand-crafted machine learning (ML) algorithms were created and trained retrospectively on colonoscopy images and videos, achieving high sensitivity, specificity, and accuracy of over 90% in many of the studies. Over time, the advancement of DL and CNNs has allowed algorithms to be trained on non-medical images and applied retrospectively to colonoscopy videos and images with similar results. Within the past few years, these algorithms have been applied in real-time colonoscopies and has shown mixed results, one showing no difference while others showing increased polyp detection.Various methods of polyp characterization have also been investigated. Through AI, DL, and CNNs polyps can be identified has hyperplastic/adenomatous or non-neoplastic/neoplastic with high sensitivity, specificity, and accuracy. One of the research areas in polyp characterization is how to capture the polyp image. This paper looks at different modalities of characterizing polyps such as magnifying narrow band imaging (NBI), endocytoscopy, laser-induced florescent spectroscopy, auto-florescent endoscopy, and white-light endoscopy.Conclusions:Overall, much progress has been made in automatic detection and characterization of polyps in real time. Barring ethical or mass adoption setbacks, it is inevitable that AI will be involved in the field of GI, especially in colorectal polyp detection and identification.

Artificial intelligence for identification and characterization of colonic polyps.

Colonoscopy remains the gold standard exam for colorectal cancer screening due to its ability to detect and resect pre-cancerous lesions in the colon. However, its performance is greatly operator dependent. Studies have shown that up to one-quarter of colorectal polyps can be missed on a single colonoscopy, leading to high rates of interval colorectal cancer. In addition, the American Society for Gastrointestinal Endoscopy has proposed the “resect-and-discard” and “diagnose-and-leave” strategies for diminutive colorectal polyps to reduce the costs of unnecessary polyp resection and pathology evaluation. However, the performance of optical biopsy has been suboptimal in community practice. With recent improvements in machine-learning techniques, artificial intelligence–assisted computer-aided detection and diagnosis have been increasingly utilized by endoscopists. The application of computer-aided design on real-time colonoscopy has been shown to increase the adenoma detection rate while decreasing the withdrawal time and improve endoscopists’ optical biopsy accuracy, while reducing the time to make the diagnosis. These are promising steps toward standardization and improvement of colonoscopy quality, and implementation of “resect-and-discard” and “diagnose-and-leave” strategies. Yet, issues such as real-world applications and regulatory approval need to be addressed before artificial intelligence models can be successfully implemented in clinical practice. In this review, we summarize the recent literature on the application of artificial intelligence for detection and characterization of colorectal polyps and review the limitation of existing artificial intelligence technologies and future directions for this field.

Artificial intelligence in gastrointestinal endoscopy.

Background and AimsArtificial intelligence (AI)-based applications have transformed several industries and are widely used in various consumer products and services. In medicine, AI is primarily being used for image classification and natural language processing and has great potential to affect image-based specialties such as radiology, pathology, and gastroenterology (GE). This document reviews the reported applications of AI in GE, focusing on endoscopic image analysis.MethodsThe MEDLINE database was searched through May 2020 for relevant articles by using key words such as machine learning, deep learning, artificial intelligence, computer-aided diagnosis, convolutional neural networks, GI endoscopy, and endoscopic image analysis. References and citations of the retrieved articles were also evaluated to identify pertinent studies. The manuscript was drafted by 2 authors and reviewed in person by members of the American Society for Gastrointestinal Endoscopy Technology Committee and subsequently by the American Society for Gastrointestinal Endoscopy Governing Board.ResultsDeep learning techniques such as convolutional neural networks have been used in several areas of GI endoscopy, including colorectal polyp detection and classification, analysis of endoscopic images for diagnosis of Helicobacter pylori infection, detection and depth assessment of early gastric cancer, dysplasia in Barrett’s esophagus, and detection of various abnormalities in wireless capsule endoscopy images.ConclusionsThe implementation of AI technologies across multiple GI endoscopic applications has the potential to transform clinical practice favorably and improve the efficiency and accuracy of current diagnostic methods.

Assessing perspectives on artificial intelligence applications to gastroenterology

Assessing perspectives on artificial intelligence applications to gastroenterology

Second-generation colon capsule endoscopy for detection of colorectal polyps: A meta-analysis

Second-generation colon capsule endoscopy for detection of colorectal polyps: A meta-analysis

Scope of Artificial Intelligence in Screening and Diagnosis of Colorectal Cancer

Globally, colorectal cancer is the third most diagnosed malignancy. It causes significant mortality and morbidity, which can be reduced by early diagnosis with an effective screening test. Integrating artificial intelligence (AI) and computer-aided detection (CAD) with screening methods has shown promising colorectal cancer screening results. AI could provide a “second look” for endoscopists to decrease the rate of missed polyps during a colonoscopy. It can also improve detection and characterization of polyps by integration with colonoscopy and various advanced endoscopic modalities such as magnifying narrow-band imaging, endocytoscopy, confocal endomicroscopy, laser-induced fluorescence spectroscopy, and magnifying chromoendoscopy. This descriptive review discusses various AI and CAD applications in colorectal cancer screening, polyp detection, and characterization.

Real-time unblinding for validation of a new CADe tool for colorectal polyp detection

### Summary box #### What is already known about this subject? #### What are the new findings? #### How might it impact on clinical practice in the foreseeable future? In...

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.

Automatic detection and segmentation of adenomatous colorectal polyps during colonoscopy using Mask R-CNN.

Colorectal cancer (CRC) is one of the main alimentary tract system malignancies affecting people worldwide. Adenomatous polyps are precursors of CRC, and therefore, preventing the development of these lesions may also prevent subsequent malignancy. However, the adenoma detection rate (ADR), a measure of the ability of a colonoscopist to identify and remove precancerous colorectal polyps, varies significantly among endoscopists. Here, we attempt to use a convolutional neural network (CNN) to generate a unique computer-aided diagnosis (CAD) system by exploring in detail the multiple-scale performance of deep neural networks. We applied this system to 3,375 hand-labeled images from the screening colonoscopies of 1,197 patients; of whom, 3,045 were assigned to the training dataset and 330 to the testing dataset. The images were diagnosed simply as either an adenomatous or non-adenomatous polyp. When applied to the testing dataset, our CNN-CAD system achieved a mean average precision of 89.5%. We conclude that the proposed framework could increase the ADR and decrease the incidence of interval CRCs, although further validation through large multicenter trials is required.

Application of Artificial Intelligence in the Detection and Characterization of Colorectal Neoplasm.

Endoscpists always have tried to pursue a perfect colonoscopy, and application of artificial intelligence (AI) using deep-learning algorithms is one of the promising supportive options for detection and characterization of colorectal polyps during colonoscopy. Many retrospective studies conducted with real-time application of AI using convolutional neural networks have shown improved colorectal polyp detection. Moreover, a recent randomized clinical trial reported additional polyp detection with shorter analysis time. Studies conducted regarding polyp characterization provided additional promising results. Application of AI with narrow band imaging in real-time prediction of the pathology of diminutive polyps resulted in high diagnostic accuracy. In addition, application of AI with endocytoscopy or confocal laser endomicroscopy was investigated for real-time cellular diagnosis, and the diagnostic accuracy of some studies was comparable to that of pathologists. With AI technology, we can expect a higher polyp detection rate with reduced time and cost by avoiding unnecessary procedures, resulting in enhanced colonoscopy efficiency. However, for AI application in actual daily clinical practice, more prospective studies with minimized selection bias, consensus on standardized utilization, and regulatory approval are needed. (Gut Liver 2021;15:-353)

Second-generation colon capsule endoscopy for detection of colorectal polyps: A meta-analysis.

Background: Colorectal cancer counts as the third prevalent type of cancer and the fourth cause of death worldwide. The second-generation colon capsule endoscopy (CCE-2) is a new technology for the diagnosis of colon cancer. The aim of this review was to provide information on the diagnostic accuracy (diagnostic effectiveness) of the second-generation colon capsule endoscopy compared to colonoscopy for the diagnosis of colon cancer and disorders. Methods: A systematic review of literature in PubMed, Scopus, Science Direct, and Cochrane Library and Iranian databases, such as MagIran, SID, Irandoc, the grey literature (via Google Scholar) was conducted on February 30, 2018. QUADAS-2 was used to assess the quality of the studies. MetaDiSc 2.0 software was used for the meta-analysis. Results: In this review, 480 records were identified. Eight prospective cohort articles were included among which 7 included in the meta-analysis. For the diagnosis of colorectal polyps with a diameter of 6-10 mm, the pooled sensitivity and specificity were 84% (95% CI, 80% -88%) and 88% (95%CI, 85% -90%). For the diagnosis of 10 mm or bigger colorectal polyps, the pooled sensitivity and specificity were 84% (95% CI, 76%-89%) and 96% (95% CI, 94 %-97%). The sensitivity and specificity of the capsule in the detection of any size polyps were 93% (95% CI, 97%-84%) and 66% (95% CI, 48%-81%), respectively. Conclusion: There is little evidence to show the accuracy of CCE-2. Nevertheless, this review showed that the second-generation colon capsule endoscopy has good accuracy in the detection of polyps and colorectal cancer among high- and middle-risk patients.

Improved adenoma detection with linked color imaging technology compared to white-light colonoscopy

ObjectivesLinked color imaging (LCI) is a new endoscopic technology that may increase colorectal adenoma detection rate (ADR) and polyp detection rate (PDR) by virtual chromoendoscopy. Aim of the present study was to evaluate the effectiveness of LCI in ADR and PDR compared to the HD white-light colonoscopy (WLC) technique.Materials and methodsBetween October 2016 and June 2018, we enrolled consecutive outpatients prospectively. Eligible patients allocated randomly to undergo HD WLC or LCI colonoscopy technique during instrument withdrawal. Each colonoscopy was performed in a single center by the same three expert endoscopists (with expertise more than 5000 colonoscopies).ResultsA total of 1278 patients underwent colonoscopy in the study period. ADR and PDR were significantly higher in the LCI group compared to the WLC group (34.4% vs. 26.8%; p = .007; and 53.3% vs 46.4%; p = .023, respectively). Similarly, the mean number of adenomas per patient (MAP) was significantly higher with the LCI than WLC (0.64 vs 0.44, respectively; p = .002). The mean age of patients at the time of colonoscopy was 51.95 years (SD = 13.861) in the LCI group and 51.96 years (SD = 14.028) in the WLC group. No significant differences observed in patient demographic characteristics (there was no difference in gender and age distribution, p = .986), quality of colonoscopy preparation and withdrawal times (WT) between the two groups (WLC and the LCI groups, 493.9 (SD: 143.5) and 514.0 (SD: 139.5) sec, respectively).ConclusionsAccording to our results, LCI virtual chromoendoscopic technology was superior compared to conventional HD WLC in detecting colorectal polyps and adenomas.

Decomposition of color wavelet with higher order statistical texture and convolutional neural network features set based classification of colorectal polyps from video endoscopy

Gastrointestinal cancer is one of the leading causes of death across the world. The gastrointestinal polyps are considered as the precursors of developing this malignant cancer. In order to condense the probability of cancer, early detection and removal of colorectal polyps can be cogitated. The most used diagnostic modality for colorectal polyps is video endoscopy. But the accuracy of diagnosis mostly depends on doctors' experience that is crucial to detect polyps in many cases. Computer-aided polyp detection is promising to reduce the miss detection rate of the polyp and thus improve the accuracy of diagnosis results. The proposed method first detects polyp and non-polyp then illustrates an automatic polyp classification technique from endoscopic video through color wavelet with higher-order statistical texture feature and Convolutional Neural Network (CNN). Gray Level Run Length Matrix (GLRLM) is used for higher-order statistical texture features of different directions (Ɵ = 0o, 45o, 90o, 135o). The features are fed into a linear support vector machine (SVM) to train the classifier. The experimental result demonstrates that the proposed approach is auspicious and operative with residual network architecture, which triumphs the best performance of accuracy, sensitivity, and specificity of 98.83%, 97.87%, and 99.13% respectively for classification of colorectal polyps on standard public endoscopic video databases.

Combined endo-laparoscopic surgery for difficult benign colorectal polyps.

Prevention of colorectal cancer (CRC) depends largely on the detection and removal of colorectal polyps. Despite the advances in endoscopic techniques, there are still a subgroup of polyps that cannot be treated purely by endoscopic approach, which comprise of about 10-15% of all the polyps. These so-called "difficult colorectal polyps" are polyps with large size, morphology, at difficult location, scarring or due to recurrence, which have historically been managed by surgical segmental resection. In treating benign difficult colorectal polyps, we have to balance the operative risks and morbidities associated with surgical segmental resection. Therefore, combined endoscopic and laparoscopic surgery (CELS) has been developed to remove this subgroup of difficult benign polyps. We review the currently use of CELS for difficult benign colorectal polyps which includes laparoscopy-assisted endoscopic polypectomy (LACP), full-thickness laparo-endoscopic excision (FLEX) and colonoscopy-assisted laparoscopic wedge resection (CAL-WR).

Artificial Intelligence for Colorectal Polyp Detection and Characterization

To elucidate the advantages and limitations of existing artificial intelligence technologies for colonoscopy by evaluating the currently available eight prospective studies (two for automated polyp detection and six for automated polyp characterization). AI is expected to mitigate the inherent risk of human error causing a polyp to be missed or mischaracterized by assisting polyp detection and characterization (i.e., pathological prediction). Some of the prospective studies clearly demonstrate the potential for AI to improve adenoma detection rates, which is considered one of the most important quality indicators for colonoscopies, or achieve a > 90% negative predictive value in differentiating diminutive (≤ 5 mm) rectosigmoid adenomas which is considered as a threshold required for optical diagnosis. However, it is also important to consider the negative impacts of AI, such as the deskilling effect on healthcare providers, which has yet to be sufficiently addressed. We believe that AI can become standard practice in colonoscopy procedures within several years, given its rapid spread and its expected low implementation cost. However, considering the limited evidence supporting the use of AI for colonoscopy, additional studies should be done to explore the long-term efficacy and safety of AI in colonoscopy and implement robust endpoints such as colorectal cancer incidence and mortality.