Detection Of Colorectal Polyps Research Articles

Real-time colorectal polyp detection using a novel computer-aided detection system (CADe): a feasibility study

Background and aimsColonoscopy aims to early detect and remove precancerous colorectal polyps, thereby preventing development of colorectal cancer (CRC). Recently, computer-aided detection (CADe) systems have been developed to assist endoscopists in polyp detection during colonoscopy. The aim of this study was to investigate feasibility and safety of a novel CADe system during real-time colonoscopy in three European tertiary referral centers.MethodsNinety patients undergoing colonoscopy assisted by a real-time CADe system (DISCOVERY; Pentax Medical, Tokyo, Japan) were prospectively included. The CADe system was turned on only at withdrawal, and its output was displayed on secondary monitor. To study feasibility, inspection time, polyp detection rate (PDR), adenoma detection rate (ADR), sessile serrated lesion (SSL) detection rate (SDR), and the number of false positives were recorded. To study safety, (severe) adverse events ((S)AEs) were collected. Additionally, user friendliness was rated from 1 (worst) to 10 (best) by endoscopists.ResultsMean inspection time was 10.8 ± 4.3 min, while PDR was 55.6%, ADR 28.9%, and SDR 11.1%. The CADe system users estimated that < 20 false positives occurred in 81 colonoscopy procedures (90%). No (S)AEs related to the CADe system were observed during the 30-day follow-up period. User friendliness was rated as good, with a median score of 8/10.ConclusionColonoscopy with this novel CADe system in a real-time setting was feasible and safe. Although PDR and SDR were high compared to previous studies with other CADe systems, future randomized controlled trials are needed to confirm these detection rates. The high SDR is of particular interest since interval CRC has been suggested to develop frequently through the serrated neoplasia pathway.Clinical Trial RegistrationThe study was registered in the Dutch Trial Register (reference number: NL8788).

UPolySeg: A U-Net-Based Polyp Segmentation Network Using Colonoscopy Images

Colonoscopy is a gold standard procedure for tracking the lower gastrointestinal region. A colorectal polyp is one such condition that is detected through colonoscopy. Even though technical advancements have improved the early detection of colorectal polyps, there is still a high percentage of misses due to various factors. Polyp segmentation can play a significant role in the detection of polyps at the early stage and can thus help reduce the severity of the disease. In this work, the authors implemented several image pre-processing techniques such as coherence transport and contrast limited adaptive histogram equalization (CLAHE) to handle different challenges in colonoscopy images. The processed image was then segmented into a polyp and normal pixel using a U-Net-based deep learning segmentation model named UPolySeg. The main framework of UPolySeg has an encoder–decoder section with feature concatenation in the same layer as the encoder–decoder along with the use of dilated convolution. The model was experimentally verified using the publicly available Kvasir-SEG dataset, which gives a global accuracy of 96.77%, a dice coefficient of 96.86%, an IoU of 87.91%, a recall of 95.57%, and a precision of 92.29%. The new framework for the polyp segmentation implementing UPolySeg improved the performance by 1.93% compared with prior work.

AFP-Mask: Anchor-Free Polyp Instance Segmentation in Colonoscopy.

Colorectal cancer (CRC) is a common and lethal disease. Globally, CRC is the third most commonly diagnosed cancer in males and the second in females. The most effective way to prevent CRC is through using colonoscopy to identify and remove precancerous growths at an early stage. The detection and removal of colorectal polyps have been found to be associated with a reduction in mortality from colorectal cancer. However, the false negative rate of polyp detection during colonoscopy is often high even for experienced physicians. With recent advances in deep learning based object detection techniques, automated polyp detection shows great potential in helping physicians reduce false positive rate during colonoscopy. In this paper, we propose a novel anchor-free instance segmentation framework that can localize polyps and produce the corresponding instance level masks without using predefined anchor boxes. Our framework consists of two branches: (a) an object detection branch that performs classification and localization, (b) a mask generation branch that produces instance level masks. Instead of predicting a two-dimensional mask directly, we encode it into a compact representation vector, which allows us to incorporate instance segmentation with one-stage bounding-box detectors in a simple yet effective way. Moreover, our proposed encoding method can be trained jointly with object detector. Our experiment results show that our framework achieves a precision of 99.36% and a recall of 96.44% on public datasets, outperforming existing anchor-free instance segmentation methods by at least 2.8% in mIoU on our private dataset.

Mobile-PolypNet: Lightweight Colon Polyp Segmentation Network for Low-Resource Settings.

Colon polyps, small clump of cells on the lining of the colon, can lead to colorectal cancer (CRC), one of the leading types of cancer globally. Hence, early detection of these polyps automatically is crucial in the prevention of CRC. The deep learning models proposed for the detection and segmentation of colorectal polyps are resource-consuming. This paper proposes a lightweight deep learning model for colorectal polyp segmentation that achieved state-of-the-art accuracy while significantly reducing the model size and complexity. The proposed deep learning autoencoder model employs a set of state-of-the-art architectural blocks and optimization objective functions to achieve the desired efficiency. The model is trained and tested on five publicly available colorectal polyp segmentation datasets (CVC-ClinicDB, CVC-ColonDB, EndoScene, Kvasir, and ETIS). We also performed ablation testing on the model to test various aspects of the autoencoder architecture. We performed the model evaluation by using most of the common image-segmentation metrics. The backbone model achieved a DICE score of 0.935 on the Kvasir dataset and 0.945 on the CVC-ClinicDB dataset, improving the accuracy by 4.12% and 5.12%, respectively, over the current state-of-the-art network, while using 88 times fewer parameters, 40 times less storage space, and being computationally 17 times more efficient. Our ablation study showed that the addition of ConvSkip in the autoencoder slightly improves the model’s performance but it was not significant (p-value = 0.815).

Colon polyps in children

Aim: to evaluate the results of bowel polyposis in children. Patients and methods: the retrospective study included children, aged 1 to 17 years. The clinic manifestations, diagnostics and treatment approach are described. Patients with adenomatous polyposis (n = 38) and juvenile polyposis (n = 16) are presented in details. Results: options for surgical procedure are described due to early and late results, morphological data and diagnostic findings (colectomy with ileorectal anastomosis and formation of seromuscular cylinder, n = 8; colproctectomy with ileal pouch, n = 10). Conclusions: the detection of colorectal polyps requires a wide range of diagnostic and treatment approaches depends on a clear understanding of the nosological form of polyposis, which allows to determine the optimal period and method of surgical treatment.

AGA White Paper: Challenges and Gaps in Innovation for the Performance of Colonoscopy for Screening and Surveillance of Colorectal Cancer

In 2018, the American Gastroenterological Association's Center for GI Innovation and Technology convened a consensus conference, entitled "Colorectal Cancer Screening and Surveillance: Role of Emerging Technology and Innovation to Improve Outcomes." The conference participants, which included more than 60 experts in colorectal cancer, considered recent improvements in colorectal cancer screening rates and polyp detection, persistent barriers to colonoscopy uptake, and opportunities for performance improvement and innovation. This white paper originates from that conference. It aims to summarize current patient- and physician-centered gaps and challenges in colonoscopy, diagnostic and therapeutic challenges affecting colonoscopy uptake, and the potential use of emerging technologies and quality metrics to improve patient outcomes.

Impact of artificial intelligence on colorectal polyp detection for early-career endoscopists: an international comparative study

Background Artificial intelligence (AI) for polyp detection is being introduced to colonoscopy, but there is uncertainty how this affects endoscopists’ ability to detect polyps and neoplasms. We performed a video-based study to address whether AI improved the endoscopists’ performance to detect polyps. Methods We established a dataset of 200 colonoscopy videos (length 5 s; 100 without polyps and 100 with one polyp). About 33 early-career endoscopists (50–400 colonoscopies performed) from 10 European countries classified each video as either ‘polyp present’ or ‘polyp not present’. The video assessment was performed twice with a four-week interval. The first assessment was performed without any AI tool, whereas the second was performed with an AI tool for polyp detection. The primary endpoint was early-career endoscopists’ sensitivity to detect polyps. Gold standard for presence and histology of polyps were confirmed by two expert endoscopists and pathologists, respectively. McNemar’s test was used for statistical significance. Results There were 86 neoplastic and 14 non-neoplastic polyps (mean size 5.6 mm) in the 100 videos with polyps. Early-career endoscopists’ sensitivity to detect polyps increased from 86.3% (95% confidence interval [CI]: 85.1–87.5%) to 91.7% (95%CI: 90.7–92.6%) with the AI aid (p < .0001). Their sensitivity to detect neoplastic polyps increased from 85.4% (95% CI: 84.0–86.7%) to 92.1% (95%CI: 91.1–93.1%) with the AI aid (p < .0001). Conclusion The polyp detection AI tool helped early-career endoscopists to increase their sensitivity to identify all polyps and neoplastic polyps during colonoscopy.

Melanosis coli: A factor not associated with histological progression of colorectal polyps.

ObjectiveIn this study we aimed to investigate the association of melanosis coli (MC) and the colorectal polyp detection rate (PDR).MethodsIn all, 1104 MC patients and 62 181 non‐MC participants were enrolled. And 2208 controls were matched by participants' age and gender, and quality of bowel preparation using the propensity score matching (PSM) method. Additionally, 490 polyps in MC and 980 in controls matched by age and gender, and size and location of polyps were analyzed. The association of PDR and pathological features of polyps with MC were also analyzed.ResultsMC patients showed a higher PDR (44.3% vs 39.3%, P = 0.006) and detection rate of low‐grade adenoma (45.4% vs 36.7%, P = 0.002) but fewer large polyps (≥10 mm) (18.8% vs 26.9%, P = 0.001), fewer polyps in the left colon (33.5% vs 40.0%, P = 0.018), and a lower detection rate of advanced adenoma/adenocarcinoma (17.4% vs 24.3%, P = 0.003) than the matched controls. On multivariate logistic regression analysis, MC was independently associated with an increased PDR (odds ratio 1.184, 95% confidence interval 1.045–1.343, P = 0.008). Analysis targeting polyps showed that there were significant differences in age, gender, location, and pathology (P < 0.001) between polyps with and without MC. However, after adjusting for participants' age and gender, size and location of polyps, there was no difference between the two groups in pathology (P = 0.635).ConclusionMC is independently associated with increased colorectal PDR, but not with histological progression of polyps.

Strengths and Weaknesses of an Artificial Intelligence Polyp Detection Program as Assessed by a High-Detecting Endoscopist

Strengths and Weaknesses of an Artificial Intelligence Polyp Detection Program as Assessed by a High-Detecting Endoscopist

Diagnostic accuracy of ultra-low-dose CT colonography for the detection of colorectal polyps: a feasibility study.

The aim of this feasibility study was to evaluate the diagnostic accuracy of ultra-low-dose CT colonography using iterative reconstruction algorithms with reference to standard colonoscopy. Prior to this study, a phantom study was performed to investigate the optimal protocol for ultra-low-dose CT colonography. A total of 206 patients with average/high risk of colorectal cancer were recruited. After undergoing full bowel preparation, the patients were scanned in the prone and supine positions with the CT conditions set to 120kV, standard deviation 45 to 50, and an adaptive iterative reconstruction algorithm applied. Two expert readers read the images independently. The main outcome measures were the per-patient and per-polyp accuracies for the detection of polyps ≥ 10mm, with colonoscopy results as the reference standard. Two hundred patients (102 females, mean age 67.5years) underwent both ultra-low-dose CT colonography and colonoscopy on the same day. The mean radiation exposure dose was 0.64 ± 0.34mSv. On colonoscopy, 39 patients had 45 polyps ≥ 10mm (non-polypoid morphology 7), including 4 cancers. Per-patient sensitivity, specificity, and accuracy of CT colonography for polyps ≥ 10mm were 0.74, 0.96, and 0.92 for reader one, and 0.74, 0.99, and 0.94 for reader two, respectively. Per-polyp sensitivities for polyps ≥ 10mm were 0.73 for reader one and 0.71 for reader two. On subgroup analysis by morphology, non-polypoid polyps ≥ 10mm were not detected by both readers. Extreme ultra-low-dose CT colonography had an insufficient diagnostic performance for the detection of polyps ≥ 10mm, because it was unable to detect non-polypoid polyps. This study showed that the problem with ultra-low-dose CT colonography was the lack of detectability of small-size polyps, especially non-polypoid polyps. To use ultra-low-dose CT colonography clinically, it is necessary to resolve the problems identified by this study.

N-myristoyltransferase 2-based Blood Test for the Detection of Colorectal Adenomatous Polyps and Cancer.

Colorectal cancer is the second leading cause of cancer-related deaths. This study demonstrates the utility of a simple blood test with high sensitivity and specificity for colorectal adenomatous polyps and cancer. A simple blood test with high sensitivity and specificity for adenomas would help identify individuals for a follow-up colonoscopy during which any adenomatous polyps found could be removed, thus preventing colorectal cancer (CRC). We determined the H-score by using immunohistochemical analyses of N-myristoyltransferase 2 (NMT2) in peripheral blood mononuclear cells (PBMC) isolated from the blood. We determined the sensitivity and specificity of the NMT2-based blood test in identifying colorectal adenomatous polyps and cancer. All experimental procedures were performed by research personnel blinded to the colonoscopy status of the participants. In this cohort study, participants were recruited from those coming for an outpatient colonoscopy at a referral center. PBMC were collected from 74 subjects at the Health Sciences Centre, Winnipeg, Canada. Samples were collected from colonoscopy patients prior to colonoscopy. All 74 subjects were included in CRC vs. non-CRC analysis, whereas only 70 subjects were analyzed for colorectal adenomatous polyps and cancer versus individuals with no evidence of disease and non-adenomatous polyps. NMT2 expression was tested in samples by immunohistochemistry. The expression of NMT2 was significantly higher in PBMC of subjects with colorectal adenomatous polyps and cancer (n = 34) compared with individuals with non-adenomatous polyps or no evidence of disease (n = 36) (P < 0.0001). The test had an overall sensitivity of 91% (95% confidence intervals: 84.49-97.80) and specificity of 81% (95% confidence intervals: 71.28-89.83) in detecting colorectal adenomatous polyps and cancer (all stages). Our results suggest that the sensitivity of NMT2 in detecting adenomatous polyps is high (91%). A simple blood-based CRC screening test using NMT2 expression detects colorectal adenomatous polyps and cancer with high sensitivity and specificity has the potential of increasing the compliance for CRC screening as has been reported for other blood-based CRC screening tests.

Artificial intelligence–assisted detection of colorectal polyps in Lynch syndrome

A young patient with a familial history of Lynch syndrome presented for colonoscopy. She had undergone colonic polypectomy at another hospital 1 year previously. A computer-aided detection system (EX-1, Fujifilm Holdings Corp, Tokyo, Japan) was applied during colonoscopy in our hospital. When polyps that arouse suspicion are detected by this system, the lesions are indicated by cyan squares (A). On observation of the highlighted areas, the false positive indicators disappeared (B). Blue-light imaging revealed a regular vessel pattern, and computer-aided detection indicated that the detected polyp was a neoplastic lesion with a yellow mark on the position map (C).

Advanced Endoscopic Technologies to Improve the Diagnosis of Colorectal Polyps

AbstractColonoscopy is the gold standard for colorectal cancer (CRC) prevention. The main quality indicator of colonoscopy is the adenoma detection rate, which is inversely associated with the risk of interval CRC and the risk of death from this neoplasia. In the setting of CRC prevention, diagnostic colonoscopy has undergone a remarkable evolution in the past 20 years. Hand in hand with the implementation of CRC prevention programs and technological advances, we are now able to identify tiny and subtle neoplastic lesions and predict their histology with great efficiency. In this article, we briefly review the endoscopy technologies that can be used to improve the detection and characterization of colorectal polyps.

Asistenční systém pro detekci polypů v reálném čase na bázi konvoluční neuronové sítě

Summary: The use of artifi cial intelligence as an assistive detection method in endoscopy has attracted increasing interest in recent years. Machine learning algorithms promise to improve the effi ciency of polyp detection and even optical localization of fi ndings, all with minimal training of the endoscopist. The practical goal of this study is to analyse the CAD software (computer-aided dia gnosis) Carebot for colorectal polyp detection using a convolutional neural network. The proposed binary classifier for polyp detection achieves accuracy of up to 98%, specifi city of 0.99 and precision of 0.96. At the same time, the need for the availability of large-scale clinical data for the development of artifi cial- -intelligence-based models for the automatic detection of adenomas and benign neoplastic lesions is discussed. Key words: polyp detection – convolutional neural network – artifi cial intelligence – computer-aided dia gnosis – spatial location

Automated colorectal polyp detection based on image enhancement and dual-path CNN architecture

Colorectal Cancer (CRC) has the highest mortality rate of all cancers and is currently the third leading cause of cancer-related death worldwide. The early detection and diagnosis of colorectal polyps are necessary for early interventional therapies. The use of AI and ML techniques to analyse colonoscopy images has been gaining traction in recent years for early and accurate detection of polyps and other colorectal abnormalities. Existing deep learning classification and detection methods of polyps are computationally intensive, restrict memory potency, require extensive training, and affect the optimization of hyperparameters. This makes them unsuitable for real-time applications and applications with limited computing resources. This paper proposes a Dual-Path Convolutional Neural Network (DP-CNN) to classify polyp and non-polyp patches from the colonoscopy images. The proposed approach comprises image enhancement followed by the use of DP-CNN architecture and a sigmoid classifier for efficient detection of polyps. The publicly available database CVC ClinicDB is used to train the proposed network, and it is tested on ETIS-Larib and CVC ColonDB databases. The testing accuracy of the network on CVC ColonDB and ETIS-Larib are 99.60%, 90.81%, respectively. The performance measures are as follows: precision (100%), recall (99.20%), F1 score (99.60%) and F2 score (99.83%) on CVC ColonDB database and precision (89.81%), recall (92.85%), F1 score (91.00%) and F2 score (89.91%) on ETIS-Larib database. Compared with other existing methods, the proposed approach outperforms in precision, recall, F1-score, and F2-score in both databases. The number of learnable parameters of the proposed method is 8737. The proposed approach is promising as an accurate polyp detection technique. It is applicable for real-time applications due to lower complexity and fewer learnable parameters than required by other existing methods.

Artificial intelligence-assisted colonoscopy: A review of current state of practice and research.

Colonoscopy is an effective screening procedure in colorectal cancer prevention programs; however, colonoscopy practice can vary in terms of lesion detection, classification, and removal. Artificial intelligence (AI)-assisted decision support systems for endoscopy is an area of rapid research and development. The systems promise improved detection, classification, screening, and surveillance for colorectal polyps and cancer. Several recently developed applications for AI-assisted colonoscopy have shown promising results for the detection and classification of colorectal polyps and adenomas. However, their value for real-time application in clinical practice has yet to be determined owing to limitations in the design, validation, and testing of AI models under real-life clinical conditions. Despite these current limitations, ambitious attempts to expand the technology further by developing more complex systems capable of assisting and supporting the endoscopist throughout the entire colonoscopy examination, including polypectomy procedures, are at the concept stage. However, further work is required to address the barriers and challenges of AI integration into broader colonoscopy practice, to navigate the approval process from regulatory organizations and societies, and to support physicians and patients on their journey to accepting the technology by providing strong evidence of its accuracy and safety. This article takes a closer look at the current state of AI integration into the field of colonoscopy and offers suggestions for future research.

Artificial intelligence-assisted detection and classification of colorectal polyps under colonoscopy: a systematic review and meta-analysis.

BackgroundArtificial intelligence (AI) is used to solve the problem of missed diagnosis of polyps in colonoscopy, which has been proved to improve the detection rate of adenomas. The aim of this review was to evaluate the diagnostic performance of AI-assisted detection and classification of polyps in colonoscopy.MethodsThe literature search was undertaken on 4 electronic databases (PubMed, Web of Science, Embase, and Cochrane Library). The inclusion criteria were as follows: studies reporting AI-assisted detection and classification of polyps; studies containing patients, images, or videos receiving AI-assisted diagnosis; studies which included AI-assisted diagnosis and reported classification based on histopathology; and studies providing accurate diagnostic data. Non-English language studies, case-reports, reviews, meeting abstracts and so on were excluded. The Quality Assessment of Diagnostic Accuracy Studies-2 scale was used to evaluate the quality of literature and the Stata 13.0 software was used to perform meta-analysis.ResultsTwenty-six articles were included with all of medium quality. Meta-analysis showed none of literature had any obvious publication bias. The application of AI in detection of colorectal polyps achieved a sensitivity of 0.95 [95% confidence interval (CI): 0.89–0.98] and an area under the curve (AUC) of 0.79 (95% CI: 0.79–0.82). In the AI-assisted classification, the sensitivity was 0.92 (95% CI: 0.88–0.95) with a specificity of 0.82 (95% CI: 0.71–0.89) and an AUC of 0.94 (95% CI: 0.92–0.96). For the classification of diminutive polyps, the AI-assisted technique yielded a sensitivity of 0.95 (95% CI: 0.94–0.97), a specificity of 0.88 (95% CI: 0.74–0.95), and an AUC of 0.97 (95% CI: 0.95–0.98). For AI-assisted classification under magnifying endoscopy, the sensitivity was 0.954 (95% CI: 0.92–0.96) with a specificity of 0.95 (95% CI: 0.80–0.99) and an AUC of 0.97 (95% CI: 0.95–0.98).DiscussionThe AI-assisted technique demonstrates impressive accuracy for the detection and characterization of colorectal polyps and can be expected to be a novel auxiliary diagnosis method. Our study has inevitable limitations including heterogeneity due to different AI systems and the inability to further analyze the specificity and sensitivity of AI for different types of endoscopes.

Colorectal Cancer and Polyp Detection Using a New Preparation-Free, Colon-Scan Capsule: A Pilot Study of Safety and Patient Satisfaction.

Current strategies to prevent colorectal cancer (CRC) vary considerably regarding safety, invasiveness, and patient satisfaction. A known deterrent for patients is the required bowel cleansing for colonoscopy. A new colon-scan capsule system is a unique preparation-free approach that provides structural information on colonic mucosa intended for detection of colorectal polyps and masses. The aim of this study was to determine safety and patient satisfaction with the colon-scan capsule. Prospective single-arm pilot study conducted at two tertiary care centers. Patients with a pre-scheduled colonoscopy for CRC screening or surveillance were included. Patients participating in this study underwent the colon-scan capsule and colonoscopy. Safety was defined by the occurrence of procedure or device-related adverse events. Satisfaction was based on survey questionnaires using a scoring system 1 (strongly disagree) to 5 (strongly agree). Patient satisfaction with the colon-scan capsule was compared to colonoscopy. Forty patients were included (52.9 [5.7] years; 64.1% females). There were no serious adverse events and no occurrences of capsule retention. The most common (12.5%) complaint was self-limiting abdominal cramping. Satisfaction questionnaires were completed by more than 87% of patients, with patients likely to recommend the capsule (score 4.1 [1.03]) compared to colonoscopy (score 2.8 [1.2]), p = 0.001. The new prepless colon-scan capsule system is an innovative, minimally invasive technology with demonstrated safety and high patient satisfaction. A multicenter pivotal study is planned to validate the performance, safety, and accuracy of polyp detection using the capsule system in comparison with colonoscopy.

Deep learning for automated bowel preparation assessment during colonoscopy: time to embrace a new approach?

Some of the most translationally mature artificial intelligence (AI) applications in health care belong to colonoscopy, where algorithms can now be used in clinical practice to assist colorectal polyp detection and characterisation.1 The rapid pace of translation reflects the desperate need to identify solutions to drive up quality in colonoscopy and overcome the operator dependence associated with variable colorectal cancer protection. It is not surprising that AI developers are expanding research to further use cases applied to colonoscopy, particularly those that relate to performance measures and quality metrics.

Artificial intelligence application in diagnostic gastrointestinal endoscopy - Deus ex machina?

The close relationship of medicine with technology and the particular interest in this symbiosis in recent years has led to the development of several computed artificial intelligence (AI) systems aimed at various areas of medicine. A number of studies have demonstrated that those systems allow accurate diagnoses with histological precision, thus facilitating decision-making by clinicians in real time. In the field of gastroenterology, AI has been applied in the diagnosis of pathologies of the entire digestive tract and their attached glands, and are increasingly accepted for the detection of colorectal polyps and confirming their histological classification. Studies have shown high accuracy, sensitivity, and specificity in relation to expert endoscopists, and mainly in relation to those with less experience. Other applications that are increasingly studied and with very promising results are the investigation of dysplasia in patients with Barrett's esophagus and the endoscopic and histological assessment of colon inflammation in patients with ulcerative colitis. In some cases AI is thus better than or at least equal to human abilities. However, additional studies are needed to reinforce the existing data, and mainly to determine the applicability of this technology in other indications. This review summarizes the state of the art of AI in gastroenterological pathology.