Polyp Recognition Research Articles

ACU-TransNet: Attention and convolution-augmented UNet-transformer network for polyp segmentation.

UNet has achieved great success in medical image segmentation. However, due to the inherent locality of convolution operations, UNet is deficient in capturing global features and long-range dependencies of polyps, resulting in less accurate polyp recognition for complex morphologies and backgrounds. Transformers, with their sequential operations, are better at perceiving global features but lack low-level details, leading to limited localization ability. If the advantages of both architectures can be effectively combined, the accuracy of polyp segmentation can be further improved. In this paper, we propose an attention and convolution-augmented UNet-Transformer Network (ACU-TransNet) for polyp segmentation. This network is composed of the comprehensive attention UNet and the Transformer head, sequentially connected by the bridge layer. On the one hand, the comprehensive attention UNet enhances specific feature extraction through deformable convolution and channel attention in the first layer of the encoder and achieves more accurate shape extraction through spatial attention and channel attention in the decoder. On the other hand, the Transformer head supplements fine-grained information through convolutional attention and acquires hierarchical global characteristics from the feature maps. mcU-TransNet could comprehensively learn dataset features and enhance colonoscopy interpretability for polyp detection. Experimental results on the CVC-ClinicDB and Kvasir-SEG datasets demonstrate that mcU-TransNet outperforms existing state-of-the-art methods, showcasing its robustness.

Improving polygon image segmentation by enhancing U-Net architecture

The crucial task of polyp recognition in medical imaging plays a pivotal role in the early detection and prevention of colorectal cancer. Semantic segmentation, particularly utilizing sophisticated deep learning models such as U-Net, has demonstrated promising results in the realm of polyp segmentation. However, the traditional U-Net structure sometimes grapples with accurately delineating the edges of polyps, which subsequently impacts the overall performance of segmentation. To address this issue, the current study introduces a novel approach by proposing a modified framework of U-Net, equipped with an enhanced edge loss function. This function is designed to ameliorate the accuracy of segmentation within polyp images. The aim is to elevate the model’s capacity to capture intricate details, specifically the edges, which is an area where standard U-Net structures often falter. Experimental outcomes of this study serve to underscore the effectiveness of the proposed approach in accomplishing superior segmentation of edges and improved overall performance in polyp recognition. By successfully tackling the challenges inherent to polyp edge segmentation, the modified U-Net model contributes significantly towards more precise diagnostic systems in the field of medical imaging. Consequently, this research is poised to make a valuable contribution to advancements in the prevention and early detection of colorectal cancer.

Spatio-Temporal Feature Transformation Based Polyp Recognition for Automatic Detection: Higher Accuracy than Novice Endoscopists in Colorectal Polyp Detection and Diagnosis.

Artificial intelligence represents an emerging area with promising potential for improving colonoscopy quality. To develop a colon polyp detection model using STFT and evaluate its performance through a randomized sample experiment. Colonoscopy videos from the Digestive Endoscopy Center of the First Affiliated Hospital of Anhui Medical University, recorded between January 2018 and November 2022, were selected and divided into two datasets. To verify the model's practical application in clinical settings, 1500 colonoscopy images and 1200 polyp images of various sizes were randomly selected from the test set and compared with the STFT model's and endoscopists' recognition results with different years of experience. In the randomized sample trial involving 1500 colonoscopy images, the STFT model demonstrated significantly higher accuracy and specificity compared to endoscopists with low years of experience (0.902 vs. 0.809, 0.898 vs. 0.826, respectively). Moreover, the model's sensitivity was 0.904, which was higher than that of endoscopists with low, medium, or high years of experience (0.80, 0.896, 0.895, respectively), with statistical significance (P < 0.05). In the randomized sample experiment of 1200 polyp images of different sizes, the accuracy of the STFT model was significantly higher than that of endoscopists with low years of experience when the polyp size was ≤ 0.5cm and 0.6-1.0cm (0.902 vs. 0.70, 0.953 vs. 0.865, respectively). The STFT-based colon polyp detection model exhibits high accuracy in detecting polyps in colonoscopy videos, with a particular efficiency in detecting small polyps (≤ 0.5cm)(0.902 vs. 0.70, P < 0.001).

Evaluation of the competence of an artificial intelligence-assisted colonoscopy system in clinical practice: A post hoc analysis.

Artificial intelligence-assisted colonoscopy (AIAC) has been proposed and validated in recent years, but the effectiveness of clinic application remains unclear since it was only validated in some clinical trials rather than normal conditions. In addition, previous clinical trials were mostly concerned with colorectal polyp identification, while fewer studies are focusing on adenoma identification and polyps size measurement. In this study, we validated the effectiveness of AIAC in the clinical environment and further investigated its capacity for adenoma identification and polyps size measurement. The information of 174 continued patients who went for coloscopy in Chongqing Rongchang District People's hospital with detected colon polyps was retrospectively collected, and their coloscopy images were divided into three validation datasets, polyps dataset, polyps/adenomas dataset (all containing narrow band image, NBI images), and polyp size measurement dataset (images with biopsy forceps and polyps) to assess the competence of the artificial intelligence system, and compare its diagnostic ability with endoscopists with different experiences. A total of 174 patients were included, and the sensitivity of the colorectal polyp recognition model was 99.40%, the accuracy of the colorectal adenoma diagnostic model was 93.06%, which was higher than that of endoscopists, and the mean absolute error of the polyp size measurement model was 0.62 mm and the mean relative error was 10.89%, which was lower than that of endoscopists. Artificial intelligence-assisted model demonstrated higher competence compared with endoscopists and stable diagnosis ability in clinical use.

Identifying key mechanisms leading to visual recognition errors for missed colorectal polyps using eye-tracking technology.

Lack of visual recognition of colorectal polyps may lead to interval cancers. The mechanisms contributing to perceptual variation, particularly for subtle and advanced colorectal neoplasia, have scarcely been investigated. We aimed to evaluate visual recognition errors and provide novel mechanistic insights. Eleven participants (seven trainees and four medical students) evaluated images from the UCL polyp perception dataset, containing 25 polyps, using eye-tracking equipment. Gaze errors were defined as those where the lesion was not observed according to eye-tracking technology. Cognitive errors occurred when lesions were observed but not recognized as polyps by participants. A video study was also performed including 39 subtle polyps, where polyp recognition performance was compared with a convolutional neural network. Cognitive errors occurred more frequently than gaze errors overall (65.6%), with a significantly higher proportion in trainees (P=0.0264). In the video validation, the convolutional neural network detected significantly more polyps than trainees and medical students, with per-polyp sensitivities of 79.5%, 30.0%, and 15.4%, respectively. Cognitive errors were the most common reason for visual recognition errors. The impact of interventions such as artificial intelligence, particularly on different types of perceptual errors, needs further investigation including potential effects on learning curves. To facilitate future research, a publicly accessible visual perception colonoscopy polyp database was created.

Automated Endoscopic Image Classification via Deep Neural Network With Class Imbalance Loss

Recently, many computer-aided diagnosis (CAD) methods have been proposed to help physicians automatically classify endoscopic images. However, most existing methods often result in poor performance, especially for the minority classes, when the dataset is imbalanced. In this paper, we propose a new CAD method for automated endoscopic image classification by introducing a novel class imbalance (CI) loss to the classical deep neural network (DNN). Specifically, we use the DNN to extract rich feature representations. Given that the majority class usually dominates the prediction error and influences the gradient of the network, the proposed CI loss considers both the class frequency and prediction probability of the ground-truth class to assign the weight of each sample and helps the minority classes contribute more to descending the gradient in the training process than the majority classes. Thanks to the CI loss, the network pays more attention to minority classes and hard samples. To verify the effectiveness of our proposed method, we conduct comprehensive experiments in the binary-class classification task on our collected polyp recognition dataset (22,935 images) and in the multi-class classification task on the public Hyper-Kvasir dataset (10,662 images). Experimental results show that our method is competent for the imbalanced endoscopic image classification task with good performance.

Performance and comparison of artificial intelligence and human experts in the detection and classification of colonic polyps

ObjectiveThe main aim of this study was to analyze the performance of different artificial intelligence (AI) models in endoscopic colonic polyp detection and classification and compare them with doctors with different experience.MethodsWe searched the studies on Colonoscopy, Colonic Polyps, Artificial Intelligence, Machine Learning, and Deep Learning published before May 2020 in PubMed, EMBASE, Cochrane, and the citation index of the conference proceedings. The quality of studies was assessed using the QUADAS-2 table of diagnostic test quality evaluation criteria. The random-effects model was calculated using Meta-DISC 1.4 and RevMan 5.3.ResultsA total of 16 studies were included for meta-analysis. Only one study (1/16) presented externally validated results. The area under the curve (AUC) of AI group, expert group and non-expert group for detection and classification of colonic polyps were 0.940, 0.918, and 0.871, respectively. AI group had slightly lower pooled specificity than the expert group (79% vs. 86%, P < 0.05), but the pooled sensitivity was higher than the expert group (88% vs. 80%, P < 0.05). While the non-experts had less pooled specificity in polyp recognition than the experts (81% vs. 86%, P < 0.05), and higher pooled sensitivity than the experts (85% vs. 80%, P < 0.05).ConclusionThe performance of AI in polyp detection and classification is similar to that of human experts, with high sensitivity and moderate specificity. Different tasks may have an impact on the performance of deep learning models and human experts, especially in terms of sensitivity and specificity.

ColoRectalCADx: Expeditious Recognition of Colorectal Cancer with Integrated Convolutional Neural Networks and Visual Explanations Using Mixed Dataset Evidence.

Colorectal cancer typically affects the gastrointestinal tract within the human body. Colonoscopy is one of the most accurate methods of detecting cancer. The current system facilitates the identification of cancer by computer-assisted diagnosis (CADx) systems with a limited number of deep learning methods. It does not imply the depiction of mixed datasets for the functioning of the system. The proposed system, called ColoRectalCADx, is supported by deep learning (DL) models suitable for cancer research. The CADx system comprises five stages: convolutional neural networks (CNN), support vector machine (SVM), long short-term memory (LSTM), visual explanation such as gradient-weighted class activation mapping (Grad-CAM), and semantic segmentation phases. Here, the key components of the CADx system are equipped with 9 individual and 12 integrated CNNs, implying that the system consists mainly of investigational experiments with a total of 21 CNNs. In the subsequent phase, the CADx has a combination of CNNs of concatenated transfer learning functions associated with the machine SVM classification. Additional classification is applied to ensure effective transfer of results from CNN to LSTM. The system is mainly made up of a combination of CVC Clinic DB, Kvasir2, and Hyper Kvasir input as a mixed dataset. After CNN and LSTM, in advanced stage, malignancies are detected by using a better polyp recognition technique with Grad-CAM and semantic segmentation using U-Net. CADx results have been stored on Google Cloud for record retention. In these experiments, among all the CNNs, the individual CNN DenseNet-201 (87.1% training and 84.7% testing accuracies) and the integrated CNN ADaDR-22 (84.61% training and 82.17% testing accuracies) were the most efficient for cancer detection with the CNN+LSTM model. ColoRectalCADx accurately identifies cancer through individual CNN DesnseNet-201 and integrated CNN ADaDR-22. In Grad-CAM's visual explanations, CNN DenseNet-201 displays precise visualization of polyps, and CNN U-Net provides precise malignant polyps.

Colorectal polyp region extraction using saliency detection network with neutrosophic enhancement

Colorectal polyp recognition is crucial for early colorectal cancer detection and treatment. Colonoscopy is always employed for colorectal polyp scanning. However, one out of four polyps may be ignored, due to the similarity of polyp and normal tissue. In this paper, we present a novel method called NeutSS-PLP for polyp region extraction in colonoscopy images using a short connected saliency detection network with neutrosophic enhancement. We first utilize the neutrosophic theory to enhance the quality of specular reflections detection in the colonoscopy images. We develop the local and global threshold criteria in the single-valued neutrosophic set (SVNS) domain and define the corresponding T (Truth), I (Indeterminacy), and F (Falsity) functions for each criterion. The well-built neutrosophic images are processed and employed for specular reflection detection and suppressing. Next, we introduce two-level short connections into the saliency detection network, aiming to take advantage of the multi-level and multi-scale features extracted from different stages of the network. Experimental results conducted on two public colorectal polyp datasets achieve 0.877 and 0.9135 mIoU for polyp extraction respectively, and our method performs better compared with several state-of-the-art saliency networks and semantic segmentation networks, which demonstrate the effectiveness of applying the saliency detection mechanism for colorectal polyp region extraction.

Diagnostic Accuracy of Wireless Capsule Endoscopy in Polyp Recognition Using Deep Learning: A Meta-Analysis.

Aim As the completed studies have small sample sizes and different algorithms, a meta-analysis was conducted to assess the accuracy of WCE in identifying polyps using deep learning. Method Two independent reviewers searched PubMed, Embase, the Web of Science, and the Cochrane Library for potentially eligible studies published up to December 8, 2021, which were analysed on a per-image basis. STATA RevMan and Meta-DiSc were used to conduct this meta-analysis. A random effects model was used, and a subgroup and regression analysis was performed to explore sources of heterogeneity. Results Eight studies published between 2017 and 2021 included 819 patients, and 18,414 frames were eventually included in the meta-analysis. The summary estimates for the WCE in identifying polyps by deep learning were sensitivity 0.97 (95% confidence interval (CI), 0.95–0.98); specificity 0.97 (95% CI, 0.94–0.98); positive likelihood ratio 27.19 (95% CI, 15.32–50.42); negative likelihood ratio 0.03 (95% CI 0.02–0.05); diagnostic odds ratio 873.69 (95% CI, 387.34–1970.74); and the area under the sROC curve 0.99. Conclusion WCE uses deep learning to identify polyps with high accuracy, but multicentre prospective randomized controlled studies are needed in the future.

Colored Video Analysis in Wireless Capsule Endoscopy: A Survey of State-of-the-Art.

Wireless Capsule Endoscopy (WCE) is a highly promising technology for gastrointestinal (GI) tract abnormality diagnosis. However, low image resolution and low frame rates are challenging issues in WCE. In addition, the relevant frames containing the features of interest for accurate diagnosis only constitute 1% of the complete video information. For these reasons, analyzing the WCE videos is still a time consuming and laborious examination for the gastroenterologists, which reduces WCE system usability. This leads to the emergent need to speed-up and automates the WCE video process for GI tract examinations. Consequently, the present work introduced the concept of WCE technology, including the structure of WCE systems, with a focus on the medical endoscopy video capturing process using image sensors. It discussed also the significant characteristics of the different GI tract for effective feature extraction. Furthermore, video approaches for bleeding and lesion detection in the WCE video were reported with computer-aided diagnosis systems in different applications to support the gastroenterologist in the WCE video analysis. In image enhancement, WCE video review time reduction is also discussed, while reporting the challenges and future perspectives, including the new trend to employ the deep learning models for feature Learning, polyp recognition, and classification, as a new opportunity for researchers to develop future WCE video analysis techniques.

Intestinal Polyp Recognition Based on Salient Codebook Locality-Constrained Linear Coding with Annular Spatial Pyramid Matching

Gastrointestinal endoscopy has become a widely used technique to diagnose intestinal anomalies like polyp. However, the vast quantities of images produced in the detection process greatly increase the burden of clinicians and the misdiagnosis rate. To tackle this problem, a new computer-assisted detection system called Salient Codebook Locality-Constrained Linear Coding (SCLLC) with Annular Spatial Pyramid Matching (ASPM) is proposed in this paper to classify intestinal polyp images automatically. Firstly, SIFT features are extracted from images and k-means clustering method is utilized on the patch features to obtain the initial codebook. Secondly, the proposed SCLLC algorithm is employed to achieve the salient codebook and encode the features by emphasizing the salient basis vectors in feature coding process. Then, a max-pooling strategy of annular region segmentation based on Spatial Pyramid Matching is proposed to improve the effectiveness of processing for intestinal images. Finally, SVM classifier is developed to carry out polyp images classification tasks. The experimental results exhibit promising 94.10% accuracy, 91.20% sensitivity and 97.01% specificity. In addition, the 0.97 s detecting time indicates the feasibility of clinical application. Our proposed method can effectively improve the overall performance of intestinal polyp recognition by integrating the internal base vector correlation of codebook and considering the annular structure of the intestinal images.

Automatic Polyp Recognition in Colonoscopy Images Using Deep Learning and Two-Stage Pyramidal Feature Prediction

Polyp recognition in colonoscopy images is crucial for early colorectal cancer detection and treatment. However, the current manual review requires undivided concentration of the gastroenterologist and is prone to diagnostic errors. In this article, we present an effective, two-stage approach called PLPNet, where the abbreviation “PLP” stands for the word “polyp,” for automated pixel-accurate polyp recognition in colonoscopy images using very deep convolutional neural networks (CNNs). Compared to hand-engineered approaches and previous neural network architectures, our PLPNet model improves recognition accuracy by adding a polyp proposal stage that predicts the location box with polyp presence. Several schemes are proposed to ensure the model’s performance. First of all, we construct a polyp proposal stage as an extension of the faster R-CNN, which performs as a region-level polyp detector to recognize the lesion area as a whole and constitutes stage I of PLPNet. Second, stage II of PLPNet is built in a fully convolutional fashion for pixelwise segmentation. We define a feature sharing strategy to transfer the learned semantics of polyp proposals to the segmentation task of stage II, which is proven to be highly capable of guiding the learning process and improve recognition accuracy. Additionally, we design skip schemes to enrich the feature scales and thus allow the model to generate detailed segmentation predictions. For accurate recognition, the advanced residual nets and feature pyramids are adopted to seek deeper and richer semantics at all network levels. Finally, we construct a two-stage framework for training and run our model convolutionally via a single-stream network at inference time to efficiently output the polyp mask. Experimental results on public data sets of GIANA Challenge demonstrate the accuracy gains of our approach, which surpasses previous state-of-the-art methods on the polyp segmentation task (74.7 Jaccard Index) and establishes new top results in the polyp localization challenge (81.7 recall). Note to Practitioners —Given the current manual review of colonoscopy is laborious and time-consuming, computational methods that can assist automatic polyp recognition will enhance the outcome both in terms of efficiency and diagnostic accuracy of colonoscopy. This article suggests a new approach using a very deep convolutional neural network (CNN) architecture for polyp recognition, which gains accuracy from deeper and richer representations. The method, called PLPNet, can effectively detect polyps in colonoscopy images and generate high-quality segmentation masks in a pixel-to-pixel manner. We evaluate the proposed framework on publicly available data sets, and we show by experiments that our method surpasses the state-of-the-art polyp recognition results. The finding of this article corroborates that CNNs with very deep architecture and richer semantics are highly efficient in medical image learning and inference. We believe that the proposed method will facilitate potential computer-aided applications in clinical practice, in that it can enhance medical decision-making in cancer detection and imaging.

Wireless Capsule Endoscopy: A New Tool for Cancer Screening in the Colon With Deep-Learning-Based Polyp Recognition

Accurate recognition of polyps is crucial for early colorectal cancer diagnosis and treatment. Wireless capsule endoscopy (WCE) is a noninvasive, wireless imaging tool that allows direct visualization of the entire colon without discomfort to patients and has the potential to revolutionize the screening workup for colorectal diseases. However, current manual review is laborious and time consuming, requiring the undivided concentration of the gastroenterologist. Computational methods that can assist automated polyp recognition will enhance the outcome both in terms of diagnostic accuracy and efficiency of WCE. This review introduces the computer-assisted algorithms as applied to colorectal polyp screening, focusing on the successes of deep-learning-based strategies in the WCE sequences. We survey key applications of WCE polyp recognition, covering deep-learning-based image-level classification, lesion region detection, and pixel-accurate segmentation. We conclude by discussing emerging research challenges, possible trends, and future directions.

Densely Connected Neural Network With Unbalanced Discriminant and Category Sensitive Constraints for Polyp Recognition

Automatic polyp recognition in endoscopic images is challenging because of the low contrast between polyps and the surrounding area, the fuzzy and irregular polyp borders, and varying imaging light conditions. In this article, we propose a novel densely connected convolutional network with “unbalanced discriminant (UD)” loss and “category sensitive (CS)” loss (DenseNet-UDCS) for the task. We first utilize densely connected convolutional network (DenseNet) as the basic framework to conduct end-to-end polyp recognition task. Then, the proposed dual constraints, UD loss and CS loss, are simultaneously incorporated into the DenseNet model to calculate discriminative and suitable image features. The UD loss in our network effectively captures classification errors from both majority and minority categories to deal with the strong data imbalance of polyp images and normal ones. The CS loss imposes the ratio of intraclass and interclass variations in the deep feature learning process to enable features with large interclass variation and small intraclass compactness. With the joint supervision of UD loss and CS loss, a robust DenseNet-UDCS model is trained to recognize polyps from endoscopic images. The experimental results achieved polyp recognition accuracy of 93.19%, showing that the proposed DenseNet-UDCS can accurately characterize the endoscopic images and recognize polyps from the images. In addition, our DenseNet-UDCS model is superior in detection accuracy in comparison with state-of-the-art polyp recognition methods. Note to Practitioners —Wireless capsule endoscopy (WCE) is a crucial diagnostic tool for polyp detection and therapeutic monitoring, thanks to its noninvasive, user-friendly, and nonpainful properties. A challenge in harnessing the enormous potential of the WCE to benefit the gastrointestinal (GI) patients is that it requires clinicians to analyze a huge number of images (about 50 000 images for each patient). We propose a novel automatic polyp recognition scheme, namely, DenseNet-UDCS model, by addressing practical image unbalanced problem and small interclass variances and large intraclass differences in the data set. The comprehensive experimental results demonstrate superior reliability and robustness of the proposed model compared to the other polyp recognition approaches. Our DenseNet-UDCS model can be further applied in the clinical practice to provide valuable diagnosis information for GI disease recognition and precision medicine.

A locally based feature descriptor for abnormalities detection

Wireless capsule endoscopy (WCE) is a novel imaging technique that can view the entire small bowel in human body. Therefore, it has been gradually adopted compared with traditional endoscopies for gastrointestinal diseases. However, the task of reviewing the vast amount of images produced by a WCE test is exhaustive for the physicians. This paper presents a new feature extraction scheme for pathological inflammation and ulcer regions discrimination in WCE images. In addition, the novel approach is adopted for polyp recognition in colonoscopy videos. A novel idea based on extracting certain local features from the image is proposed. Then, the occurrence histogram of these features is used as descriptor of the image. The new feature descriptor scheme is grayscale rotation invariant and computationally simple as the operator can be realized with a few operations in a small neighborhood. The proposed operator does not discard the contrast information. Besides, we propose to test the quality of the model using logarithmic loss metric and show how calibration can be useful in reducing the aforementioned measure. Extensive classification experiments have been applied on different datasets, which prove that the occurrence histogram of the extracted features is powerful. The proposed method achieved 99.1%, 99.7% and 99.2% in terms of the precision in the first, second and third datasets, respectively, and surpassed some known local descriptors on a texture dataset.

Automatic Polyp Recognition from Colonoscopy Images Based on Bag of Visual Words

ABSTRACT Colorectal cancer (CRC) is one of the most popular cancer in the world. Adenoma and sessile serrated polyp precursor lesions claim over 95% of CRC. The incidence of CRC is reduced 76-90% through the early diagnosis and removal of colorectal polyps. Colonoscopy is the golden standard for the detection of colorectal polyps but about 25% of polyps were missed during colonoscopy examinations. In this study, we proposed a novel method to recognize polyps from colonoscopy images based on bag-of-visual-words (BoW) with extracted regions of interest. The proposed method generates a histogram of visual word occurrences to represent an image, and uses support vector machine (SVM) with error correcting output codes (ECOC) for the detection of polyps. A dataset composed of 131 cases’ clinical data were used to train and test the proposed method. Validation demonstrates an average specificity of 97.8±1.5%, an average sensitivity of 97.2±1.7%, and an average accuracy of 97.5±1.0%.

Deep learning for polyp recognition in wireless capsule endoscopy images.

Wireless capsule endoscopy (WCE) enables physicians to examine the digestive tract without any surgical operations, at the cost of a large volume of images to be analyzed. In the computer-aided diagnosis of WCE images, the main challenge arises from the difficulty of robust characterization of images. This study aims to provide discriminative description of WCE images and assist physicians to recognize polyp images automatically. We propose a novel deep feature learning method, named stacked sparse autoencoder with image manifold constraint (SSAEIM), to recognize polyps in the WCE images. Our SSAEIM differs from the traditional sparse autoencoder (SAE) by introducing an image manifold constraint, which is constructed by a nearest neighbor graph and represents intrinsic structures of images. The image manifold constraint enforces that images within the same category share similar learned features and images in different categories should be kept far away. Thus, the learned features preserve large intervariances and small intravariances among images. The average overall recognition accuracy (ORA) of our method for WCE images is 98.00%. The accuracies for polyps, bubbles, turbid images, and clear images are 98.00%, 99.50%, 99.00%, and 95.50%, respectively. Moreover, the comparison results show that our SSAEIM outperforms existing polyp recognition methods with relative higher ORA. The comprehensive results have demonstrated that the proposed SSAEIM can provide descriptive characterization for WCE images and recognize polyps in a WCE video accurately. This method could be further utilized in the clinical trials to help physicians from the tedious image reading work.

Assessment of colorectal polyp recognition skill: development and validation of an objective test.

The quality of colonoscopy is known to vary. The extent to which colonoscopists can recognize the presence of subtle colorectal lesions by visually distinguishing them from the surrounding mucosa (i.e., polyp recognition skill) may be one of several attributes that influence polyp detection rates. The aim of the present study was to develop and validate the first objective test of polyp recognition skill. Validation study. Twenty-eight experienced colonoscopists and eighty novices took a preliminary 280-item computer-based polyp recognition test. Items were genuine endoscopic images which participants assessed for the presence of "likely polyps." Half included clinically identified polyps. Participants clicked on a suspected lesion or a button marked "no likely polyp", and the main outcome measures were accuracy and response latency. The best items were selected for the final 50-item test. In the preliminary test, experienced colonoscopists correctly identified more polyps than novices (P<.0001) and better discriminated between clinically identified polyps and non-polyp features (as measured by d', P<.0001). For polyp items, the experienced group also responded faster (P<.01). Effect sizes were large for accuracy (Cohen's d=3.22) and d' (Cohen's d=3.22). The 50 final test items produced comparable results for accuracy, d', and response latency. For both versions of the test, score scale reliability was high for both polyp and non-polyp items (α=.82 to .97). The observed experienced-novice differences support the construct validity of the performance measures derived from the tests, indicating that polyp recognition skill can be quantified objectively. The final test may potentially be used to assess trainees, but test sensitivity may be insufficient to make fine-grained distinctions between different skill levels among experienced colonoscopists. More sensitive future tests may provide a valuable supplement to clinical detection rates, allowing objective comparisons between skilled colonoscopists.

A prospective dual-center proof-of-principle study evaluating the incremental benefit of narrow-band imaging with a fixed zoom function in real-time prediction of polyp histology. Can we resect and discard?

Advancements in endoscopic technology have increased the ability to distinguish neoplastic polyps during colonoscopy. If a minimum accuracy can be achieved, then a resect-and-discard model can be implemented, although studies to date have demonstrated limited success, especially in the assessment of serrated polyps. To perform a proof-of-principle study assessing the accuracy of narrow-band imaging with near focusin predicting polyp histology including serrated polyps and to determine whether the minimum requirements can be achieved for a resect-and-discard policy. Dual-center, prospective case series. Two tertiary-care referral endoscopic centers in Australia. Two hundred consecutive patients undergoing colonoscopy for routine indications were recruited. Any polyps identified were assessed by using standard white light followed by narrow-band imaging with near focus for Kudo pit patterns and modified Sano capillary patterns. Based on this assessment and the macroscopic appearance, the polyp histology was predicted and subsequently compared with histopathology results. Correlation in postpolypectomy surveillance intervals between endoscopic and pathologic assessments as well as negative predictive value for rectosigmoid hyperplastic polyps. There was a 96% agreement for surveillance intervals between endoscopic assessment and histology by using the American Society for Gastrointestinal Endoscopy guidelines. There was a 96% negative predictive value in assessing rectosigmoid hyperplastic polyps. Because this was a proof-of-principle study, there was no control arm, and there were small numbers, especially in assessing subgroups. The results have limited generalizability with the training requirements for polyp recognition, with confidence to be determined. Narrow-band imaging with near focus can predict polyp histology (including serrated polyps) accurately in the hands of trained endoscopists. Further studies with larger numbers are required to further validate this practice.