Accuracy Of Polyp Detection Research Articles

Impact of User's Background Knowledge and Polyp Characteristics in Colonoscopy with Computer-Aided Detection.

We investigated how interactions between humans and computer-aided detection (CADe) systems are influenced by the user's experience and polyp characteristics. We developed a CADe system using YOLOv4, trained on 16,996 polyp images from 1,914 patients and 1,800 synthesized sessile serrated lesion (SSL) images. The performance of polyp detection with CADe assistance was evaluated using a computerized test module. Eighteen participants were grouped by colonoscopy experience (nurses, fellows, and experts). The value added by CADe based on the histopathology and detection difficulty of polyps were analyzed. The area under the curve for CADe was 0.87 (95% confidence interval [CI], 0.83 to 0.91). CADe assistance increased overall polyp detection accuracy from 69.7% to 77.7% (odds ratio [OR], 1.88; 95% CI, 1.69 to 2.09). However, accuracy decreased when CADe inaccurately detected a polyp (OR, 0.72; 95% CI, 0.58 to 0.87). The impact of CADe assistance was most and least prominent in the nurses (OR, 1.97; 95% CI, 1.71 to 2.27) and the experts (OR, 1.42; 95% CI, 1.15 to 1.74), respectively. Participants demonstrated better sensitivity with CADe assistance, achieving 81.7% for adenomas and 92.4% for easy-to-detect polyps, surpassing the standalone CADe performance of 79.7% and 89.8%, respectively. For SSLs and difficult-to-detect polyps, participants' sensitivities with CADe assistance (66.5% and 71.5%, respectively) were below those of standalone CADe (81.1% and 74.4%). Compared to the other two groups (56.1% and 61.7%), the expert group showed sensitivity closest to that of standalone CADe in detecting SSLs (79.7% vs 81.1%, respectively). CADe assistance boosts polyp detection significantly, but its effectiveness depends on the user's experience, particularly for challenging lesions.

An efficient real-time colonic polyp detection with YOLO algorithms trained by using negative samples and large datasets

Colorectal cancer (CRC) is one of the common types of cancer with a high mortality rate. Colonoscopy is the gold standard for CRC screening and significantly reduces CRC mortality. However, due to many factors, the rate of missed polyps, which are the precursors of colorectal cancer, is high in practice. Therefore, many artificial intelligence-based computer-aided diagnostic systems have been presented to increase the detection rate of missed polyps. In this article, we present deep learning-based methods for reliable computer-assisted polyp detection. The proposed methods differ from state-of-the-art methods as follows. First, we improved the performances of YOLOv3 and YOLOv4 object detection algorithms by integrating Cross Stage Partial Network (CSPNet) for real-time and high-performance automatic polyp detection. Then, we utilized advanced data augmentation techniques and transfer learning to improve the performance of polyp detection. Next, for further improving the performance of polyp detection using negative samples, we substituted the Sigmoid-weighted Linear Unit (SiLU) activation functions instead of the Leaky ReLU and Mish activation functions, and Complete Intersection over Union (CIoU) as the loss function. In addition, we present a comparative analysis of these activation functions for polyp detection. We applied the proposed methods on the recently published novel datasets, which are the SUN polyp database and the PICCOLO database. Additionally, we investigated the proposed models for MICCAI Sub-Challenge on Automatic Polyp Detection in Colonoscopy dataset. The proposed methods outperformed the other studies in both real-time performance and polyp detection accuracy.

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.

Colorectal Polyp Image Detection and Classification through Grayscale Images and Deep Learning.

Colonoscopy screening and colonoscopic polypectomy can decrease the incidence and mortality rate of colorectal cancer (CRC). The adenoma detection rate and accuracy of diagnosis of colorectal polyp which vary in different experienced endoscopists have impact on the colonoscopy protection effect of CRC. The work proposed a colorectal polyp image detection and classification system through grayscale images and deep learning. The system collected the data of CVC-Clinic and 1000 colorectal polyp images of Linkou Chang Gung Medical Hospital. The red-green-blue (RGB) images were transformed to 0 to 255 grayscale images. Polyp detection and classification were performed by convolutional neural network (CNN) model. Data for polyp detection was divided into five groups and tested by 5-fold validation. The accuracy of polyp detection was 95.1% for grayscale images which is higher than 94.1% for RGB and narrow-band images. The diagnostic accuracy, precision and recall rates were 82.8%, 82.5% and 95.2% for narrow-band images, respectively. The experimental results show that grayscale images achieve an equivalent or even higher accuracy of polyp detection than RGB images for lightweight computation. It is also found that the accuracy of polyp detection and classification is dramatically decrease when the size of polyp images small than 1600 pixels. It is recommended that clinicians could adjust the distance between the lens and polyps appropriately to enhance the system performance when conducting computer-assisted colorectal polyp analysis.

Factors influencing the miss rate of polyps in a tandem colonoscopy study

The miss rate of colon polyps and its related factors have not been clearly identified yet. This study aims to review the miss rate of polyps both on the patient-level and on the polyp-level and to analyze the factors affecting the miss rate such as those related to the endoscopist, procedure, patient, and polyp. From August 2011 to August 2013, patients who underwent elective second colonoscopy for resection of polyps, the sizes of which were not small enough to be resected by biopsy forceps alone at first colonoscopy, were enrolled retrospectively. The miss rate on the patient-level was 59.2% (234/395) and on the polyp-level was 27.9% (578/2,068). There was no significant difference in the miss rate depending on the experience of the endoscopists or characteristics of the patients. In terms of the procedure, the miss rate was higher when the colonoscopy was performed in the afternoon (OR 1.632, p=0.046). It was found that the miss rate of polyps increased when the polyps were small (OR 4.595, p<0.001 in <5 mm/OR 3.447, p<0.001 in 5-10 mm), flat or sessile (OR 2.406, p<0.001 in flat/OR 1.768, p=0.002 in sessile), and located in the left colon (OR 1.391, p=0.007). The experience of endoscopists did not have influence on the accuracy of polyp detection. However, the fatigue of endoscopists in the afternoon is considered to render polyp detection less accurate. Also, the large curves and folds of the sigmoid colon are regarded as a reason for the higher miss rate of polyps in the left colon.

Volume-based feature analysis of mucosa for automatic initial polyp detection in virtual colonoscopy

In this paper, we present a volume-based mucosa-based polyp candidate determination scheme for automatic polyp detection in computed colonography. Different from most of the existing computer-aided detection (CAD) methods where mucosa layer is a one-layer surface, a thick mucosa of 3-5 voxels wide fully reflecting partial volume effect is intentionally extracted, which excludes the direct applications of the traditional geometrical features. In order to address this dilemma, fast marching-based adaptive gradient/curvature and weighted integral curvature along normal directions (WICND) are developed for volume-based mucosa. In doing so, polyp candidates are optimally determined by computing and clustering these fast marching-based adaptive geometrical features. By testing on 52 patients datasets in which 26 patients were found with polyps of size 4-22 mm, both the locations and number of polyp candidates detected by WICND and previously developed linear integral curvature (LIC) were compared. The results were promising that WICND outperformed LIC mainly in two aspects: (1) the number of detected false positives was reduced from 706 to 132 on average, which significantly released our burden of machine learning in the feature space, and (2) both the sensitivity and accuracy of polyp detection have been slightly improved, especially for those polyps smaller than 5mm.

Feasibility of automated matching of supine and prone CT-colonography examinations

Matching of prone and supine positions in CT colonography may improve accuracy of polyp detection. The purpose of this study was to investigate the feasibility of automatic prone-supine matching in CT-colonography using proven polyps as fixed points of reference. The method is based on similarities in the direction of centre-lines and allows for compression and extraction of the centre-lines in both positions. To illustrate the impact of the match error of the new method in practice, the visibility of the matched polyps in a primary three-dimensional unfolded cube setting was determined as well. The method was compared with a method that relies on the normalized distance along the centre-line (NDAC method). The median absolute match error was 14 mm (range 0-59 mm, average 20 mm) either proximal or distal from the actual polyp in prone position. In the observer study, 70% (26/37) of the polyps were directly visible in prone view. The overall difference in median absolute match error between both methods was small (2 mm), although half way along the centre-line there were polyps with substantial differences in match error (larger with NDAC). We concluded that automated prone-supine matching of CT-colonography studies is feasible and has a low match error. The difference with the NDAC method was small and not significant, although half way along the centre-line some differences were seen.

Virtual colonoscopy—finally a credible contender

Virtual colonoscopy—finally a credible contender

Accuracy of polyp detection by gastroenterologists and nurse endoscopists during flexible sigmoidoscopy: A randomized trial

Background & Aims: The use of nurse endoscopists to perform flexible sigmoidoscopy is expanding, increasing the availability of colorectal cancer screening. However, the effectiveness of this practice has not been studied in randomized trials. The aim of this trial was to examine the miss rate of polyps, the depth of sigmoidoscope insertion, and the incidence of complications during flexible sigmoidoscopy performed by nurse endoscopists and by gastroenterologists. Methods: Three hundred twenty-eight patients were randomized to undergo screening flexible sigmoidoscopy performed by a nurse endoscopist or by a gastroenterologist. Frequency of missed polyps was determined by repeat sigmoidoscopy, performed by a gastroenterologist blinded to the identity of the first endoscopist. Multiple logistic regression analysis identified characteristics associated with missed polyps. Results: Gastroenterologists and nurse endoscopists had equivalent miss rates for adenomatous polyps (20% vs. 21%, respectively; P = 0.91). No complications occurred in any patient. Gastroenterologists inserted the sigmoidoscope further than nurse endoscopists (61 vs. 55 cm, respectively; P < 0.00001). Polyp location in the descending colon (odds ratio, 4.1; 95% confidence interval, 1.7-10.3) was highly associated with missed polyps. Conclusions: No differences in detection of adenomatous polyps or frequency of complications were found. These data suggest that experienced nurse endoscopists may perform screening flexible sigmoidoscopy as safely and as effectively as gastroenterologists.

Accuracy of polyp detection during screening flexible sigmoidoscopy by gastroenterologists and nurse endoscopists: A randomized controlled trial

Accuracy of polyp detection during screening flexible sigmoidoscopy by gastroenterologists and nurse endoscopists: A randomized controlled trial