Identification Of Polyps Research Articles

FocusU2Net: Pioneering dual attention with gated U-Net for colonoscopic polyp segmentation.

The detection and excision of colorectal polyps, precursors to colorectal cancer (CRC), can improve survival rates by up to 90%. Automated polyp segmentation in colonoscopy images expedites diagnosis and aids in the precise identification of adenomatous polyps, thus mitigating the burden of manual image analysis. This study introduces FocusU2Net, an innovative bi-level nested U-structure integrated with a dual-attention mechanism. The model integrates Focus Gate (FG) modules for spatial and channel-wise attention and Residual U-blocks (RSU) with multi-scale receptive fields for capturing diverse contextual information. Comprehensive evaluations on five benchmark datasets - Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, ETISLarib, and EndoScene - demonstrate Dice score improvements of 3.14% to 43.59% over state-of-the-art models, with an 85% success rate in cross-dataset validations, significantly surpassing prior competing models with sub-5% success rates. The model combines high segmentation accuracy with computational efficiency, featuring 46.64 million parameters, 78.09 GFLOPs, and 39.02 GMacs, making it suitable for real-time applications. Enhanced with Explainable AI techniques, FocusU2Net provides clear insights into its decision-making process, improving interpretability. This combination of high performance, efficiency, and transparency positions FocusU2Net as a powerful, scalable solution for automated polyp segmentation in clinical practice, advancing medical image analysis and computer-aided diagnosis.

MCH-PAN: gastrointestinal polyp detection model integrating multi-scale feature information.

The rise of object detection models has brought new breakthroughs to the development of clinical decision support systems. However, in the field of gastrointestinal polyp detection, there are still challenges such as uncertainty in polyp identification and inadequate coping with polyp scale variations. To address these challenges, this paper proposes a novel gastrointestinal polyp object detection model. The model can automatically identify polyp regions in gastrointestinal images and accurately label them. In terms of design, the model integrates multi-channel information to enhance the ability and robustness of channel feature expression, thus better coping with the complexity of polyp structures. At the same time, a hierarchical structure is constructed in the model to enhance the model's adaptability to multi-scale targets, effectively addressing the problem of large-scale variations in polyps. Furthermore, a channel attention mechanism is designed in the model to improve the accuracy of target positioning and reduce uncertainty in diagnosis. By integrating these strategies, the proposed gastrointestinal polyp object detection model can achieve accurate polyp detection, providing clinicians with reliable and valuable references. Experimental results show that the model exhibits superior performance in gastrointestinal polyp detection, which helps improve the diagnostic level of digestive system diseases and provides useful references for related research fields.

A lighter hybrid feature fusion framework for polyp segmentation

Colonoscopy is widely recognized as the most effective method for the detection of colon polyps, which is crucial for early screening of colorectal cancer. Polyp identification and segmentation in colonoscopy images require specialized medical knowledge and are often labor-intensive and expensive. Deep learning provides an intelligent and efficient approach for polyp segmentation. However, the variability in polyp size and the heterogeneity of polyp boundaries and interiors pose challenges for accurate segmentation. Currently, Transformer-based methods have become a mainstream trend for polyp segmentation. However, these methods tend to overlook local details due to the inherent characteristics of Transformer, leading to inferior results. Moreover, the computational burden brought by self-attention mechanisms hinders the practical application of these models. To address these issues, we propose a novel CNN-Transformer hybrid model for polyp segmentation (CTHP). CTHP combines the strengths of CNN, which excels at modeling local information, and Transformer, which excels at modeling global semantics, to enhance segmentation accuracy. We transform the self-attention computation over the entire feature map into the width and height directions, significantly improving computational efficiency. Additionally, we design a new information propagation module and introduce additional positional bias coefficients during the attention computation process, which reduces the dispersal of information introduced by deep and mixed feature fusion in the Transformer. Extensive experimental results demonstrate that our proposed model achieves state-of-the-art performance on multiple benchmark datasets for polyp segmentation. Furthermore, cross-domain generalization experiments show that our model exhibits excellent generalization performance.

NA-segformer: A multi-level transformer model based on neighborhood attention for colonoscopic polyp segmentation

In various countries worldwide, the incidence of colon cancer-related deaths has been on the rise in recent years. Early detection of symptoms and identification of intestinal polyps are crucial for improving the cure rate of colon cancer patients. Automated computer-aided diagnosis (CAD) has emerged as a solution to the low efficiency of traditional methods relying on manual diagnosis by physicians. Deep learning is the latest direction of CAD development and has shown promise for colonoscopic polyp segmentation. In this paper, we present a multi-level encoder-decoder architecture for polyp segmentation based on the Transformer architecture, termed NA-SegFormer. To improve the performance of existing Transformer-based segmentation algorithms for edge segmentation on colon polyps, we propose a patch merging module with a neighbor attention mechanism based on overlap patch merging. Since colon tract polyps vary greatly in size and different datasets have different sample sizes, we used a unified focal loss to solve the problem of category imbalance in colon tract polyp data. To assess the effectiveness of our proposed method, we utilized video capsule endoscopy and typical colonoscopy polyp datasets, as well as a dataset containing surgical equipment. On the datasets Kvasir-SEG, Kvasir-Instrument and KvasirCapsule-SEG, the Dice score of our proposed model reached 94.30%, 94.59% and 82.73%, with an accuracy of 98.26%, 99.02% and 81.84% respectively. The proposed method achieved inference speed with an Frame-per-second (FPS) of 125.01. The results demonstrated that our suggested model effectively segmented polyps better than several well-known and latest models. In addition, the proposed method has advantages in trade-off between inference speed and accuracy, and it will be of great significance to real-time colonoscopic polyp segmentation. The code is available at https://github.com/promisedong/NAFormer.

Self-Adaptive Teacher-Student framework for colon polyp segmentation from unannotated private data with public annotated datasets.

Colon polyps have become a focal point of research due to their heightened potential to develop into appendiceal cancer, which has the highest mortality rate globally. Although numerous colon polyp segmentation methods have been developed using public polyp datasets, they tend to underperform on private datasets due to inconsistencies in data distribution and the difficulty of fine-tuning without annotations. In this paper, we propose a Self-Adaptive Teacher-Student (SATS) framework to segment colon polyps from unannotated private data by utilizing multiple publicly annotated datasets. The SATS trains multiple teacher networks on public datasets and then generates pseudo-labels on private data to assist in training a student network. To enhance the reliability of the pseudo-labels from the teacher networks, the SATS includes a newly proposed Uncertainty and Distance Fusion (UDFusion) strategy. UDFusion dynamically adjusts the pseudo-label weights based on a novel reconstruction similarity measure, innovatively bridging the gap between private and public data distributions. To ensure accurate identification and segmentation of colon polyps, the SATS also incorporates a Granular Attention Network (GANet) architecture for both teacher and student networks. GANet first identifies polyps roughly from a global perspective by encoding long-range anatomical dependencies and then refines this identification to remove false-positive areas through multi-scale background-foreground attention. The SATS framework was validated using three public datasets and one private dataset, achieving 76.30% on IoU, 86.00% on Recall, and 7.01 pixels on HD. These results outperform the existing five methods, indicating the effectiveness of this approach for colon polyp segmentation.

PolySegNet: improving polyp segmentation through swin transformer and vision transformer fusion.

Colorectal cancer ranks as the second most prevalent cancer worldwide, with a high mortality rate. Colonoscopy stands as the preferred procedure for diagnosing colorectal cancer. Detecting polyps at an early stage is critical for effective prevention and diagnosis. However, challenges in colonoscopic procedures often lead medical practitioners to seek support from alternative techniques for timely polyp identification. Polyp segmentation emerges as a promising approach to identify polyps in colonoscopy images. In this paper, we propose an advanced method, PolySegNet, that leverages both Vision Transformer and Swin Transformer, coupled with a Convolutional Neural Network (CNN) decoder. The fusion of these models facilitates a comprehensive analysis of various modules in our proposed architecture.To assess the performance of PolySegNet, we evaluate it on three colonoscopy datasets, a combined dataset, and their augmented versions. The experimental results demonstrate that PolySegNet achieves competitive results in terms of polyp segmentation accuracy and efficacy, achieving a mean Dice score of 0.92 and a mean Intersection over Union (IoU) of 0.86. These metrics highlight the superior performance of PolySegNet in accurately delineating polyp boundaries compared to existing methods. PolySegNet has shown great promise in accurately and efficiently segmenting polyps in medical images. The proposed method could be the foundation for a new class of transformer-based segmentation models in medical image analysis.

A Novel Ensemble Deep Learning Based Polyp Detection Using Colonoscopy Dataset

This work addresses the critical task of polyp detection and classification using the SUN colonoscopy video database, which consists of still images annotated with bounding boxes. These images categorize frames into polyp and non-polyp and encompass six distinct classes of polyps: Hyperplastic polyp, Sessile serrated lesion, Low-grade adenoma, Traditional serrated adenoma, High-grade adenoma, and Invasive carcinoma. The approach involves a two-stage classification process. Initially, MobileNetV2 is employed to distinguish between polyp and non-polyp frames. Subsequently, ResNet50 and GoogLeNet are utilized to classify the identified polyps into the six predefined categories. Data augmentation techniques are implemented to address the inherent imbalance in class distribution within the dataset, enhancing model performance and generalizability. The results highlight the effectiveness of GoogLeNet, which achieved an impressive accuracy of 98%, significantly outperforming ResNet50's accuracy of 76.16%. This substantial improvement underscores the potential of GoogLeNet in enhancing the accuracy of polyp classification. The significance of this work lies in its contribution to advancing automated polyp detection and cancer stage classification, crucial for early diagnosis and treatment. These findings provide a foundation for further research and development in this domain, with the potential to improve clinical outcomes through more accurate and timely identification of colorectal polyps. Received: 30 January 2024 | Revised: 22 May 2024 | Accepted: 9 July 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement K. Sai Rakshana: Data curation, Writing - original draft, Writing - review & editing, Visualization. Antony Dennis Ananth: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources. L. Gowri: Supervision, Project administration.

Peutz-Jeghers Syndrome: A Comprehensive Review of Genetics, Clinical Features, and Management Approaches.

A relatively rare inherited condition known as Peutz-Jeghers syndrome (PJS) causes mucocutaneous pigmentation and gastrointestinal hamartomatous polyps. These polyps are non-cancerous, but the presence of PJS significantly increases the chances of developing various types of cancers, such as colorectal, pancreatic, gastric, and breast cancer. The purpose of this review article is to give an abbreviated summary of what is currently known about this syndrome, covering its clinical symptoms, pathophysiology, genetics, and management. PJS also raises the risk of getting many malignancies, especially gastrointestinal and pelvic cancers. Symptoms of the gastrointestinal tract brought on by hamartomatous polyps are frequent and include stool blockage, bleeding, and stomach pain. The pigmentation commonly appears as prominent bluish-black macules and frequently affects the skin and mucous membranes. Small macules and large regions of lentiginous pigmentation are both possible. Numerous areas, including the perioral area, buccal mucosa, fingers, and lips, exhibit pigmentation. Bowel obstruction and intussusception risk can be decreased by early identification and routine surveillance of gastrointestinal polyps. The gene serine/threonine kinase 11 (STK11) controls several biological functions, including cell polarity, growth, and proliferation. Genetic counseling is recommended for the affected individuals and their families. This can help assess the risk of passing on the condition to future generations and provide information about available reproductive options. Regular surveillance is crucial for managing the syndrome and reducing the risk of cancer development. Other syndromes and extra-gastrointestinal characteristics, such as somatic tumor polyps outside the gastrointestinal tract, are also linked to this syndrome.

An approach of polyp segmentation from colonoscopy images using Dilated-U-Net-Seg – A deep learning network

Throughout the entire world, colorectal cancer (CRC) is the second most common cause of death. The most important element in preventing cancer is early detection. It increases the likelihood that those in need will survive. There are several approaches for diagnosing CRC, but colonoscopy is still the go-to procedure for both screening and diagnosis. It finds polyps and other abnormal CRC indications in the colon and rectum. Although several technical developments have enhanced the early identification of polyps, there is still a considerable likelihood that polyps may be overlooked due to several variables. Techniques based on artificial intelligence are crucial for finding such polyps. This study develops the Dilated-U-Net-Seg Deep Learning-based technique for effectively segmenting the polyp area. The suggested framework combines dilated convolutions and feature concatenation in the same layer with an encoder-decoder structure. Five datasets have been divided into separate sets for training and testing is used to empirically test this framework. This yields 97.11 % of pixel accuracy, 97.90 % of dice score, 88 % of the Jaccard Index, 95.89 % of recall, and 92.40 % of precision. In comparison to the current U-Net architecture, this novel framework for polyp segmentation that implements Dilated-U-Net-Seg demonstrates a 3.05 % increase in Pixel accuracy.

Polypoid Lesion Segmentation Using YOLO-V8 Network in Wireless Video Capsule Endoscopy Images.

Gastrointestinal (GI) tract disorders are a significant public health issue. They are becoming more common and can cause serious health problems and high healthcare costs. Small bowel tumours (SBTs) and colorectal cancer (CRC) are both becoming more prevalent, especially among younger adults. Early detection and removal of polyps (precursors of malignancy) is essential for prevention. Wireless Capsule Endoscopy (WCE) is a procedure that utilises swallowable camera devices that capture images of the GI tract. Because WCE generates a large number of images, automated polyp segmentation is crucial. This paper reviews computer-aided approaches to polyp detection using WCE imagery and evaluates them using a dataset of labelled anomalies and findings. The study focuses on YOLO-V8, an improved deep learning model, for polyp segmentation and finds that it performs better than existing methods, achieving high precision and recall. The present study underscores the potential of automated detection systems in improving GI polyp identification.

An automatic system to detect colorectal polyp using hybrid fused method from colonoscopy images

The third most common disease in the world that leads to mortality is colorectal cancer (CRC). Colonoscopy, which locates and removes colonic polyps, is the most common procedure worldwide for the early identification and preventative treatment of colorectal cancer. The likelihood of a CRC patient dying can be considerably decreased by early diagnosis and treatment of precancerous polyps. Unidentified polyps may eventually turn into cancer. Although several classification algorithms have been put out to identify polyps, their effectiveness has not yet been compared to that of skilled endoscopes. An ensemble machine learning-based classification approach for detecting colorectal cancer from colonoscopy images is presented in this research. Firstly researchers collect colorectal cancer or polyp images from three standard datasets. In the preprocessing phase, researcher convert the RGB image to grayscale image and identified the region of interest (ROI) by removing the unwanted regions. A hybrid anisotropic diffusion filtering (HADF) approach was employed to eliminate noise from each image. Then the system extracts features from individual feature extractor methods and fused extracted features in a vector. The fused features help to detect colorectal polyp or cancer for increasing cancer or polyp identification rates. Finally, an ensemble classifier classifies the colorectal cancer or normal images and achieves better accuracy. The suggested technique exceeds the prior conventional methods, according to tests on widely used public datasets, improving accuracy by 99.45 %, sensitivity by 99.30 %, specificityby 99.58 %, and precision 99.53 %.

Design and development of artificial intelligence‐based application programming interface for early detection and diagnosis of colorectal cancer from wireless capsule endoscopy images

AbstractThe colorectal cancer (CRC) is gaining attention in the context of gastrointestinal tract diseases as it ranks third among the most prevalent type of cancer. The early diagnosis of the CRC can be done by periodic examination of the colon and rectum for innocuous tissue abnormality called polyp as it has the potential to evolve as malignant in future. The CRC diagnosis using wireless capsule endoscopy requires the dedicated commitment of the medical expert demanding significant time, focus and effort. The accuracy of manual analysis in identifying polyps is extensively reliant on the cognitive condition of the physician, thus emphasizing the requirement for automatic polyp identification. The artificial intelligence integrated computer‐aided diagnosis system could assist the clinician in better diagnosis, thereby reducing the miss‐rates of polyps. In our proposed study, we developed an application program interface to aid the clinician in automatic segmentation of the polyp and evaluation of its dimension by manual placement of four landmarks on the predicted polyp. The segmentation is performed by the proposed light weight Padded U‐Net for the effective polyp segmentation in the colorectal images. We trained and validated the Padded U‐Net with augmented images of Kvasir dataset and calculated the performance parameters. In order to facilitate image augmentation, a graphical user interface called Augment Tree was developed, which incorporates 92 augmentation techniques. The accuracy, recall, precision, IoU, F1‐score, loss achieved during validation of Padded U‐Net were 95.6%, 0.946%, 0.985%, 0.933%, 0.965% and 0.080% respectively. We demonstrated that accuracy was improved and loss was reduced when the model was trained with augmented images rather than only the limited original dataset images. On comparison of our Padded U‐net architecture with recently developed architectures, our model attained optimal performance in all the metrics except accuracy in which it attained marginal performance to the highest value.

Simultaneous segmentation and classification of colon cancer polyp images using a dual branch multi-task learning network.

Accurate classification and segmentation of polyps are two important tasks in the diagnosis and treatment of colorectal cancers. Existing models perform segmentation and classification separately and do not fully make use of the correlation between the two tasks. Furthermore, polyps exhibit random regions and varying shapes and sizes, and they often share similar boundaries and backgrounds. However, existing models fail to consider these factors and thus are not robust because of their inherent limitations. To address these issues, we developed a multi-task network that performs both segmentation and classification simultaneously and can cope with the aforementioned factors effectively. Our proposed network possesses a dual-branch structure, comprising a transformer branch and a convolutional neural network (CNN) branch. This approach enhances local details within the global representation, improving both local feature awareness and global contextual understanding, thus contributing to the improved preservation of polyp-related information. Additionally, we have designed a feature interaction module (FIM) aimed at bridging the semantic gap between the two branches and facilitating the integration of diverse semantic information from both branches. This integration enables the full capture of global context information and local details related to polyps. To prevent the loss of edge detail information crucial for polyp identification, we have introduced a reverse attention boundary enhancement (RABE) module to gradually enhance edge structures and detailed information within polyp regions. Finally, we conducted extensive experiments on five publicly available datasets to evaluate the performance of our method in both polyp segmentation and classification tasks. The experimental results confirm that our proposed method outperforms other state-of-the-art methods.

Exploring the Potential of Ensembles of Deep Learning Networks for Image Segmentation

To identify objects in images, a complex set of skills is needed that includes understanding the context and being able to determine the borders of objects. In computer vision, this task is known as semantic segmentation and it involves categorizing each pixel in an image. It is crucial in many real-world situations: for autonomous vehicles, it enables the identification of objects in the surrounding area; in medical diagnosis, it enhances the ability to detect dangerous pathologies early, thereby reducing the risk of serious consequences. In this study, we compare the performance of various ensembles of convolutional and transformer neural networks. Ensembles can be created, e.g., by varying the loss function, the data augmentation method, or the learning rate strategy. Our proposed ensemble, which uses a simple averaging rule, demonstrates exceptional performance across multiple datasets. Notably, compared to prior state-of-the-art methods, our ensemble consistently shows improvements in the well-studied polyp segmentation problem. This problem involves the precise delineation and identification of polyps within medical images, and our approach showcases noteworthy advancements in this domain, obtaining an average Dice of 0.887, which outperforms the current SOTA with an average Dice of 0.885.

Polyp Identification from a Colonoscopy Image Using Semantic Segmentation Approach

Colorectal Cancer (CRC) is a major contributor to cancer-related mortality worldwide, necessitating early detection and treatment of polyps to prevent cancer progression. A colonoscopy is a critical diagnostic procedure for identifying colon abnormalities and removing premalignant polyps. However, accurately segmenting polyps in colonoscopy images poses challenges due to their diverse appearance and indistinct boundaries. In this study, we investigate augmentation techniques to enhance polyp semantic segmentation using the U-Net model. Our analysis reveals that the most effective technique is found in sub-scenario 2.6.c with an input size of 320×320, striking a favorable balance between accuracy and efficiency. Additionally, we explore the benefits of larger input sizes, taking into account resource considerations. Moreover, we conduct further testing of the best augmentation technique identified in previous experiments with the SegNet model. The results show a 3.5% improvement in the dice coefficient and slightly better qualitative outcomes. However, it is important to note that this enhancement comes with a nearly fivefold increase in training time. Moving forward, our objective is to develop a unified model for segmenting diverse medical images, pushing the boundaries of polyp detection and medical imaging. This research provides valuable insights and lays the foundation for more advanced applications in polyp detection and medical image analysis.

Identification of Polyp from Colonoscopy Images by Deep Belief Network based Polyp Detector Integration Model

Cancer is a disease involving unusual cell growth likely to spread to other parts of the body. According to WHO 2020 report, colorectal malignancy is the globally accepted second leading cause of cancer related deaths. Colorectal malignancy arises when malignant cells often called polyp, grow inside the tissues of the colon or rectum of the large intestine. Colonoscopy, CT scan, Histopathological analysis are some manual approaches of malignancy detection that are time consuming and lead to diagnostic errors. Supervised CNN data model requires a large number of labeled training samples to learn parameters from images. In this study we propose an expert system that can detect the colorectal malignancy and identify the exact polyp area from complex images. In this approach an unsupervised Deep Belief Network (DBN) is applied for effective feature extraction and classification of images. The classified image output of DBN is utilized by Polyp Detector. Residual network and feature extractor components of Polyp Detector helps polyp inspector in pixel wise learning. Two stage polyp network (PLPNet) is a R-CNN architecture with two stage advantage. The first stage is the extension of R-CNN to detect the polyp lesion area through a location box also called Polyp Inspector. Second Stage performs polyp segmentation. Polyp Inspector transfers the learned semantics to the polyp segmentation stage. It helps to enhance the ability to detect polyp with improved accuracy and guide the learning process. Skip schemes enrich the feature scale. Publicly available CVC-Clinical DB and CVC Colon DB datasets are used for experiment purposes to achieve a better prediction capability for clinical practices.

A deep learning pipeline for automated classification of vocal fold polyps in flexible laryngoscopy.

To develop and validate a deep learning model for distinguishing healthy vocal folds (HVF) and vocal fold polyps (VFP) on laryngoscopy videos, while demonstrating the ability of a previously developed informative frame classifier in facilitating deep learning development. Following retrospective extraction of image frames from 52 HVF and 77 unilateral VFP videos, two researchers manually labeled each frame as informative or uninformative. A previously developed informative frame classifier was used to extract informative frames from the same video set. Both sets of videos were independently divided into training (60%), validation (20%), and test (20%) by patient. Machine-labeled frames were independently verified by two researchers to assess the precision of the informative frame classifier. Two models, pre-trained on ResNet18, were trained to classify frames as containing HVF or VFP. The accuracy of the polyp classifier trained on machine-labeled frames was compared to that of the classifier trained on human-labeled frames. The performance was measured by accuracy and area under the receiver operating characteristic curve (AUROC). When evaluated on a hold-out test set, the polyp classifier trained on machine-labeled frames achieved an accuracy of 85% and AUROC of 0.84, whereas the classifier trained on human-labeled frames achieved an accuracy of 69% and AUROC of 0.66. An accurate deep learning classifier for vocal fold polyp identification was developed and validated with the assistance of a peer-reviewed informative frame classifier for dataset assembly. The classifier trained on machine-labeled frames demonstrates improved performance compared to the classifier trained on human-labeled frames.

MCSF-Net: a multi-scale channel spatial fusion network for real-time polyp segmentation.

Colorectal cancer is a globally prevalent cancer type that necessitates prompt screening. Colonoscopy is the established diagnostic technique for identifying colorectal polyps. However, missed polyp rates remain a concern. Early detection of polyps, while still precancerous, is vital for minimizing cancer-related mortality and economic impact. In the clinical setting, precise segmentation of polyps from colonoscopy images can provide valuable diagnostic and surgical information. Recent advances in computer-aided diagnostic systems, specifically those based on deep learning techniques, have shown promise in improving the detection rates of missed polyps, and thereby assisting gastroenterologists in improving polyp identification. In the present investigation, we introduce MCSF-Net, a real-time automatic segmentation framework that utilizes a multi-scale channel space fusion network. The proposed architecture leverages a multi-scale fusion module in conjunction with spatial and channel attention mechanisms to effectively amalgamate high-dimensional multi-scale features. Additionally, a feature complementation module is employed to extract boundary cues from low-dimensional features, facilitating enhanced representation of low-level features while keeping computational complexity to a minimum. Furthermore, we incorporate shape blocks to facilitate better model supervision for precise identification of boundary features of polyps. Our extensive evaluation of the proposed MCSF-Net on five publicly available benchmark datasets reveals that it outperforms several existing state-of-the-art approaches with respect to different evaluation metrics. The proposed approach runs at an impressive ∼45 FPS, demonstrating notable advantages in terms of scalability and real-time segmentation.

An Accurate and Automated Method for Adenoma Detection Rate and Report Card Generation Utilizing Common Electronic Health Records.

To develop an automated method for Adenoma Detection Rate (ADR) calculation and report card generation using common electronic health records (EHRs). ADR is the most widely accepted colonoscopy quality indicator and is inversely associated with interval colorectal cancer incidence and mortality. However, ADR is difficult to efficiently measure and disseminate, due to need for data integration from distinct electronic databases. We migrated data from an endoscopy reporting software (Endosoft) to Epic Reporting Servers where it was combined with anatomic pathology data (Beaker Lab Information System, EPIC Systems). A natural language processing expression was developed to search Beaker pathology reports for accurate identification of adenomatous polyps. A blinded physician manually validated a final cohort of 200 random procedures. ADR report cards were automatically generated utilizing the Crystal Reports feature within EPIC. Validation of the natural language processing algorithm for ADR showed a sensitivity, specificity, and accuracy of 100%. ADR was automatically calculated for 12 endoscopists over a calendar year. Two thousand two hundred seventy-six screening colonoscopies were performed with 775 procedures having a least one adenoma detected, for a total ADR of 34%. Report cards were successfully generated within the EPIC EHR and distributed to endoscopists by secure e-mail. We describe an accurate, automated and scalable process for ADR calculation and reporting utilizing commonly adopted EHRs and data integration methods. By integrating the process of ADR collection and streamlining dissemination of reports, this methodology is poised to enhance colonoscopy quality care across health care networks that use it.

Transgastric laparoscopic polypectomy in giant gastric polyps not susceptible of endoscopic management: novel surgical technique and case series.

The identification of gastric polyps incidentally in endoscopies of the upper digestive tract has increased its incidence, varying between 0.5% and 23%. 10% of these polyps have symptoms, 40% are hyperplastic. We allow ourselves to propose a laparoscopic technique for the management of giant hyperplastic polyps associated with a pyloric syndrome, not susceptible to endoscopic resection. A series of patients approached by laparoscopic transgastric polypectomy due to the giant gastric polyps associated with pyloric syndrome, in Bogotá, Colombia, from January 2015 to December 2018. Seven patients, 85% female, with an average age of 51 years, who were admitted for pyloric syndrome and were taken to laparoscopic management, with an average surgical time of 42 min, intraoperative bleeding 7-8 cc, tolerance to the oral route 24 hours, no conversion, without mortality. Transgastric polypectomy for the management of benign giant gastric polyps that cannot be resected endoscopically turns out to be a feasible method, with a low rate of complications and without mortality.