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Conventional endoscopy is limited in its ability to examine the small bowel and perform long-term monitoring due to the risk of infection and tissue perforation. Wireless Capsule Endoscopy (WCE) is a painless and non-invasive method of examining the body's internal organs using a small camera that is swallowed like a pill. The existing active locomotion technologies do not have a practical localization system to control the capsule's movement within the body. A robust localization system is essential for safely guiding the WCE device through the complex gastrointestinal (GI) tract. Moreover, having access to the capsule's trajectory data is highly desirable for drug delivery and surgery, as well as for creating accurate user profiles for diagnosis and future reference. Therefore, a robust, real-time, and practical localization system is imperative to advance the field of WCE and make it desirable for clinical trials. In this work, we have identified salient features of different localization techniques and categorized studies in comprehensive tables. This study is self-contained as it offers a comprehensive overview of emerging localization techniques based on magnetic field, radio frequency (RF), video, and hybrid methods. A summary at the end of each method is provided to point out the potential gaps and give directions for future research. The main point of this work is to present an in-depth review of the most recent localization techniques published in the past five years. This will assist researchers in comprehending current techniques and pinpointing potential areas for further investigation. This review can be a significant reference and guide for future research on WCE localization.

Purpose: Wireless capsule endoscopy (WCE) is a non-invasive technology used for diagnosing gastrointestinal abnormalities. A single examination generates ∼ 55,000 images, making manual review both time-consuming and costly for doctors. Therefore, the development of computer vision-assisted systems is highly desirable to aid in the diagnostic process., Approach: We presents a deep learning approach leveraging knowledge distillation (KD) from a convolutional neural network (CNN) teacher model to a vision transformer (ViT) student model for gastrointestinal abnormality recognition. The CNN teacher model utilizes attention mechanisms and depth-wise separable convolutions to extract features from WCE images, supervising the ViT in learning these representations., Results: The proposed method achieves accuracy of 97% and 96% on the Kvasir and KID datasets, respectively, demonstrating its effectiveness in distinguishing normal from abnormal regions and bleeding from non-bleeding cases. The proposed approach offers computational efficiency and generalization to unseen datasets, outperforming several state-of-the-art methods., Conclusions: We proposed a deep learning approach utilizing CNNs and a ViT with KD to effectively classify gastrointestinal diseases in WCE images. It demonstrates promising performance on public datasets, distinguishing normal from abnormal regions and bleeding from non-bleeding cases while offering optimal computational efficiency compared with existing methods, making it suitable for GI disease applications., (© 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Background: Wireless capsule endoscopy (WCE) has become an important noninvasive and portable tool for diagnosing digestive tract diseases and has been propelled by advancements in medical imaging technology. However, the complexity of the digestive tract structure, and the diversity of lesion types, results in different sites and types of lesions distinctly appearing in the images, posing a challenge for the accurate identification of digestive tract diseases., Aim: To propose a deep learning-based lesion detection model to automatically identify and accurately label digestive tract lesions, thereby improving the diagnostic efficiency of doctors, and creating significant clinical application value., Methods: In this paper, we propose a neural network model, WCE_Detection, for the accurate detection and classification of 23 classes of digestive tract lesion images. First, since multicategory lesion images exhibit various shapes and scales, a multidetection head strategy is adopted in the object detection network to increase the model's robustness for multiscale lesion detection. Moreover, a bidirectional feature pyramid network (BiFPN) is introduced, which effectively fuses shallow semantic features by adding skip connections, significantly reducing the detection error rate. On the basis of the above, we utilize the Swin Transformer with its unique self-attention mechanism and hierarchical structure in conjunction with the BiFPN feature fusion technique to enhance the feature representation of multicategory lesion images., Results: The model constructed in this study achieved an mAP50 of 91.5% for detecting 23 lesions. More than eleven single-category lesions achieved an mAP50 of over 99.4%, and more than twenty lesions had an mAP50 value of over 80%. These results indicate that the model outperforms other state-of-the-art models in the end-to-end integrated detection of human digestive tract lesion images., Conclusion: The deep learning-based object detection network detects multiple digestive tract lesions in WCE images with high accuracy, improving the diagnostic efficiency of doctors, and demonstrating significant clinical application value., Competing Interests: Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article., (©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.)

Wireless capsule endoscopy is a non-invasive medical imaging modality used for diagnosing and monitoring digestive tract diseases. However, the analysis of images obtained from wireless capsule endoscopy is a challenging task, as the images are of low resolution and often contain a large number of artifacts. In recent years, deep learning has shown great promise in the analysis of medical images, including wireless capsule endoscopy images. This paper provides a review of the current trends and future directions in deep learning for wireless capsule endoscopy. We focus on the recent advances in transfer learning, attention mechanisms, multi-modal learning, automated lesion detection, interpretability and explainability, data augmentation, and edge computing. We also highlight the challenges and limitations of current deep learning methods and discuss the potential future directions for the field. Our review provides insights into the ongoing research and development efforts in the field of deep learning for wireless capsule endoscopy, and can serve as a reference for researchers, clinicians, and engineers working in this area inspection process., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Habe TT et al.)

Background: Wireless capsule endoscopy (WCE) has significantly advanced the diagnosis of gastrointestinal (GI) diseases by allowing for the non-invasive visualization of the entire small intestine. However, machine learning-based methods for organ classification in WCE often rely on color information, leading to decreased performance when obstacles such as food debris are present. This study proposes a novel model that integrates convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to analyze multiple frames and incorporate temporal information, ensuring that it performs well even when visual information is limited., Methods: We collected data from 126 patients using PillCam™ SB3 (Medtronic, Minneapolis, MN, USA), which comprised 2,395,932 images. Our deep learning model was trained to identify organs (stomach, small intestine, and colon) using data from 44 training and 10 validation cases. We applied calibration using a Gaussian filter to enhance the accuracy of detecting organ boundaries. Additionally, we estimated the transit time of the capsule in the gastric and small intestine regions using a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) designed to be aware of the sequence information of continuous videos. Finally, we evaluated the model's performance using WCE videos from 72 patients., Results: Our model demonstrated high performance in organ classification, achieving an accuracy, sensitivity, and specificity of over 95% for each organ (stomach, small intestine, and colon), with an overall accuracy and F1-score of 97.1%. The Matthews Correlation Coefficient (MCC) and Geometric Mean (G-mean) were used to evaluate the model's performance on imbalanced datasets, achieving MCC values of 0.93 for the stomach, 0.91 for the small intestine, and 0.94 for the colon, and G-mean values of 0.96 for the stomach, 0.95 for the small intestine, and 0.97 for the colon. Regarding the estimation of gastric and small intestine transit times, the mean time differences between the model predictions and ground truth were 4.3 ± 9.7 min for the stomach and 24.7 ± 33.8 min for the small intestine. Notably, the model's predictions for gastric transit times were within 15 min of the ground truth for 95.8% of the test dataset (69 out of 72 cases). The proposed model shows overall superior performance compared to a model using only CNN., Conclusions: The combination of CNN and LSTM proves to be both accurate and clinically effective for organ classification and transit time estimation in WCE. Our model's ability to integrate temporal information allows it to maintain high performance even in challenging conditions where color information alone is insufficient. Including MCC and G-mean metrics further validates the robustness of our approach in handling imbalanced datasets. These findings suggest that the proposed method can significantly improve the diagnostic accuracy and efficiency of WCE, making it a valuable tool in clinical practice for diagnosing and managing GI diseases.

Wireless capsule endoscopy (CE) has revolutionized gastrointestinal diagnostics, offering a non-invasive means to visualize and monitor the GI tract. This review traces the evolution of CE technology. Addressing the limitations of traditional white light (WL) CE, the paper explores non-WL technologies, integrating diverse sensing modalities and novel biomarkers to enhance diagnostic capabilities. Concluding with an assessment of Technology Readiness Levels, the paper emphasizes the transformative impact of non-WL colon CE devices on GI diagnostics, promising more precise, patient-centric, and accessible healthcare for GI disorders., Competing Interests: The authors declare that there is no conflict of interest., (© The Author(s), 2024.)

Background: A significant number of frames captured by the wireless capsule endoscopy are involved with varying amounts of bubbles. Whereas different studies have considered bubbles as nonuseful agents due to the fact that they reduce the visualization quality of the small intestine mucosa, this research aims to develop a practical way of assessing the rheological capability of the circular bubbles as a suggestion for future clinical diagnostic purposes., Methods: From the Kvasir-capsule endoscopy dataset, frames with varying levels of bubble engagements were chosen in two categories based on bubble size. Border reflections are present on the edges of round-shaped bubbles in their boundaries, and in the frequency domain, high-frequency bands correspond to these edges in the spatial domain. The first step is about high-pass filtering of border reflections using wavelet transform (WT) and Differential of Gaussian, and the second step is related to applying the Fast Circlet Transform (FCT) and the Hough transform as circle detection tools on extracted borders and evaluating the distribution and abundance of all bubbles with the variety of radii., Results: Border's extraction using WT as a preprocessing approach makes it easier for circle detection tool for better concentration on high-frequency circular patterns. Consequently, applying FCT with predefined parameters can specify the variety and range of radius and the abundance for all bubbles in an image. The overall discrimination factor (ODF) of 15.01, and 7.1 showing distinct bubble distributions in the gastrointestinal (GI) tract. The discrimination in ODF from datasets 1-2 suggests a relationship between the rheological properties of bubbles and their coverage area plus their abundance, highlighting the WT and FCT performance in determining bubbles' distributions for diagnostic objectives., Conclusion: The implementation of an object-oriented attitude in gastrointestinal analysis makes it intelligible for gastroenterologists to approximate the constituent features of intra-intestinal fluids. this can't be evaluated until the bubbles are considered as non-useful agents. The obtained results from the datasets proved that the difference between the calculated ODF can be used as an indicator for the quality estimation of intraintestinal fluids' rheological features like viscosity, which helps gastroenterologists evaluate the quality of patient digestion., Competing Interests: There are no conflicts of interest., (Copyright: © 2024 Journal of Medical Signals & Sensors.)

While the adoption of wireless capsule endoscopy (WCE) has been steadily increasing, its primary application remains limited to observing the small intestine, with relatively less application in the upper gastrointestinal tract. However, there is a growing anticipation that advancements in capsule endoscopy technology will lead to a significant increase in its application in upper gastrointestinal examinations. This study addresses the underexplored domain of landmark identification within the upper gastrointestinal tract using WCE, acknowledging the limited research and public datasets available in this emerging field. To contribute to the future development of WCE for gastroscopy, a novel approach is proposed. Utilizing color transfer techniques, a simulated WCE dataset tailored for the upper gastrointestinal tract is created. Using Euclidean distance measurements, the similarity between this color-transferred dataset and authentic WCE images is verified. Pioneering the exploration of anatomical landmark classification with WCE data, this study integrates similarity evaluation with image preprocessing and deep learning techniques, specifically employing the DenseNet169 model. As a result, utilizing the color-transferred dataset achieves an anatomical landmark classification accuracy exceeding 90% in the upper gastrointestinal tract. Furthermore, the application of sharpen and detail filters demonstrates an increase in classification accuracy from 91.32% to 94.06%.

Background: Gastrointestinal tract (GIT) diseases impact the entire digestive system, spanning from the mouth to the anus. Wireless Capsule Endoscopy (WCE) stands out as an effective analytic instrument for Gastrointestinal tract diseases. Nevertheless, accurately identifying various lesion features, such as irregular sizes, shapes, colors, and textures, remains challenging in this field., Objective: Several computer vision algorithms have been introduced to tackle these challenges, but many relied on handcrafted features, resulting in inaccuracies in various instances., Methods: In this work, a novel Deep SS-Hexa model is proposed which is a combination two different deep learning structures for extracting two different features from the WCE images to detect various GIT ailment. The gathered images are denoised by weighted median filter to remove the noisy distortions and augment the images for enhancing the training data. The structural and statistical (SS) feature extraction process is sectioned into two phases for the analysis of distinct regions of gastrointestinal. In the first stage, statistical features of the image are retrieved using MobileNet with the support of SiLU activation function to retrieve the relevant features. In the second phase, the segmented intestine images are transformed into structural features to learn the local information. These SS features are parallelly fused for selecting the best relevant features with walrus optimization algorithm. Finally, Deep belief network (DBN) is used classified the GIT diseases into hexa classes namely normal, ulcer, pylorus, cecum, esophagitis and polyps on the basis of the selected features., Results: The proposed Deep SS-Hexa model attains an overall average accuracy of 99.16% in GIT disease detection based on KVASIR and KID datasets. The proposed Deep SS-Hexa model achieves high level of accuracy with minimal computational cost in the recognition of GIT illness., Conclusions: The proposed Deep SS-Hexa Model progresses the overall accuracy range of 0.04%, 0.80% better than GastroVision, Genetic algorithm based on KVASIR dataset and 0.60%, 1.21% better than Modified U-Net, WCENet based on KID dataset respectively.

Background: Wireless capsule endoscopy (WCE) is a patient-friendly and non-invasive technology that scans the whole of the gastrointestinal tract, including difficult-to-access regions like the small bowel. Major drawback of this technology is that the visual inspection of a large number of video frames produced during each examination makes the physician diagnosis process tedious and prone to error. Several computer-aided diagnosis (CAD) systems, such as deep network models, have been developed for the automatic recognition of abnormalities in WCE frames. Nevertheless, most of these studies have only focused on spatial information within individual WCE frames, missing the crucial temporal data within consecutive frames., Methods: In this article, an automatic multiclass classification system based on a three-dimensional deep convolutional neural network (3D-CNN) is proposed, which utilizes the spatiotemporal information to facilitate the WCE diagnosis process. The 3D-CNN model fed with a series of sequential WCE frames in contrast to the two-dimensional (2D) model, which exploits frames as independent ones. Moreover, the proposed 3D deep model is compared with some pre-trained networks. The proposed models are trained and evaluated with 29 subject WCE videos (14,691 frames before augmentation). The performance advantages of 3D-CNN over 2D-CNN and pre-trained networks are verified in terms of sensitivity, specificity, and accuracy., Results: 3D-CNN outperforms the 2D technique in all evaluation metrics (sensitivity: 98.92 vs. 98.05, specificity: 99.50 vs. 86.94, accuracy: 99.20 vs. 92.60). In conclusion, a novel 3D-CNN model for lesion detection in WCE frames is proposed in this study., Conclusion: The results indicate the performance of 3D-CNN over 2D-CNN and some well-known pre-trained classifier networks. The proposed 3D-CNN model uses the rich temporal information in adjacent frames as well as spatial data to develop an accurate and efficient model., (© 2023. The Author(s).)

Wireless Capsule Endoscopy is a medical procedure that uses a small, wireless camera to capture images of the inside of the digestive tract. The identification of the entrance and exit of the small bowel and of the large intestine is one of the first tasks that need to be accomplished to read a video. This paper addresses the design of a clinical decision support tool to detect these anatomical landmarks. We have developed a system based on deep learning that combines images, timestamps, and motion data to achieve state-of-the-art results. Our method does not only classify the images as being inside or outside the studied organs, but it is also able to identify the entrance and exit frames. The experiments performed with three different datasets (one public and two private) show that our system is able to approximate the landmarks while achieving high accuracy on the classification problem (inside/outside of the organ). When comparing the entrance and exit of the studied organs, the distance between predicted and real landmarks is reduced from 1.5 to 10 times with respect to previous state-of-the-art methods., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Hagen Wenzek reports a relationship with CorporateHealth International ApS that includes: board membership., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Capsule endoscopy (CE) is a widely used medical imaging tool for the diagnosis of gastrointestinal tract abnormalities like bleeding. However, CE captures a huge number of image frames, constituting a time-consuming and tedious task for medical experts to manually inspect. To address this issue, researchers have focused on computer-aided bleeding detection systems to automatically identify bleeding in real time. This paper presents a systematic review of the available state-of-the-art computer-aided bleeding detection algorithms for capsule endoscopy. The review was carried out by searching five different repositories (Scopus, PubMed, IEEE Xplore, ACM Digital Library, and ScienceDirect) for all original publications on computer-aided bleeding detection published between 2001 and 2023. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) methodology was used to perform the review, and 147 full texts of scientific papers were reviewed. The contributions of this paper are: (I) a taxonomy for computer-aided bleeding detection algorithms for capsule endoscopy is identified; (II) the available state-of-the-art computer-aided bleeding detection algorithms, including various color spaces (RGB, HSV, etc.), feature extraction techniques, and classifiers, are discussed; and (III) the most effective algorithms for practical use are identified. Finally, the paper is concluded by providing future direction for computer-aided bleeding detection research.

Esophagitis, cancerous growths, bleeding, and ulcers are typical symptoms of gastrointestinal disorders, which account for a significant portion of human mortality. For both patients and doctors, traditional diagnostic methods can be exhausting. The major aim of this research is to propose a hybrid method that can accurately diagnose the gastrointestinal tract abnormalities and promote early treatment that will be helpful in reducing the death cases. The major phases of the proposed method are: Dataset Augmentation, Preprocessing, Features Engineering (Features Extraction, Fusion, Optimization), and Classification. Image enhancement is performed using hybrid contrast stretching algorithms. Deep Learning features are extracted through transfer learning from the ResNet18 model and the proposed XcepNet23 model. The obtained deep features are ensembled with the texture features. The ensemble feature vector is optimized using the Binary Dragonfly algorithm (BDA), Moth-Flame Optimization (MFO) algorithm, and Particle Swarm Optimization (PSO) algorithm. In this research, two datasets (Hybrid dataset and Kvasir-V1 dataset) consisting of five and eight classes, respectively, are utilized. Compared to the most recent methods, the accuracy achieved by the proposed method on both datasets was superior. The Q_SVM's accuracies on the Hybrid dataset, which was 100%, and the Kvasir-V1 dataset, which was 99.24%, were both promising.

Endoscopic investigation plays a critical role in the diagnosis of gastrointestinal (GI) diseases. Since 2001, Wireless Capsule Endoscopy (WCE) has been available for small bowel exploration and is in continuous development. Over the last decade, WCE has achieved impressive improvements in areas such as miniaturisation, image quality and battery life. As a result, WCE is currently a very useful alternative to wired enteroscopy in the investigation of various small bowel abnormalities and has the potential to become the leading screening technique for the entire gastrointestinal tract. However, commercial solutions still have several limitations, namely incomplete examination and limited diagnostic capacity. These deficiencies are related to technical issues, such as image quality, motion estimation and power consumption management. Computational methods, based on image processing and analysis, can help to overcome these challenges and reduce both the time required by reviewers and human interpretation errors. Research groups have proposed a series of methods including algorithms for locating the capsule or lesion, assessing intestinal motility and improving image quality.In this work, we provide a critical review of computational vision-based methods for WCE image analysis aimed at overcoming the technological challenges of capsules. This article also reviews several representative public datasets used to evaluate the performance of WCE techniques and methods. Finally, some promising solutions of computational methods based on the analysis of multiple-camera endoscopic images are presented.

The use of computer-aided detection models to diagnose lesions in images from wireless capsule endoscopy (WCE) is a topical endoscopic diagnostic solution. We revised our artificial intelligence (AI) model, RetinaNet, to better diagnose multiple types of lesions, including erosions and ulcers, vascular lesions, and tumors. RetinaNet was trained using the data of 1234 patients, consisting of images of 6476 erosions and ulcers, 1916 vascular lesions, 7127 tumors, and 14,014,149 normal tissues. The mean area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for each lesion were evaluated using five-fold stratified cross-validation. Each cross-validation set consisted of between 6,647,148 and 7,267,813 images from 217 patients. The mean AUC values were 0.997 for erosions and ulcers, 0.998 for vascular lesions, and 0.998 for tumors. The mean sensitivities were 0.919, 0.878, and 0.876, respectively. The mean specificities were 0.936, 0.969, and 0.937, and the mean accuracies were 0.930, 0.962, and 0.924, respectively. We developed a new version of an AI-based diagnostic model for the multiclass identification of small bowel lesions in WCE images to help endoscopists appropriately diagnose small intestine diseases in daily clinical practice., Competing Interests: No author has any possible conflict of interest.

Colon capsule endoscopy (CCE) has been available for nearly two decades but has grappled with being an equal diagnostic alternative to optical colonoscopy (OC). Due to the COVID-19 pandemic, CCE has gained more foothold in clinical practice. In this cutting-edge review, we aim to present the existing knowledge on the pros and cons of CCE and discuss whether the modality is ready for a larger roll-out in clinical settings. We have included clinical trials and reviews with the most significant impact on the current position of CCE in clinical practice and discuss the challenges that persist and how they could be addressed to make CCE a more sustainable imaging modality with an adenoma detection rate equal to OC and a low re-investigation rate by a proper preselection of suitable populations. CCE is embedded with a very low risk of severe complications and can be performed in the patient's home as a pain-free procedure. The diagnostic accuracy is found to be equal to OC. However, a significant drawback is low completion rates eliciting a high re-investigation rate. Furthermore, the bowel preparation before CCE is extensive due to the high demand for clean mucosa. CCE is currently not suitable for large-scale implementation in clinical practice mainly due to high re-investigation rates. By a better preselection before CCE and the implantation of artificial intelligence for picture and video analysis, CCE could be the alternative to OC needed to move away from in-hospital services and relieve long-waiting lists for OC., Competing Interests: The authors declared the following potential conflicts of interest with respect to the research, authorship and publication, or both, of this article: Thomas Bjørsum-Meyer has participated in advisory board meetings for Medtronic. Anastasios Koulaouzidis is consultant for Jinshan. He is director of iCERV Ltd. and cofounder of AJM Medicaps Ltd. He has received a GivenImaging Ltd-ESGE grant and material support for clinical research from SynMed/Intromedic. In the past 10 years, he has received honoraria and lecture fees from Jinshan, Dr FalkPharma UK and Ferring. He has also received educational travel support from Aquilant, Jinshan, Dr FalkPharma, Almirall and Ferring, and has participated in advisory board meetings for Tillots, Ankon and Dr FalkPharmaUK., (© The Author(s), 2022.)

Wireless capsule endoscopy (WCE) can be viewed as an innovative technology introduced in the medical domain to directly visualize the digestive system using a battery-powered electronic capsule. It is considered a desirable substitute for conventional digestive tract diagnostic methods for a comfortable and painless inspection. Despite many benefits, WCE results in poor video quality due to low frame resolution and diagnostic accuracy. Many research groups have presented diversified, low-complexity compression techniques to economize battery power consumed in the radio-frequency transmission of the captured video, which allows for capturing the images at high resolution. Many vision-based computational methods have been developed to improve the diagnostic yield. These methods include approaches for automatically detecting abnormalities and reducing the amount of time needed for video analysis. Though various research works have been put forth in the WCE imaging field, there is still a wide gap between the existing techniques and the current needs. Hence, this article systematically reviews recent WCE video compression and summarization techniques. The review's objectives are as follows: First, to provide the details of the requirement, challenges and design percepts for the low complexity WCE video compressor. Second, to discuss the most recent compression methods, emphasizing simple distributed video coding methods. Next, to review the most recent summarization techniques and the significance of using deep neural networks. Further, this review aims to provide a quantitative analysis of the state-of-the-art methods along with their advantages and drawbacks. At last, to discuss existing problems and possible future directions for building a robust WCE imaging framework., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background : The emergence of wireless capsule endoscopy (WCE) has presented a viable non-invasive mean of identifying gastrointestinal diseases in the field of clinical gastroenterology. However, to overcome its extended time of manual inspection, a computer aided automatic detection system is getting vast popularity. In this case, major challenges are low resolution and lack of regional context in images extracted from WCE videos. Methods : For tackling these challenges, in this paper a convolution neural network (CNN) based architecture, namely RAt-CapsNet, is proposed that reliably employs regional information and attention mechanism to classify abnormalities from WCE video data. The proposed RAt-CapsNet consists of two major pipelines: Compression Pipeline and Regional Correlative Pipeline. In the compression pipeline, an encoder module is designed using a Volumetric Attention Mechanism which provides 3D enhancement to feature maps using spatial domain condensation as well as channel-wise filtering for preserving relevant structural information of images. On the other hand, the regional correlative pipeline consists of Pyramid Feature Extractor which operates on image driven feature vectors to generalize and propagate local relationships of pixels from WCE abnormalities with respect to the normal healthy surrounding. The feature vectors generated by the pipelines are then accumulated to formulate a classification standpoint. Results : Promising computational accuracy of mean 98.51% in binary class and over 95.65% in multi-class are obtained through extensive experimentation on a highly unbalanced public dataset with over 47 thousand labelled. Conclusion : This outcome in turn supports the efficacy of the proposed methodology as a noteworthy WCE abnormality detection as well as diagnostic system.

The traveled distance and orientation of capsule endoscopes for each video frame are not available in commercial systems, but they would be highly relevant for physicians. Furthermore, scientific approaches lack precisely tracking the capsules along curved trajectories within the typical gastrointestinal tract. Recently, we showed that the differential static magnetic localisation method is suitable for the precise absolute localisation of permanent magnets assumed to be integrated into capsule endoscopes. Thus, in the present study, the differential method was employed to track permanent magnets in terms of traveled distance and orientation along a length trajectory of 487.5 mm, representing a model of the winding gastrointestinal tract. Permanent magnets with a diameter of 10 mm and different lengths were used to find a lower boundary for magnet size. Results reveal that the mean relative distance and orientation errors did not exceed 4.3 ± 3.3%, and 2 ± 0.6∘, respectively, when the magnet length was at least 5 mm. Thus, a 5 mm long magnet would be a good compromise between achievable tracking accuracy and magnet volume, which are essential for integration into small commercial capsules. Overall, the proposed tracking accuracy was better than that of the state of the art within a region covering the typical gastrointestinal-tract size.

Colon capsule endoscopy (CCE) was introduced nearly two decades ago. Initially, it was limited by poor image quality and short battery time, but due to technical improvements, it has become an equal diagnostic alternative to optical colonoscopy (OC). Hastened by the coronavirus disease 2019 pandemic, CCE has been introduced in clinical practice to relieve overburdened endoscopy units and move investigations to out-patient clinics. A wider adoption of CCE would be bolstered by positive patient experience, as it offers a diagnostic investigation that is not inferior to other modalities. The shortcomings of CCE include its inability to differentiate adenomatous polyps from hyperplastic polyps. Solving this issue would improve the stratification of patients for polyp removal. Artificial intelligence (AI) has shown promising results in polyp detection and characterization to minimize incomplete CCEs and avoid needless examinations. Onboard AI appears to be a needed application to enable near-real-time decision-making in order to diminish patient waiting times and avoid superfluous subsequent OCs. With this letter, we discuss the potential and role of AI in CCE as a diagnostic tool for the large bowel., Competing Interests: Conflict-of-interest statement: AK is consultant for Jinshan. He is director of iCERV Ltd and cofounder of AJM Medicaps Ltd. He has received a GivenImaging Ltd-ESGE grant, and material support for clinical research from SynMed/Intromedic. In the last ten years, he has received honoraria & lecture fees from Jinshan, Dr FalkPharma UK and Ferring. He has also received educational travel support from Aquilant, Jinshan, Dr FalkPharma, Almirall, Ferring, and has participated in advisory board meetings for Tillots, Ankon, and Dr FalkPharmaUK., (©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.)

Medical databases usually contain a significant volume of images, therefore search engines based on low-level features frequently used to retrieve similar images are necessary for a fast operation. Color, texture, and shape are the most common features used to characterize an image, however extracting the proper features for image retrievals in a similar manner with the human cognition remains a constant challenge. These algorithms work by sorting the images based on a similarity index that defines how different two or more images are, and histograms are one of the most employed methods for image comparison. In this paper, we have extended the concept of image database to the set of frames acquired following wireless capsule endoscopy (from a unique patient). Then, we have used color and texture histograms to identify very similar images (considered duplicates) and removed one of them for each pair of two successive frames. The volume reduction represented an average of 20% from the initial data set, only by removing frames with very similar informational content., Competing Interests: None to declare., (Copyright © 2014, Medical University Publishing House Craiova.)

Video capsule endoscopy (VCE) is a wireless technology used by gastroenterologists for various indications in their clinical practice. There has been significant improvement in this technology since its start about two decades ago. Specific video capsules have been made to evaluate the small bowel, colon, and esophagus. Now pan-enteric video capsule is available to assess both the small bowel and colon. VCE is a non-invasive procedure that has been tremendously evaluated for various gastrointestinal disorders, particularly small intestinal bleeding. There are specific contraindications and complications of VCE. This procedure has the technical part and video reading part. Newer software programs will come to reduce the reading time. Artificial intelligence is also coming for quick and accurate diagnosis of any positive findings during VCE. VCE is an important diagnostic test in the field of gastroenterology. Although it is an addition to optical endoscopic procedures to visualize the gastrointestinal mucosa, it has advantages and disadvantages., (Copyright 2022, Ahmed.)

Widely used traditional supervised deep learning methods require a large number of training samples but often fail to generalize on unseen datasets. Therefore, a more general application of any trained model is quite limited for medical imaging for clinical practice. Using separately trained models for each unique lesion category or a unique patient population will require sufficiently large curated datasets, which is not practical to use in a real-world clinical set-up. Few-shot learning approaches can not only minimize the need for an enormous number of reliable ground truth labels that are labour-intensive and expensive, but can also be used to model on a dataset coming from a new population. To this end, we propose to exploit an optimization-based implicit model agnostic meta-learning (iMAML) algorithm under few-shot settings for medical image segmentation. Our approach can leverage the learned weights from diverse but small training samples to perform analysis on unseen datasets with high accuracy. We show that, unlike classical few-shot learning approaches, our method improves generalization capability. To our knowledge, this is the first work that exploits iMAML for medical image segmentation and explores the strength of the model on scenarios such as meta-training on unique and mixed instances of lesion datasets. Our quantitative results on publicly available skin and polyp datasets show that the proposed method outperforms the naive supervised baseline model and two recent few-shot segmentation approaches by large margins. In addition, our iMAML approach shows an improvement of 2%-4% in dice score compared to its counterpart MAML for most experiments., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Wireless capsule endoscopy is currently considered the gold standard in the investigation of the small bowel. It is both practical for physicians and easily accepted by patients. Prior to its development, two types of imaging investigations of the small bowel were available: radiologic and endoscopic. The first category is less invasive and comfortable for patients; it presents the ensemble of the small bowel, but it may imply radiation exposure. Images are constructed based on signals emitted by various equipment and require special interpretation. Endoscopic techniques provide real-time colored images acquired by miniature cameras from inside the small bowel, require interpretation only from a medical point of view, may allow the possibility to perform biopsies, but the investigation only covers a part of the small bowel and are more difficult to accept by patients. Wireless capsule endoscopy is the current solution that overcomes a part of the previous drawbacks: it covers the entire small bowel, it provides real-time images acquired by cameras, it is painless for patients, and it represents an abundant source of information for physicians. Yet, it lacks motion control and the possibility to perform biopsies or administer drugs. However, significant effort has been oriented in these directions by technical and medical teams, and more advanced capsules will surely be available in the following years., Competing Interests: The authors declare that they have no competing interests., (Copyright: © Ionescu et al.)

Wireless capsule endoscopy (WCE) is one of the most efficient methods for the examination of gastrointestinal tracts. Computer-aided intelligent diagnostic tools alleviate the challenges faced during manual inspection of long WCE videos. Several approaches have been proposed in the literature for the automatic detection and localization of anomalies in WCE images. Some of them focus on specific anomalies such as bleeding, polyp, lesion, etc. However, relatively fewer generic methods have been proposed to detect all those common anomalies simultaneously. In this paper, a deep convolutional neural network (CNN) based model 'WCENet' is proposed for anomaly detection and localization in WCE images. The model works in two phases. In the first phase, a simple and efficient attention-based CNN classifies an image into one of the four categories: polyp, vascular, inflammatory, or normal. If the image is classified in one of the abnormal categories, it is processed in the second phase for the anomaly localization. Fusion of Grad-CAM++ and a custom SegNet is used for anomalous region segmentation in the abnormal image. WCENet classifier attains accuracy and area under receiver operating characteristic of 98% and 99%. The WCENet segmentation model obtains a frequency weighted intersection over union of 81%, and an average dice score of 56% on the KID dataset. WCENet outperforms nine different state-of-the-art conventional machine learning and deep learning models on the KID dataset. The proposed model demonstrates potential for clinical applications., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Wireless capsule endoscopy was first developed to observe the small intestine. A small capsule can be swallowed and images of gastrointestinal tract are taken with natural movement of peristalsis. Application of capsule endoscopy for observing the stomach has also received much attention as a useful alternative to esophagogastroduodenoscopy, but anatomical characteristics of the stomach have demanded technical obstacles that need to be tackled: clear visualization and active movements that could be controlled. Different methods of controlling the capsule within stomach have been studied and magnetic manipulation is the only system that is currently used in clinical settings. Magnets within the capsule can be controlled with a hand-held magnet paddle, robotic arm, and electromagnetic coil system. Studies on healthy volunteers and patients with upper gastrointestinal symptoms have shown that it is a safe and effective alternative method of observing the stomach. This work reviews different magnetic locomotion systems that have been used for observation of the stomach as an emerging new application of wireless capsule endoscopy.

Artificial intelligence (AI) has revolutionized the medical diagnostic process of various diseases. Since the manual reading of capsule endoscopy videos is a time-intensive, error-prone process, computerized algorithms have been introduced to automate this process. Over the past decade, the evolution of convolutional neural network (CNN) enabled AI to detect multiple lesions simultaneously with increasing accuracy and sensitivity. Difficulty in validating CNN performance and unique characteristics of capsule endoscopy images make computer-aided reading systems in capsule endoscopy still on a preclinical level. Although AI technology can be used as an auxiliary second observer in capsule endoscopy, it is expected that in the near future, it will effectively reduce the reading time and ultimately become an independent, integrated reading system.

The majority of current systems for automatic diagnosis considers the detection of a unique and previously known pathology. Considering specifically the diagnosis of lesions in the small bowel using endoscopic capsule images, very few consider the possible existence of more than one pathology and when they do, they are mainly detection based systems therefore unable to localize the suspected lesions. Such systems do not fully satisfy the medical community, that in fact needs a system that detects any pathology and eventually more than one, when they coexist. In addition, besides the diagnostic capability of these systems, localizing the lesions in the image has been of great interest to the medical community, mainly for training medical personnel purposes. So, nowadays, the inclusion of the lesion location in automatic diagnostic systems is practically mandatory. Multi-pathology detection can be seen as a multi-object detection task and as each frame can contain different instances of the same lesion, instance segmentation seems to be appropriate for the purpose. Consequently, we argue that a multi-pathology system benefits from using the instance segmentation approach, since classification and segmentation modules are both required complementing each other in lesion detection and localization. According to our best knowledge such a system does not yet exist for the detection of WCE pathologies. This paper proposes a multi-pathology system that can be applied to WCE images, which uses the Mask Improved RCNN (MI-RCNN), a new mask subnet scheme which has shown to significantly improve mask predictions of the high performing state-of-the-art Mask-RCNN and PANet systems. A novel training strategy based on the second momentum is also proposed for the first time for training Mask-RCNN and PANet based systems. These approaches were tested using the public database KID, and the included pathologies were bleeding, angioectasias, polyps and inflammatory lesions. Experimental results show significant improvements for the proposed versions, reaching increases of almost 7% over the PANet model when the new proposed training approach was employed., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background and Aim: The implementation rates of pediatric gastrointestinal endoscopy are increasing with advancements in the devices used and pediatricians' skills. As part of the Japan Pediatric Endoscopy Study Group, we aimed to investigate the rates of pediatric gastrointestinal endoscopy use and the associated adverse events through a nationwide survey., Methods: A questionnaire was sent to 630 institutions in Japan. The numbers of pediatric gastrointestinal endoscopy cases and adverse events occurring during endoscopy, from April 2011 to March 2016, were investigated., Results: Responses were obtained from 445 facilities. The total number of pediatric gastrointestinal endoscopies was 37 447 and that of endoscopic examinations was 32 219 (86.0%), with esophagogastroduodenoscopy accounting for 18 484 cases; ileal colonoscopy, 11 936; endoscopic retrograde cholangiopancreatography, 389; wireless capsule endoscopy, 897; and balloon-assisted enteroscopy, 513. The number of endoscopic treatments was 5228, followed by balloon dilatation (1703), foreign body removal (989), and polypectomy (822); 201 adverse events (0.54%) occurred, 79 of which presented during endoscopic examination (0.25%). Eight serious perforations were noted in 0.0054% and 0.025% of those undergoing esophagogastroduodenoscopy and colonoscopy, respectively. Overall, 122 adverse events (2.33%) occurred in association with endoscopic treatment. One case of cardiopulmonary arrest occurred because of accidental extubation. However, no deaths occurred., Conclusion: Endoscopic examinations had a slightly higher adverse event rate, because of an increase in endoscopic retrograde cholangiopancreatography and small intestine enteroscopy, than that reported in previous studies, but the adverse event rate of endoscopic treatment did not increase., (© 2020 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.)

Wireless capsule endoscopy is the commonly employed modality in the treatment of gastrointestinal tract pathologies. However, the time taken for interpretation of these images is very high due to the large volume of images generated. Automated detection of disorders with these images can facilitate faster clinical interventions. In this paper, we propose an automated system based on Gaussian mixture model superpixels for bleeding detection and segmentation of candidate regions. The proposed system is realized with a classic binary support vector machine classifier trained with seven features including color and texture attributes extracted from the Gaussian mixture model superpixels of the WCE images. On detection of bleeding images, bleeding regions are segmented from them, by incrementally grouping the superpixels based on deltaE color differences. Tested with standard datasets, this system exhibits best performance compared to the state-of-the-art approaches with respect to classification accuracy, feature selection, computational time, and segmentation accuracy. The proposed system achieves 99.88% accuracy, 99.83% sensitivity, and 100% specificity signifying the effectiveness of the proposed system in bleeding detection with very few classification errors.

Wireless capsule endoscopy is a non-invasive, wireless imaging tool that has developed rapidly over the last several years. One of the main limiting factors using this technology is that it produces a huge number of images, whose analysis, to be done by a doctor, is an extremely time-consuming process. In this research area, the management of this problem has been addressed with the development of Computer-aided Diagnosis systems thanks to which the automatic inspection and analysis of images acquired by the capsule has clearly improved. Recently, a big advance in classification of endoscopic images is achieved with the emergence of deep learning methods. The proposed expert system employs three pre-trained deep convolutional neural networks for feature extraction. In order to construct efficient feature sets, the features from VGG19, InceptionV3 and ResNet50 models are then selected and fused using the minimum Redundancy Maximum Relevance method and different fusion rules. Finally, supervised machine learning algorithms are employed to classify the images using the extracted features into two categories: bleeding and nonbleeding images. For performance evaluation a series of experiments are performed on two standard benchmark datasets. It has been observed that the proposed architecture outclass the single deep learning architectures, with an average accuracy in detection bleeding regions of 97.65 % and 95.70 % on well-known state-of-the-art datasets considering three different fusion rules, with the best combination in terms of accuracy and training time obtained using mean value pooling as fusion rule and Support Vector Machine as classifier., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

One of the most recent non-invasive technologies to examine the gastrointestinal tract is wireless capsule endoscopy (WCE). As there are thousands of endoscopic images in an 8-15 h long video, an evaluator has to pay constant attention for a relatively long time (60-120 min). Therefore the possibility of the presence of pathological findings in a few images (displayed for evaluation for a few seconds only) brings a significant risk of missing the pathology with all negative consequences for the patient. Hence, manually reviewing a video to identify abnormal images is not only a tedious and time consuming task that overwhelms human attention but also is error prone. In this paper, a method is proposed for the automatic detection of abnormal WCE images. The differential box counting method is used for the extraction of fractal dimension (FD) of WCE images and the random forest based ensemble classifier is used for the identification of abnormal frames. The FD is a well-known technique for extraction of features related to texture, smoothness, and roughness. In this paper, FDs are extracted from pixel-blocks of WCE images and are fed to the classifier for identification of images with abnormalities. To determine a suitable pixel block size for FD feature extraction, various sizes of blocks are considered and are fed into six frequently used classifiers separately, and the block size of 7×7 giving the best performance is empirically determined. Further, the selection of the random forest ensemble classifier is also done using the same empirical study. Performance of the proposed method is evaluated on two datasets containing WCE frames. Results demonstrate that the proposed method outperforms some of the state-of-the-art methods with AUC of 85% and 99% on Dataset-I and Dataset-II respectively., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: In this paper, we have presented a new custom smart CMOS image sensor (CIS) for low power wireless capsule endoscopy., Method: The proposed new smart CIS includes a 256 × 256 current mode pixels array with a new on-chip adaptive neuro-fuzzy inference system that has been used to diagnosing bleeding images. We use a new pinned photodiode to realize the current mode of active pixels in the standard CMOS process. The proposed chip has been implemented in 0.18 μm CMOS 1P6M TSMC RF technology with a die area of 7 mm × 8 mm., Results and Conclusion: A built-in smart bleeding detection system on CIS leads to decrease in the RF transmitter power consumption near zero. The average power dissipation of the proposed smart CIS is 610 μW., Competing Interests: There are no conflicts of interest., (Copyright: © 2020 Journal of Medical Signals & Sensors.)

Wireless capsule endoscopy (WCE) is a process in which a patient swallows a camera-embedded pill-shaped device that passes through the gastrointestinal (GI) tract, captures and transmits images to an external receiver. WCE devices are considered as a replacement of conventional endoscopy methods which are usually painful and distressful for the patients. WCE devices produce over 60,000 images typically during their course of operation inside the GI tract. These images need to be examined by expert physicians who attempt to identify frames that contain inflammation/disease. It can be hectic for a physician to go through such a large number of frames, hence computer-aided detection methods are considered an efficient alternative. Various anomalies can take place in the GI tract of a human being but the most important and common ones and the aim of this survey are ulcers, polyps, and tumors. In this paper, we have presented a survey of contemporary computer-aided detection methods that take WCE images as input and classify those images in a diseased/abnormal or disease-free/normal image. We have considered methods that detect tumors, polyps and ulcers, as these three diseases lie in the same category. Furthermore, general abnormalities and bleeding inside the GI tract may be the symptoms of these diseases; so an attempt is also made to enlighten the research work done for abnormalities and bleeding detection inside WCE images. Several studies have been included with in-depth detail of their methodologies, findings, and conclusions. Also, we have attempted to classify these methods based on their technical aspects. A formal discussion and comparison of recent review articles are also provided to have a benchmark for the presented survey mentioning their limitations. This paper also includes a proposed classification approach where a cascade approach of neural networks is presented for the classification of tumor, polyp, and ulcer jointly along with data set specifications and results., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Research interest in ingestible Wireless Capsule Endoscopy (WCE) studies in humans showed better results than conventional invasive probe endoscopy methods. Because of the structure and the position of the small intestine, proper scanning cannot be done in the area using traditional endoscopic methods. For patient comfort, continuous developments have been suggested in capsule endoscopy designs in terms of the quality of images transmitted, capsule orientation, positioning of the capsule from outside the body, link budget analysis, impedance matching and capsule miniaturization. To improve the image quality, transmission efficiency of the antenna has to be improved. This has led to the development of many antenna structures in an ingestible capsule system. Literatures have identified Med Radio and ISM (Industrial, Scientific and Medical) band as operation bands for the WCE systems. This review aims to highlight: (1) design considerations for various antenna types, (2) miniaturization techniques, (3) operating bands, specifications and various design challenges and (4) research gap, advanced design technologies and targets of ingestible antenna system. The main aim of this paper is to tutorial the up-to-date information on the recent antenna designing techniques and challenges for ingestible system.

Wireless capsule endoscopy (WCE) is noninvasive, painless, and riskless on detection for gastrointestinal disease. It attracts increasing attention. Wireless power transfer (WPT) technology is utilized to supply power for WCE. Receiving coil (RC) of WPT is capsulated into WCE. Its position and direction change all through gastrointestinal tract. Transmitting coil (TC) is worn by the patient. So the mutual inductance varies all the time. It should be studied to ensure sufficient receiving power. However, existing analytical methods do not reach satisfactory accuracy. They can only solve simple cases with positional misalignment. Numerical simulation models are time-consuming. Furthermore, an entirely new simulation must be repeated when any change in alignment occurs. Thus, based on geometry and misalignment of RC and TC, a model for mutual inductance is proposed. Compared with analytical methods, it is applicable to not only circular and rectangular RC, but also the elliptic, with directional misalignment. It costs below 0.1% of computational time of the simulation for the same accuracy. Moreover, any change in misalignment is easily handled by a simple change of parameter in the model. It reaches a tradeoff between computational accuracy and time. Receiving power is evaluated rapidly and accurately with proposed model.

Background and Aim: Detecting blood content in the gastrointestinal tract is one of the crucial applications of capsule endoscopy (CE). The suspected blood indicator (SBI) is a conventional tool used to automatically tag images depicting possible bleeding in the reading system. We aim to develop a deep learning-based system to detect blood content in images and compare its performance with that of the SBI., Methods: We trained a deep convolutional neural network (CNN) system, using 27 847 CE images (6503 images depicting blood content from 29 patients and 21 344 images of normal mucosa from 12 patients). We assessed its performance by calculating the area under the receiver operating characteristic curve (ROC-AUC) and its sensitivity, specificity, and accuracy, using an independent test set of 10 208 small-bowel images (208 images depicting blood content and 10 000 images of normal mucosa). The performance of the CNN was compared with that of the SBI, in individual image analysis, using the same test set., Results: The AUC for the detection of blood content was 0.9998. The sensitivity, specificity, and accuracy of the CNN were 96.63%, 99.96%, and 99.89%, respectively, at a cut-off value of 0.5 for the probability score, which were significantly higher than those of the SBI (76.92%, 99.82%, and 99.35%, respectively). The trained CNN required 250 s to evaluate 10 208 test images., Conclusions: We developed and tested the CNN-based detection system for blood content in CE images. This system has the potential to outperform the SBI system, and the patient-level analyses on larger studies are required., (© 2019 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.)

Wireless capsule endoscopy (WCE) has been widely used in gastrointestinal (GI) diagnosis that allows the physicians to examine the interior wall of the human GI tract through a pain-free procedure. However, there are still several limitations of the technology, which limits its functionality, ultimately limiting its wide acceptance. Its counterpart, the wired endoscopic system is a painful procedure that demotivates patients from going through the procedure, and adversely affects early diagnosis. Furthermore, the current generation of capsules is unable to automate the detection of abnormality. As a result, physicians are required to spend longer hours to examine each image from the endoscopic capsule for abnormalities, which makes this technology tiresome and error-prone. Early detection of cancer is important to improve the survival rate in patients with colorectal cancer. Hence, a fluorescence-imaging-based endoscopic capsule that automates the detection process of colorectal cancer was designed and developed in our lab. The proof of concept of this endoscopic capsule was tested on porcine intestine and liquid phantom. The proposed WCE system offers great possibilities for future applicability in selective and specific detection of other fluorescently labelled cancers.

Wireless Capsule Endoscopy is a state-of-the-art technology for medical diagnoses of gastrointestinal diseases. The amount of data produced by an endoscopic capsule camera is huge. These vast amounts of data are not practical to be saved internally due to power consumption and the available size. So, this data must be transmitted wirelessly outside the human body for further processing. The data should be compressed and transmitted efficiently in the domain of power consumption. In this paper, a new approach in the design and implementation of a low complexity, multiplier-less compression algorithm is proposed. Statistical analysis of capsule endoscopy images improved the performance of traditional lossless techniques, like Huffman coding and DPCM coding. Furthermore the Huffman implementation based on simple logic gates and without the use of memory tables increases more the speed and reduce the power consumption of the proposed system. Further analysis and comparison with existing state-of-the-art methods proved that the proposed method has better performance.

Recent facts and figures published in various studies in the US show that approximately 27,510 new cases of gastric infections are diagnosed. Furthermore, it has also been reported that the mortality rate is quite high in diagnosed cases. The early detection of these infections can save precious human lives. As the manual process of these infections is time-consuming and expensive, therefore automated Computer-Aided Diagnosis (CAD) systems are required which helps the endoscopy specialists in their clinics. Generally, an automated method of gastric infection detections using Wireless Capsule Endoscopy (WCE) is comprised of the following steps such as contrast preprocessing, feature extraction, segmentation of infected regions, and classification into their relevant categories. These steps consist of various challenges that reduce the detection and recognition accuracy as well as increase the computation time. In this review, authors have focused on the importance of WCE in medical imaging, the role of endoscopy for bleeding-related infections, and the scope of endoscopy. Further, the general steps and highlighting the importance of each step have been presented. A detailed discussion and future directions have been provided at the end., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

This paper proposes an ultra-miniaturised, flexible 5.8 GHz antenna to give better performance for an ingestible wireless capsule endoscopy system. The use of an artificial magnetic conductor (AMC) unit cell in this design helps in enhancing the gain, improving the efficiency and reducing the size at resonance. This novel AMC-based layer is modelled as a radiator, and is excited by a complementary strip feed. The proposed design is printed on a flexible polyimide material of size 4.6 × 7.6 × 0.15 mm, with dielectric constant 3.5 and loss tangent 0.008. The antenna has an omni-directional radiation pattern with gain 1.64 dBi. The size is reduced drastically by bending the structure to fit inside a standard-sized ingestible capsule of 26 × 11 mm. The performance metrics of the antenna after placing inside the wireless ingestible capsule are presented with the help of an analysis of the reflection coefficient curves, radiation pattern, specific absorption rate (SAR) and field distributions. In addition, different positions and orientations of the antenna and different bending conditions are used, and these cases are solved using our AMC-based unit cell radiator. We carry out simulations using High Frequency Structure Simulator (HFSS) and measurements are made by immersing the fabricated antenna in minced meat. The results of this novel structure are compared with those of other ingestible antennas proposed in the literature. The proposed design has a SAR value of 0.82505 W/kg at around 5.8 GHz over 1 g of tissue, thereby redirecting the radiation away from the human body. This conformal AMC structure achieved better miniaturisation while maintaining the antenna resonance and SAR within permissible limits., Competing Interests: Declaration of competing interest The authors declare that there is no conflict of interest., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Wireless capsule endoscopy (WCE) is a video technology to inspect abnormalities, like bleeding in the gastrointestinal tract. In order to avoid a complex and long duration manual review process, automatic bleeding detection schemes are developed that mainly utilize features extracted from WCE images. In feature-based bleeding detection schemes, either global features are used which produce averaged characteristics ignoring the effect of smaller bleeding regions or local features are utilized that cause large feature dimension. In this paper, pixels of interest (POI) in a given WCE image are determined using a linear separation scheme, local spatial features are then extracted from the POI and finally, a suitable characteristic probability density function (PDF) is fitted over the resulting feature space. The proposed PDF model fitting based approach not only reduces the computational complexity but also offers more consistent representation of a class. Details analysis are carried out to find the best suitable PDF and it is found that fitting of Rayleigh PDF model to the local spatial features is best suited for bleeding detection. For the purpose of classification, the fitted PDF parameters are used as features in the supervised support vector machine classifier. Pixels residing in the close vicinity of the POI are further classified with the help of an unsupervised clustering-based scheme to extract more precise bleeding regions. A large number of WCE images obtained from 30 publicly available WCE videos are used for performance evaluation of the proposed scheme and the effects on classification performance due to the changes in PDF models, block statistics, color spaces, and classifiers are experimentally analyzed. The proposed scheme shows satisfactory performance in terms of sensitivity (97.55%), specificity (96.59%) and accuracy (96.77%) and the results obtained by the proposed method outperforms the results reported for some state-of-the-art methods., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

With the rapid evolution in modern multimedia networks and systems, services such as telemedicine and tele-surgery are becoming more popular. Quality estimation and monitoring of medical videos is becoming important not only in the field of research, but also in real-time applications and services. The state-of-the-art video quality metric (VQM) called Video Multimethod Assessment Fusion (VMAF) is a promising solution for quality estimation of videos impaired by compression and scaling artifacts. The metric was developed by Netflix for entertainment video content and its good performance does not necessarily extend to medical videos. This paper focuses on evaluating the performance of VMAF in the context of quality assessment (QA) for medical videos. We consider in this paper medical videos compressed via High Efficiency Video Coding (HEVC) and refer in particular to medical ultrasound videos and wireless capsule endoscopy (WCE) videos for the performance estimation of VMAF. The correlation between the subjective scores of these two datasets and VMAF's quality estimates is studied and presented. The results show that VMAF outperforms other state-of-the-art VQMs in the context of WCE videos, but this is not the case for medical ultrasound videos., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Background and Aim: Wireless capsule endoscopy (WCE) has become an increasingly utilized imaging modality for the evaluation of gastrointestinal bleeding. There is a paucity of data evaluating the safety and use of WCE in patients with implantable cardiac devices., Methods: A retrospective chart review of all patients who had a WCE at Loyola University Medical Center in Maywood, IL, USA completed between January 2007 and December 2016 identified patients with internal cardiac devices and obscure gastrointestinal bleeding. Patient WCE footage was viewed in its entirety before creating a final report to ensure no gaps in footage and video quality., Results: No patient complaints were documented during the 8-h procedure duration, and there were no cardiac abnormalities noted on telemetry. There were no device-related complications documented in the 30-day postprocedure time period. Postprocedure analysis of the WCE recordings demonstrated no interference in WCE image quality (loss of images or gaps in video) or duration., Conclusions: There is no significant interference between WCE and implantable cardiac devices, and it appears to be safe to use., (© 2019 The Authors. JGH Open: An open access journal of gastroenterology and hepatology published by Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.)

Background: Crohn's disease (CD) can affect the entire gastrointestinal tract. Proximal small bowel (SB) lesions are associated with a significant risk of stricturing disease and multiple abdominal surgeries. The assessment of SB in patients with CD is therefore necessary because it may have a significant impact on prognosis with potential therapeutic implications. Because of the weak correlation that exists between symptoms and endoscopic disease activity, the "treat-to-target" paradigm has been developed, and the associated treatment goal is to achieve and maintain deep remission, encompassing both clinical and endoscopic remission. Small bowel capsule endoscopy (SBCE) allows to visualize the mucosal surface of the entire SB. At that time, there is no recommendation regarding the use of SBCE during follow-up., Aim: To investigate the impact of SBCE in a treat-to-target strategy in patients with CD., Methods: An electronic literature search was conducted in PubMed and Cochrane library using the following search terms: "capsule endoscopy", in combination with "Crohn's disease" and "treat-to-target" or synonyms. Two authors independently reviewed titles and abstracts identified by the search strategy after duplicates were removed. Following the initial screening of abstracts, all articles containing information about SBCE in the context of treat-to-target strategy in patients with CD were included. Full-text articles were retrieved, reference lists were screened manually to identify additional studies., Results: Forty-seven articles were included in this review. Two indexes are currently used to quantify disease activity using SBCE, and there is good correlation between them. SBCE was shown to be useful for disease reclassification in patients who are suspected of having or who are diagnosed with CD, with a significant incremental diagnostic yield compared to other diagnostic modalities. Nine studies also demonstrated that the mucosal healing can be evaluated by SBCE to monitor the effect of medical treatment in patients with CD. This review also demonstrated that SBCE can detect post-operative recurrence to a similar extent as ileocolonoscopy, and proximal SB lesions that are beyond the reach of the colonoscope in over half of the patients., Conclusion: SBCE could be incorporated in the treat-to-target algorithm for patients with CD. Randomized controlled trials are required to confirm its usefulness and reliability in this indication., Competing Interests: Conflict-of-interest statement: The authors declare that they have no conflicts of interest.

Wireless capsule endoscopy (WCE) is an effective technology that can be used to make a gastrointestinal (GI) tract diagnosis of various lesions and abnormalities. Due to a long time required to pass through the GI tract, the resulting WCE data stream contains a large number of frames which leads to a tedious job for clinical experts to perform a visual check of each and every frame of a complete patient's video footage. In this paper, an automated technique for bleeding detection based on color and texture features is proposed. The approach combines the color information which is an essential feature for initial detection of frame with bleeding. Additionally, it uses the texture which plays an important role to extract more information from the lesion captured in the frames and allows the system to distinguish finely between borderline cases. The detection algorithm utilizes machine-learning-based classification methods, and it can efficiently distinguish between bleeding and nonbleeding frames and perform pixel-level segmentation of bleeding areas in WCE frames. The performed experimental studies demonstrate the performance of the proposed bleeding detection method in terms of detection accuracy, where we are at least as good as the state-of-the-art approaches. In this research, we have conducted a broad comparison of a number of different state-of-the-art features and classification methods that allows building an efficient and flexible WCE video processing system., (© 2019 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

Background: Magnetic resonance enterography (MRE) and wireless capsule endoscopy (WCE) are equally accepted modalities for noninvasive screening of small bowel involvement (SBI) in children with Crohn's disease (CD) and indeterminate colitis (IC) albeit there is a paucity of data comparing the two and thereby guiding the clinician in selecting the ideal diagnostic approach. Therefore, the goal of this study is to provide additional evidence for capsule endoscopy role in the evaluation of established Crohn's disease exacerbation compared to MRE in relation to Pediatric Crohn's Disease Activity Index (PCDAI), and histological indices., Aim: To prospectively compare the findings of MRE and WCE and their agreement with PCDAI or histology in children with CD or IC., Methods: Consecutive patients diagnosed with CD and IC were screened for inclusion. After informed consent, patient's demographic and clinical data was abstracted. The current pediatric disease activity index (PCDAI) and endoscopic findings were included. Patients underwent MRE and WCE including preprocedural patency capsule within a maximum of 7 d of each other. Pathological presence of active small bowel disease in ileal and duodenal biopsies were collected if the endoscopy was performed within 2 mo of the WCE study. Patients who failed to pass the PC were excluded from the study. WCE was read by two different experienced gastroenterologists (Attard TM and Colombo JM) blinded to each other's findings and to the findings on MRE (Mardis NJ). Agreement between WCE reviewers, WCE and MRE findings and concordance between positive PCDAI and SBI based on MRE compared with WCE was computed., Results: Forty-five patients were included in the study, 18 withdrew and 27 (20 males and 20 CD), mean age (standard deviation) 13.46 (2.4) years, completed the study protocol. There were no instances of capsule retention. Concordance between gastroenterologist reviewers was excellent for the diagnosis of small intestinal CD with good correlation between the two Lewis scores ( r = 0.875, P < 0.001). Concordance between WCE and MRE was poor (69%). In CD patients, when both MRE and WCE were compared using PCDAI > 10 as the standard reference reflecting active small intestinal CD, the sensitivity of MRE and WCE were 100% and 83% respectively and the specificity of MRE and WCE were 57.14% and 78.6%, respectively. If the histology in ileum or/and duodenum was used as the reference for active small bowel involvement, WCE had a higher specificity as compared to MRE (83.3% vs 50%). In patients with Crohn's disease, those with a positive PCDAI (> 10) were more likely to have a positive WCE as compared to those with a negative PCDAI (83% vs 21%; P = 0.018)., Conclusion: We suggest that MRE and WCE have a complementary role in the assessment of SBI in CD. WCE detected SBI with a much higher specificity while MRE had a higher sensitivity., Competing Interests: Conflict-of-interest statement: All authors of this manuscript have no conflict of interest to disclose.

Detection of abnormalities in wireless capsule endoscopy (WCE) images is a challenging task. Typically, these images suffer from low contrast, complex background, variations in lesion shape and color, which affect the accuracy of their segmentation and subsequent classification. This research proposes an automated system for detection and classification of ulcers in WCE images, based on state-of-the-art deep learning networks. Deep learning techniques, and in particular, convolutional neural networks (CNNs), have recently become popular in the analysis and recognition of medical images. The medical image datasets used in this study were obtained from WCE video frames. In this work, two milestone CNN architectures, namely the AlexNet and the GoogLeNet are extensively evaluated in object classification into ulcer or non-ulcer. Furthermore, we examine and analyze the images identified as containing ulcer objects to evaluate the efficiency of the utilized CNNs. Extensive experiments show that CNNs deliver superior performance, surpassing traditional machine learning methods by large margins, which supports their effectiveness as automated diagnosis tools.