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Static magnetic localization is widely employed to localize capsule endoscopes. A permanent magnet is integrated into the capsule, and a sensor array outside the body measures its magnetic field. However, the geomagnetic flux density distorts the localization when no compensation method is applied. In this study, an optimized differential geomagnetic compensation method is proposed. A sensor array adapted to the typical abdomen size with 12 equally oriented sensors was used. The sensors are grouped into 26 pairs, and by subtracting the measured values of those, the geomagnetic field cancels out. This method was experimentally evaluated within a localization region of the size of a typical gastrointestinal tract. Static and dynamic scenarios in domestic environments were applied to evaluate the system in daily life situations. Moreover, the magnet was continuously moved, while the traveled distance was recorded. The mean position and orientation errors for the static and dynamic scenarios did not exceed 4 mm and 2°, respectively. Furthermore, the traveled distance of the magnet was tracked with an accuracy of 3.4 mm. Therefore, it was revealed that the proposed system is applicable in daily life scenarios and capable of precisely tracking the traveled distance of the capsule inside the intestine to locate abnormalities. Overall, the results significantly outperform state-of-the-art methods. [ABSTRACT FROM AUTHOR]

A differentially fed capsule antenna with a complex input impedance is proposed in this letter. The antenna maintains a circular flat shape with a diameter of 10 mm and operates at the desired band of the RF chip (2.4–2.525 GHz). With the utilization of an inductively coupled fed structure and dual meandered radiators, a –10 dB impedance bandwidth of 10.2% is achieved. In the meantime, the high impedance tunability can be realized by adjusting corresponding structural parameters. The operating mechanisms and parametric studies are explored in detail to investigate the characteristic of the proposed structure. The capsule antenna prototype is fabricated, assembled, and measured according to the practical scenario. The measured results highly agree with the simulation ones, which indicate that the proposed antenna could be a good candidate for wireless capsule endoscopy applications. [ABSTRACT FROM AUTHOR]

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.

Automatic classification and segmentation of wireless capsule endoscope (WCE) images are two clinically significant and relevant tasks in a computer-aided diagnosis system for gastrointestinal diseases. Most of existing approaches, however, considered these two tasks individually and ignored their complementary information, leading to limited performance. To overcome this bottleneck, we propose a deep synergistic interaction network (DSI-Net) for joint classification and segmentation with WCE images, which mainly consists of the classification branch (C-Branch), the coarse segmentation (CS-Branch) and the fine segmentation branches (FS-Branch). In order to facilitate the classification task with the segmentation knowledge, a lesion location mining (LLM) module is devised in C-Branch to accurately highlight lesion regions through mining neglected lesion areas and erasing misclassified background areas. To assist the segmentation task with the classification prior, we propose a category-guided feature generation (CFG) module in FS-Branch to improve pixel representation by leveraging the category prototypes of C-Branch to obtain the category-aware features. In such way, these modules enable the deep synergistic interaction between these two tasks. In addition, we introduce a task interaction loss to enhance the mutual supervision between the classification and segmentation tasks and guarantee the consistency of their predictions. Relying on the proposed deep synergistic interaction mechanism, DSI-Net achieves superior classification and segmentation performance on public dataset in comparison with state-of-the-art methods. The source code is available at https://github.com/CityU-AIM-Group/DSI-Net. [ABSTRACT FROM AUTHOR]

• Developed GINet model specifically designed to detect gastrointestinal diseases such as angiectasia, lymphangiectasia, bleeding, and ulcer, achieving an impressive 99.6% sensitivity and 99.86% specificity in testing. • Implemented domain adaptation techniques to effectively mitigate any potential biases that may arise from merging video frames of the same patients, ensuring the model's robustness and reliability across diverse clinical scenarios. • Used GradCAM and Guided-GradCAM-based visualization to pinpoint detected lesions, enhancing the interpretability of the model's decision-making process and providing valuable insights for clinicians. Detection of gastrointestinal (GI) diseases involves several expensive, challenging, and time-consuming procedures. Deep learning techniques-based computer-aided diagnosis can significantly reduce the costs associated with examinations while enhancing the accuracy and speed of diagnosis. This study developed a 13-layered convolutional neural network (CNN) model named as GINet for diagnosing GI diseases such as angiectasia, lymphangiectasia, GI bleeding, and ulcer. The model was trained, validated, and tested on 3658 wireless capsule endoscopy images. The GINet achieved an overall classification accuracy of 99% (standard error (SE): 0.7%) on the test dataset. The model achieved an overall sensitivity of 99.6 % (SE:0.5%) and specificity of 99.86%(SE:0.3%) on the test. We additionally employ GradCAM and Guided-GradCAM technique to enhance the interpretability and localization of detected lesions. Furthermore, the study effectively addressed biases that could arise from combining video frames of the same patients through domain adaptation techniques, ensuring accurate and unbiased analysis. The results revealed that the GINet can classify GI diseases with higher sensitivity and specificity. Furthermore, the proposed approach has the potential for first-hand mass screening efforts, where advanced disease stages can be identified. This approach additionally helps gastroenterologists make quick and accurate treatment decisions at reduced time and cost. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy (WCE) has become an irreplaceable tool for diagnosing small intestinal diseases, and detecting the outliers in WCE images automatically remains as a hot research topic. Considering the difficulties in obtaining sufficient labeled WCE data, it is necessary to develop the diagnosis model which works well with only little labeled or even unlabeled training samples. In this paper, a novel semi-supervised deep-structured framework is introduced to solve the problem of outlier detection in WCE images. The key idea of our model is to mine the anomalous graphical patterns existed in the image by analyzing the spatial-scale trends of sequential image regions. Three main contributions are concluded: 1) we integrate a convolutional neural network into long short term memory network, so that the intrinsic differences between outliers and normal instances could be captured. Besides, 2) a assessment model is built by using various signs of anomaly occurrence and fake outliers knowledge learned during the training stage, which enhances the outlier alarm accuracy significantly. Furthermore, 3) a nest-structured training method is proposed, which helps our model achieving efficient training process. Experimental results on the real WCE images demonstrate the effectiveness of our model.

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.

Wireless Capsule Endoscopy (WCE) is a revolutionary technique for screening of gastrointestinal (GI) tract. However, WCE needs automated methods to reduce the time required for viewing its large image data and also to improve the accuracy of inspection. In this work, we propose novel sparse coded features to detect bleeding in WCE images. We acquire Scale-Invariant Feature Transform (SIFT) based key points as regions of interest of an image. Further, we compute SIFT and uniform Local Binary Pattern (LBP) features around the key points from an image. After that sparse coded features are obtained and support vector machine (SVM) is used for image classification. We provide comprehensive experimental results and also comparison with some recent bleeding detection methods and with traditional ways of computing sparse coded features. The best results are obtained with a dictionary size of 300 atoms. The classification accuracy we achieve with the proposed approach is 98.18%. The results presented in the paper indicate that the proposed method is reliable for bleeding detection in WCE images. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy (WCE) is a significant modern technique for observing the whole gastroenterological tract to diagnose various diseases like bleeding, ulcer, tumor, Crohn's disease, and polyps in a non-invasive manner. However, it will make a substantial onus for physicians like human oversight errors with time consumption for manual checking of a vast amount of image frames. These problems motivate the researchers to employ a computer-aided system to classify the particular information from the image frames. Therefore, a computer-aided system based on the color threshold and morphological operation has been proposed in this research to recognize specified bleeding images from the WCE. Besides, A unique classifier, quadratic support vector machine (QSVM) has been employed for classifying the bleeding and non-bleeding images with the statistical feature vector in HSV color space. After extensive experiments on clinical data, 95.8% accuracy, 95% sensitivity, 97% specificity, 80% precision, 99% negative predicted value and 85% F1 score has been achieved, which outperforms some of the existing methods in this regard. It is expected that this methodology would bring a significant contribution to the WCE technology. [ABSTRACT FROM AUTHOR]

Volume restriction, low battery capacity and low focal length camera in Wireless Capsule Endoscopy (WCE) result in images being of lower contrast, darkness, un-even brightness and image degradations. In this paper, a Laplacian pyramid based Image fusion of Contrast Limited Adaptive Histogram Equalization (CLAHE) and Multi-Scale Retinex with Colour Restoration (MSRCR) to integrate information of both techniques have been proposed. The proposed technique combines the information from CLAHE and MSRCR, reduces uncertainties, redundancy and maximizes the information. Experimental evaluation reveals that the proposed method outperforms existing methods in terms of Peak Signal to Noise Ratio and Structural Similarity Index Measure. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy (WCE) is a device that can move through human body and capture the small bowel entirely. Thus, it is presented as an excellent diagnostic tool for evaluation of gastrointestinal diseases compared with traditional endoscopies. However, the diagnosis by the physicians is tedious since it requires reviewing the video extracted from the capsule and analysing all of its frames. This tedious task has fuelled the efforts of researchers to provide automated diagnostic techniques for WCE frameworks to detect symptoms of gastrointestinal illness. In this paper, a new computer-aided diagnosis method for abnormalities detection in WCE images is proposed. After a preprocessing step, we extract from these images the descriptor we feed to a kernel extreme learning machine to perform the classification process. The descriptor used in this work is a combination between the histogram of oriented gradients (HOG) that were extracted using the hue component of the HSV colour space, and a modified rotation-invariant local binary pattern. The proposed approach has been tested on different datasets, and the results obtained are satisfactory when compared to the state-of-the-art works. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy is the most innovative technology to perceive the entire gastrointestinal (GI) tract in recent times. It can diagnose inner diseases like bleeding, ulcer, tumor, Crohn's disease, and polyps in a discretion way. It creates immense pressure and onus for clinicians to perceive a huge number of image frames, which is time-consuming and makes human oversight errors. Therefore a computer-automated system has been introduced for bleeding detection. A unique fuzzy logic technique is proposed to extract the specified bleeding and non-bleeding information from the image data. A particular quadratic support vector machine (QSVM) classifier is employed to classify the obtained statistical features from the bleeding and non-bleeding images incorporating principal component analysis (PCA). After extensive experiments on clinical data, 98% sensitivity, 98.4% accuracy, 98% specificity, 93% precision, 95.4% F1-score, and 99% negative predicted value have been achieved, which outperforms some of the states of art methods in this regard. It is optimistic that the proposed methodology would significantly contribute to bleeding detection techniques and diminish the additional onus of the physicians. [ABSTRACT FROM AUTHOR]

With the advent in services such as telemedicine and telesurgery, provision of continuous quality monitoring for these services has become a challenge for the network operators. Quality standards for provision of such services are application specific as medical imagery is quite different than general purpose images and videos. This paper presents a novel full reference objective video quality metric that focuses on estimating the quality of wireless capsule endoscopy (WCE) videos containing bleeding regions. Bleeding regions in gastrointestinal tract have been focused in this research, as bleeding is one of the major reasons behind several diseases within the tract. The method jointly estimates the diagnostic as well as perceptual quality of WCE videos, and accurately predicts the quality, which is in high correlation with the subjective differential mean opinion scores (DMOS). The proposed combines motion quality estimates, bleeding regions' quality estimates based on support vector machine (SVM) and perceptual quality estimates using the pristine and impaired WCE videos. Our method Quality Index for Bleeding Regions in Capsule Endoscopy (QI-BRiCE) videos is one of its kind and the results show high correlation in terms of Pearson's linear correlation coefficient (PLCC) and Spearman's rank order correlation coefficient (SROCC). An F-test is also provided in the results section to prove the statistical significance of our proposed method. [ABSTRACT FROM AUTHOR]

This study proposes a miniaturized dual-band antenna with a circular-polarization characteristic for a wireless-capsule endoscopic system operating in the industrial, scientific, and medical bands: 915 and 2450 MHz. The proposed capsule device is intended for deep-tissue implantation with the standard dimensions of 26 × 11 mm2. The key features of the proposed antenna are its CP characteristic at both bands, satisfactory gain values, and a smallest volume (6.5 × 6.5 × 0.05 = 2.11 mm3) with a via-less ground plane compared with the state-of-the-art device. The CP wave performance and the miniaturization of the antenna are achieved via the introduction of slots accompanied by meandered line segments in a radiating patch and open-end slots in a ground plane. Furthermore, the proposed antenna offers a reasonable axial ratio (AR) of < 3 dB at the desired frequency bands. The circularly polarized radiator attains the AR bandwidths (BWs) of 33.0% and 8.67% and the measured −10 dB impedance BWs of 13.63% and 6.28% at 915 and 2450 MHz, respectively. The measurements of the reflection coefficient and radiation pattern are performed using a fabricated prototype of the proposed antenna system in a saline solution and minced pork. Finally, the safety considerations and link budget calculations are evaluated to analyze the performance of the proposed capsule device. Due to the AR BW, impedance BW, and ease of fabrication, the proposed antenna system is a suitable candidate for use in endoscopic applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy (WCE) plays an important role in the diagnosis of gastrointestinal diseases. However, it is very time-consuming and fatiguing for a physician to review a large number of WCE images. Some methods to address this problem have recently been presented. However, these methods generally employ classification algorithms to discriminate abnormal from normal images, which do not localize, recognize, or detect abnormal patterns in abnormal images. We sought to identify a better method for the WCE abnormal pattern detection. In this paper, convolutional neural networks (CNNs) are used to implement detection function, and several methods are also adopted to boost the performance of WCE abnormality detection from aspects of the CNN architecture, region proposal, and transfer learning. First, we present a deep cascade network, namely, CascadeProposal, trained end-to-end to generate a small number of region proposals with high-recall by a region proposal rejection module and to simultaneously detect abnormal patterns using a detection module. Second, we use a multiregional combination (MRC) method to obtain good coverage of the regions of interest and employ the salient region segmentation (SRS) method to capture accurate region locations. Third, we use the dense region fusion (DRF) method for object boundary refinement. Fourth, we introduce negative category (Neg) and transfer learning (TL) strategies into our CNNs to obtain a better model performance. The extensive experiments are performed on our WCE image dataset of more than 7k annotated images. A final mean average precision (mAP) of 70.3% and a better mAP of 72.3% can be achieved via CascadeProposal with ZF and Fast R-CNN with VGG-16 networks, respectively, using MRC+Neg+TL method in the training stage and MRC+DRF+SRS method in the testing stage. The comprehensive results demonstrate that our method is efficient and effective for WCE abnormality detection with high-localization accuracy.

Until few years ago, radiological methods were widely used for the examination and investigation of the digestive tract. Today, wireless capsule endoscopy represents an innovative, noninvasive, effective solution that does not imply a risk of irradiation. Due to the impressive number of images captured on the entire “trip” covered by the video capsule, diagnostic accuracy is greatly improved, also allowing the visualization of certain areas of the digestive tract that were previously inaccessible. Captured images can be analyzed by a specialist who can identify lesions or possible active bleeding within the digestive tract. This paper presents the implementation of a recovery system for endoscopic images based on Content-Based Image Retrieval (CBIR) technique.

Wireless capsule endoscopy (WCE) is a novel imaging tool that allows noninvasive visualization of the entire gastrointestinal (GI) tract without causing discomfort to patients. Convolutional neural networks (CNNs), though perform favorably against traditional machine learning methods, show limited capacity in WCE image classification due to the small lesions and background interference. To overcome these limits, we propose a two-branch Attention Guided Deformation Network (AGDN) for WCE image classification. Specifically, the attention maps of branch1 are utilized to guide the amplification of lesion regions on the input images of branch2, thus leading to better representation and inspection of the small lesions. What’s more, we devise and insert Third-order Long-range Feature Aggregation (TLFA) modules into the network. By capturing long-range dependencies and aggregating contextual features, TLFAs endow the network with a global contextual view and stronger feature representation and discrimination capability. Furthermore, we propose a novel Deformation based Attention Consistency (DAC) loss to refine the attention maps and achieve the mutual promotion of the two branches. Finally, the global feature embeddings from the two branches are fused to make image label predictions. Extensive experiments show that the proposed AGDN outperforms state-of-the-art methods with an overall classification accuracy of 91.29% on two public WCE datasets. The source code is available at https://github.com/hathawayxxh/WCE-AGDN. [ABSTRACT FROM AUTHOR]

The digital images made with the wireless capsule endoscopy (WCE) from the patient's gastrointestinal tract are used to forecast abnormalities. The big amount of information from WCE pictures could take 2 hours to review GI tract illnesses per patient to research the digestive system and evaluate them. It is highly time consuming and increases healthcare costs considerably. In order to overcome this problem, the center symmetric local binary pattern (CS-LBP) and the auto color correlogram (ACC) were proposed to use a novel method based on a visual bag of features (VBOF). In order to solve this issue, we suggested a visual bag of features (VBOF) method by incorporating scale invariant feature transform (SIFT), CS-LBP and ACC. This combination of features is able to detect the interest point, texture and color information in an image. Features for each image are calculated to create a descriptor with a large dimension. The proposed feature descriptors are clustered by K- means referred to as visual words, and the support vector machine (SVM) method is used to automatically classify multiple disease abnormalities from the GI tract. Finally, post-processing scheme is applied to deal with final classification results i.e. validated the performance of multi-abnormal disease frame detection. [ABSTRACT FROM AUTHOR]

With the full application of Wireless Capsule Endoscopy (WCE) in the detection of gastrointestinal diseases, screening out a small number of lesion images from the massive imaging data brings a heavy burden to doctors. To solve the problems existing in the automatic detection of WCE images, such as inconspicuous colour and texture features, ease of being confused with healthy organs, fuzzy detail features and different sizes of lesions, and high impurities, we propose a residual-based multi-scale fully convolutional neural network to classify and detect lesions in WCE image. By introducing the concepts of skip connection in residual learning network and multi-scale convolution kernel in the inception network, the model can effectively extract the detailed features of various lesions in the image. The experimental results show that the sensitivity of the model reaches 98. 05 %, the specificity reaches 97. 67%，and the accuracy reaches 97. 84%. It is better than the classical deep residual network ResNetSO and the standard width multi-scale Inception-v4 algorithm. The model has high recognition rate, fast convergence speed, and improved computing performance. In short, the algorithm model takes into account the efficiency and performance of bleeding point detection, and has strong practicability. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this article, the first-ever dual-circular-polarized wideband conformal endoscopic antenna is designed at the medical implant communication service band (402 MHz) and industrial, scientific, and medical bands (915 MHz and 2.4 GHz). The proposed antenna with a small footprint of 8 mm3 (32 mm $\times 10$ mm $\times0.025$ mm) is printed on flexible polyamide and wrapped onto the inner wall of the capsule to spare the space for the capsule integrated components. For evaluation of the antenna’s performance within a full-package endoscopic device, a homogeneous muscle box and realistic Duke human phantoms were used. For measurement, the fabricated antenna is enclosed inside a 3-D-printed capsule along with the endoscopic system, and the enclosed system is waterproofed. The S-parameters and radiation patterns are measured in a saline-filled American Society for Testing and Materials (ASTM) model and a minced-pork phantom. The measured impedance bandwidths and gains at 402 MHz, 915 MHz, and 2.4 GHz are 236.2%, 104%, and 55.4% and −29.71, −28.7, and −20.8 dBi, respectively. The measured axial-ratio bandwidths are 45.9% and 21.3% at 915 MHz and 2.4 GHz, respectively. Furthermore, the link budget is numerically analyzed and measured via real-time telemetry using software-defined radios. The proposed antenna complies well with the human safety standards and satisfies the range and high data-rate requirements. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is a computer system that is able to perform tasks which normally require human intelligence. The role of AI in the field of gastroenterology has been gradually evolving since its inception in the 1950s. Discovery of wireless capsule endoscopy (WCE) and balloon enteroscopy (BE) has revolutionized small gut imaging. While WCE is a relatively patient-friendly and noninvasive mode to examine the nonobstructed small gut, it is limited by a lengthy examination time and the need for expertise in reading images acquired by the capsule. Similarly, BE, despite having the advantage of therapeutic intervention, is costly, invasive, and requires general sedation. Incorporation of concepts like machine learning and deep learning has been used to handle large amounts of data and images in gastroenterology. Interestingly, in small gut imaging, the application of AI has been limited to WCE only. This review was planned to examine and summarize available published data on various AI-based approaches applied to small bowel disease. We conducted an extensive literature search using Google search engine, Google Scholar, and PubMed database for published literature in English on the application of different AI techniques in small bowel endoscopy, and have summarized the outcome and benefits of these applications of AI in small bowel endoscopy. Incorporation of AI in WCE has resulted in significant advancements in the detection of various lesions starting from dysplastic mucosa, inflammatory and nonmalignant lesions to the detection of bleeding with increasing accuracy and has shortened the lengthy review time in image analysis. As most of the studies to evaluate AI are retrospective, the presence of inherent selection bias cannot be excluded. Besides, the interpretability (black-box nature) of AI models remains a cause for concern. Finally, issues related to medical ethics and AI need to be judiciously addressed to enable its seamless use in future. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We propose a new paradigm of a smart wireless endoscopic capsule (WCE) that has the ability to select suspicious images containing a polyp before sending them outside the body. To do so, we have designed an image processing system to select images with Regions Of Interest (ROI) containing a polyp. The criterion used to select an ROI is based on the polyp's shape. We use the Hough Transform (HT), a widely used shape-based algorithm for object detection and localization, to make this selection. In this paper, we present a new algorithm to compute in real-time the Hough Transform of high definition images (1920 x 1080 pixels). This algorithm has been designed to be integrated inside a WCE where there are specific constraints: a limited area and a limited amount of energy. To validate our algorithm, we have realized tests using a dataset containing synthetic images, real images, and endoscopic images with polyps. Results have shown that our algorithm is capable to detect circular shapes in synthetic and real images, but also can detect circles with an irregular contour, like that of polyps. We have implemented our architecture and validated it in a Xilinx Spartan 7 FPGA device, with an area of $8*8\;\text{mm}^2$ , which is compatible with integration inside a WCE. This architecture runs at 132 MHz with an estimated power consumption of 76 mW and can work close to 10 hours. To improve the capacity of our architecture, we have also made an ASIC estimation, that let our architecture work at 125 MHz, with a power consumption of only 17.2 mW and a duration of approximately 50 hours. [ABSTRACT FROM AUTHOR]

Continuous navigation information for medical insertion methods including wireless capsule endoscopy is an important feature to guide a medical instrument/object to the targeted location within a hollow organ or internal cavity of the patient in the best possible way. Also, an autonomous medical insertion or swallowable device with a self-contained navigation system would reduce the role of the high-level operator and hence reduce the human-factor mistake and risk of injury. The navigation techniques suggested for Wireless Capsule Endoscopy are image-based that are required to transfer and process a significant amount of data in real-time operation. This would increase the complexity of the system and would be more difficult in dark or liquid environments. A novel navigation system for Wireless Capsule Endoscopy/ordinary endoscopy that does not depend on any external source for operation and can handle the uncertainties of the path even in a dark or liquid environment (i.e.mucosa) of the human body is presented in this paper. The key element of the system is based on a capacitive-based pressure sensor array mounted on the capsule with the shape of semi-hemisphere. The place of each node is arranged to represent the variation in the latitude and the longitude of the travelling path. The system can acquire the required information to navigate the capsule in three-dimensional space and enhance the safety of the locomotion by monitoring the pressure between the capsule and the ambient environment. The system determines the variation of the path 180° in longitude and latitude with less than (<0.004%) percentage of error. Working algorithms for flexible and rigid environments are described in this paper. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The recently established wireless capsule endoscopy promises itself to be a great advancement in the field of medical diagnosis. The amount of data generated due to a single inspection of gastrointestinal tract is huge, and the transmission demands more cost and power. Due to the inherent nature of the capsule, a low complexity and low-power design are highly recommended with the ability to reduce the data. This paper explains the design of a compression module which satisfies the above requirements. The compression algorithm is developed around some features of the endoscopic images with its suitability for hardware implementation. The preprocessing operations followed by two-stage encoder using differential coder and Golomb–Rice coder are implemented on the experimental dataset. The core module in the preprocessing stage is the chroma subsampling which can considerably reduce the data to be transmitted. Several patterns for subsampling have been analyzed in this paper to evaluate the performance of the proposed system. With the help of a suitably designed up-sampler at the receiver, a near-lossless compression with good reconstructed image quality can be achieved. The 22:1:2 pattern yields better results and could bring about an average peak signal-to-noise ratio of 37 dB with compression rate of 70%. The results show that the proposed chroma subsampling patterns performs better than the competing methods towards wireless capsule endoscopy compression. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Keywords: Intestinal resection; Video capsule endoscopy; Wireless capsule endoscopy; Small intestine; Intestinal obstruction EN Intestinal resection Video capsule endoscopy Wireless capsule endoscopy Small intestine Intestinal obstruction 206 208 3 01/17/22 20220101 NES 220101 Nikita Narayani is currently a pre-medical student in her sophomore year at Vanderbilt University. Intestinal resection, Wireless capsule endoscopy, Small intestine, Intestinal obstruction, Video capsule endoscopy. [Extracted from the article]

Conventional radiological and endoscopic techniques utilizing long tubes were ineffective in visualizing the small bowel mucosa until the development of wireless capsule endoscopy (WCE). WCE is a revolutionary endoscopic technology that can diagnose the complete gastrointestinal tract. However, the existing capsule technologies are passive, and thus they cannot be navigated to or held in a specific location. The design of an active capsule will present the opportunity to move and stop a device at any targeted locations leading to numerous medical applications such as drug delivery or collecting tissue samples for examinations in the laboratory. This paper implements a new locomotion methodology for WCE systems using an electromagnetic platform. The platform produces a dynamic electromagnetic field to control the motion of the capsule. The strength and the direction of the electromagnetic field that is generated by the platform are continuously adjusted in order to maintain the equilibrium state during the capsule movement. We present the detailed design of the proposed platform with an experimental setup with polyvinyl chloride tubes and ex vivo to demonstrate the performance of the capsule motion.

Wireless capsule endoscopy (WCE) is the most advanced technology to visualize whole gastrointestinal (GI) tract in a non-invasive way. But the major disadvantage here, it takes long reviewing time, which is very laborious as continuous manual intervention is necessary. In order to reduce the burden of the clinician, in this paper, an automatic bleeding detection method for WCE video is proposed based on the color histogram of block statistics, namely CHOBS. A single pixel in WCE image may be distorted due to the capsule motion in the GI tract. Instead of considering individual pixel values, a block surrounding to that individual pixel is chosen for extracting local statistical features. By combining local block features of three different color planes of RGB color space, an index value is defined. A color histogram, which is extracted from those index values, provides distinguishable color texture feature. A feature reduction technique utilizing color histogram pattern and principal component analysis is proposed, which can drastically reduce the feature dimension. For bleeding zone detection, blocks are classified using extracted local features that do not incorporate any computational burden for feature extraction. From extensive experimentation on several WCE videos and 2300 images, which are collected from a publicly available database, a very satisfactory bleeding frame and zone detection performance is achieved in comparison to that obtained by some of the existing methods. In the case of bleeding frame detection, the accuracy, sensitivity, and specificity obtained from proposed method are 97.85%, 99.47%, and 99.15%, respectively, and in the case of bleeding zone detection, 95.75% of precision is achieved. The proposed method offers not only low feature dimension but also highly satisfactory bleeding detection performance, which even can effectively detect bleeding frame and zone in a continuous WCE video data.

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.

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.