Search Results (25)

The endoscopic capsule is a miniaturized device used for medical diagnosis, which is less invasive compared to traditional gastrointestinal endoscopy and can reduce patient discomfort. However, it faces challenges in communication transmission, such as high power consumption, serious signal interference, and low data transmission rate. To address these issues, this paper proposes an optimized modulation scheme that is low-cost, low-power, and robust in harsh environments, aiming to improve its transmission rate. The scheme is analyzed in terms of the in-body channel. The analysis and discussion for the scheme in wireless body area networks (WBANs) are divided into three aspects: bit error rate (BER) performance, energy efficiency (EE), and spectrum efficiency (SE), and complexity. These correspond to the following issues: transmission rate, communication quality, and low power consumption. The results demonstrate that the optimized scheme is more suitable for improving the communication performance of endoscopic capsules. Full article

The wireless capsule endoscope (WCE) has been utilized for human digestive tract examinations for over 20 years. Given the complex environment of the digestive tract and the challenge of detecting multi-category lesion images, enhancing model generalization ability is crucial. However, traditional data augmentation methods struggle to generate sufficiently diverse data. In this study, we propose a novel generative adversarial network, Special Common Attention Generative Adversarial Network (SCAGAN), to generate lesion images for capsule endoscopy. The SCAGAN model can adaptively integrate both the internal features and external global dependencies of the samples, enabling the generator to not only accurately capture the key structures and features of capsule endoscopic images, but also enhance the modeling of lesion complexity. Additionally, SCAGAN incorporates global context information to improve the overall consistency and detail of the generated images. To further enhance adaptability, self-modulation normalization is used, along with the Structural Similarity Index (SSIM) loss function to ensure structural authenticity. The Differentiable Data Augmentation (DiffAug) technique is employed to improve the model’s performance in small sample environments and balance the training process by adjusting learning rates to address issues of slow learning due to discriminator regularization. Experimental results show that SCAGAN significantly improves image quality and diversity, achieving state-of-the-art (SOTA) performance in the Frechet Inception Distance (FID) index. Moreover, when the generated lesion images were added to the dataset, the mean average precision (mAP) of the YOLOv9-based lesion detection model increased by 1.495%, demonstrating SCAGAN’s effectiveness in optimizing lesion detection. SCAGAN effectively addresses the challenges of lesion image generation for capsule endoscopy, improving both image quality and detection model performance. The proposed approach offers a promising solution for enhancing the training of lesion detection models in the context of capsule endoscopy. Full article

In biomedical research, telemetry is used to take automated physiological measurements wirelessly from animals, as it reduces their stress and allows recordings for large data collection over long periods. The ability to transmit high-throughput data from an in-body device (e.g., implantable systems, endoscopic capsules) to external devices can also be achieved by radiofrequency (RF), a standard wireless communication procedure. However, wireless in-body RF devices do not exceed a transmission speed of 2 Mbit/s, as signal absorption increases dramatically with tissue thickness and at higher frequencies. This paper presents the design of an optical wireless communication system (OWCS) for neural probes with an optical transmitter, sending out physiological data through an optical signal that is detected by an optical receiver. The optical receiver position is controlled by a tracking system of the small animal position, based on a cage with a piezoelectric floor. To validate the concept, an OWCS based on a wavelength of 850 nm for a data transfer of 5 Mbit/s, with an optical power of 55 mW, was demonstrated for a tissue thickness of approximately 10 mm, measured in an optical tissue phantom. Full article

A wireless capsule endoscope (WCE) is a medical device designed for the examination of the human gastrointestinal (GI) tract. Three-dimensional models based on WCE images can assist in diagnostics by effectively detecting pathology. These 3D models provide gastroenterologists with improved visualization, particularly in areas of specific interest. However, the constraints of WCE, such as lack of controllability, and requiring expensive equipment for operation, which is often unavailable, pose significant challenges when it comes to conducting comprehensive experiments aimed at evaluating the quality of 3D reconstruction from WCE images. In this paper, we employ a single-image-based 3D reconstruction method on an artificial colon captured with an endoscope that behaves like WCE. The shape from shading (SFS) algorithm can reconstruct the 3D shape using a single image. Therefore, it has been employed to reconstruct the 3D shapes of the colon images. The camera of the endoscope has also been subjected to comprehensive geometric and radiometric calibration. Experiments are conducted on well-defined primitive objects to assess the method’s robustness and accuracy. This evaluation involves comparing the reconstructed 3D shapes of primitives with ground truth data, quantified through measurements of root-mean-square error and maximum error. Afterward, the same methodology is applied to recover the geometry of the colon. The results demonstrate that our approach is capable of reconstructing the geometry of the colon captured with a camera with an unknown imaging pipeline and significant noise in the images. The same procedure is applied on WCE images for the purpose of 3D reconstruction. Preliminary results are subsequently generated to illustrate the applicability of our method for reconstructing 3D models from WCE images. Full article

In recent years, capsule endoscopes (CEs) have appeared as an advanced technology for the diagnosis of gastrointestinal diseases. However, only capturing the images limits the advanced diagnostic procedures and so on in CE’s applications. Herein, considering other extended functions like tissue sampling, a novel wireless biopsy CE has been presented employing active locomotion. Two permanent magnets (PMs) have been placed into the robots, which control the actuation of the capsule robot (CR) and biopsy mechanism by employing an external electromagnetic actuation (EMA) system. A spring has been attached to the biopsy mechanism to retract the biopsy tool after tissue collection. A camera module has also been attached to the front side of the CR to detect the target point and observe the biopsy process on the lesion. A prototype of CR was fabricated with a diameter of 12 mm and a length of 32 mm. A spring mechanism with a biopsy needle was placed inside the CR and sprang out around 5 mm. An in vitro experiment was conducted, which demonstrated the precise control translation (2 mm/s and 3 mm/s in the x and y directions, respectively) and desired extrusion of the biopsy mechanism ($~5 \mathrm{m}\mathrm{m})$ for sampling the tissue. A needle-based biopsy capsule robot (NBBCR) has been designed to perform the desired controlled locomotion and biopsy function by external force. This proposed active locomoted untethered NBBCR can be wirelessly controlled to perform extended function precisely, advancing the intestinal CE technique for clinical applications. Full article

Gastroparesis (GP) is a chronic disease characterized by upper gastrointestinal symptoms, primarily nausea and vomiting, and delayed gastric emptying (GE), in the absence of mechanical GI obstruction. The underlying pathophysiology of GP remains unclear, but factors contributing to the condition include vagal nerve dysfunction, impaired gastric fundic accommodation, antral hypomotility, gastric dysrhythmias, and pyloric dysfunction. Currently, gastric emptying scintigraphy (GES) is considered the gold standard for GP diagnosis. However, the overall delay in GE weakly correlates with GP symptoms and their severity. Recent research efforts have focused on developing treatments that address the presumed underlying pathophysiological mechanisms of GP, such as pyloric hypertonicity, with Gastric Peroral Endoscopic Myotomy (G-POEM) one of these procedures. New promising diagnostic tools for gastroparesis include wireless motility capsule (WMC), the 13 carbon-GE breath test, high-resolution electrogastrography, and the Endoluminal Functional Lumen Imaging Probe (EndoFLIP). Some of these tools assess alterations beyond GE, such as muscular electrical activity and pyloric tone. These modalities have the potential to characterize the pathophysiology of gastroparesis, identifying patients who may benefit from targeted therapies. The aim of this review is to provide an overview of the current knowledge on diagnostic pathways in GP, with a focus on the association between diagnosis, symptoms, and treatment. Full article

This study focuses on using wireless power transfer (WPT) technology based on magnetic resonant coupling (MRC) to supply electric power to an endoscopic capsule to be used for the direct feeding of specific functions or for battery charging. One of the main limitations of the diffusion of endoscopic capsules is the limited autonomy of the internal battery. The aim of the paper is to present an innovative system to wirelessly power capsules using inductive coupling. Here, a new transmitting coil architecture is proposed to allow the wireless charging of the capsule equipped with a monoaxial receiving coil for any possible geometric position and orientation. The new wearable transmitting coil consists of four rectangular coils with independent excitations, and it is capable of producing a magnetic field in any direction. The obtained results in terms of electrical performance of the proposed WPT system and in terms of in situ electromagnetic physical quantities are compared with the basic restrictions of electromagnetic field (EMF) safety guidelines. The results obtained are very promising, as the proposed WPT configuration can transfer at least 250 mW in a capsule that travels along the entire gastrointestinal tract. Full article

Through a wireless capsule endoscope (WCE) fitted with a miniature camera (about an inch), this study aims to examine the role of wireless capsule endoscopy (WCE) in the diagnosis, monitoring, and evaluation of GI (gastrointestinal) disorders. In a wearable belt recorder, a capsule travels through the digestive tract and takes pictures. It attempts to find tiny components that can be used to enhance the WCE. To accomplish this, we followed the steps below: Researching current capsule endoscopy through databases, designing and simulating the device using computers, implanting the system and finding tiny components compatible with capsule size, testing the system and eliminating noise and other problems, and analyzing the results. In the present study, it was shown that a spherical WCE shaper and a smaller WCE with a size of 13.5 diameter, a high resolution, and a high frame rate (8–32 fps) could help patients with pains due to the traditional capsules and provide more accurate pictures as well as prolong the battery life. In addition, the capsule can also be used to reconstruct 3D images. Simulation experiments showed that spherical endoscopic devices are more advantageous than commercial capsule-shaped endoscopic devices for wireless applications. We found that the sphere’s velocity through the fluid was greater than the capsule’s. Full article

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. Full article

Colorectal cancer is one of the most common malignancies and the leading cause of cancer death worldwide. Wireless capsule endoscopy is currently the most frequent method for detecting precancerous digestive diseases. Thus, precise and early polyps segmentation has significant clinical value in reducing the probability of cancer development. However, the manual examination is a time-consuming and tedious task for doctors. Therefore, scientists have proposed many computational techniques to automatically segment the anomalies from endoscopic images. In this paper, we present an end-to-end 2D attention residual U-Net architecture (AttResU-Net), which concurrently integrates the attention mechanism and residual units into U-Net for further polyp and bleeding segmentation performance enhancement. To reduce outside areas in an input image while emphasizing salient features, AttResU-Net inserts a sequence of attention units among related downsampling and upsampling steps. On the other hand, the residual block propagates information across layers, allowing for the construction of a deeper neural network capable of solving the vanishing gradient issue in each encoder. This improves the channel interdependencies while lowering the computational cost. Multiple publicly available datasets were employed in this work, to evaluate and verify the proposed method. Our highest-performing model was AttResU-Net, on the MICCAI 2017 WCE dataset, which achieved an accuracy of 99.16%, a Dice coefficient of 94.91%, and a Jaccard index of 90.32%. The experiment findings show that the proposed AttResU-Net overcomes its baselines and provides performance comparable to existing polyp segmentation approaches. Full article

Purpose: The detection of where an organ starts and where it ends is achievable and, since this information can be delivered in real time, it could be quite important for several reasons. For one, by having the practical knowledge of the Wireless Endoscopic Capsule (WEC) transition through an organ’s domain, we are able to align and control the endoscopic operation with any other possible protocol, i.e., delivering some form of treatment on the spot. Another is having greater anatomical topography information per session, therefore treating the individual in detail (not “in general”). Even the fact that by gathering more accurate information for a patient by merely implementing clever software procedures is a task worth exploiting, since the problems we have to overcome in real-time processing of the capsule findings (i.e., wireless transfer of images to another unit that will apply the necessary real time computations) are still challenging. This study proposes a computer-aided detection (CAD) tool, a CNN algorithm deployed to run on field programmable gate array (FPGA), able to automatically track the capsule transitions through the entrance (gate) of esophagus, stomach, small intestine and colon, in real time. The input data are the wireless transmitted image shots of the capsule’s camera (while the endoscopy capsule is operating). Methods: We developed and evaluated three distinct multiclass classification CNNs, trained on the same dataset of total 5520 images extracted by 99 capsule videos (total 1380 frames from each organ of interest). The proposed CNNs differ in size and number of convolution filters. The confusion matrix is obtained by training each classifier and evaluating the trained model on an independent test dataset comprising 496 images extracted by 39 capsule videos, 124 from each GI organ. The test dataset was further evaluated by one endoscopist, and his findings were compared with CNN-based results. The statistically significant of predictions between the four classes of each model and the comparison between the three distinct models is evaluated by calculating the p-values and chi-square test for multi class. The comparison between the three models is carried out by calculating the macro average F1 score and Mattheus correlation coefficient (MCC). The quality of the best CNN model is estimated by calculations of sensitivity and specificity. Results: Our experimental results of independent validation demonstrate that the best of our developed models addressed this topological problem by exhibiting an overall sensitivity (96.55%) and specificity of (94.73%) in the esophagus, (81.08% sensitivity and 96.55% specificity) in the stomach, (89.65% sensitivity and 97.89% specificity) in the small intestine and (100% sensitivity and 98.94% specificity) in the colon. The average macro accuracy is 95.56%, the average macro sensitivity is 91.82%. Full article

Wireless capsule endoscopes capture and transmit images of the human gastrointestinal tract for use in medical diagnosis. The localization of the capsule makes it possible to precisely identify areas with lesions detected during the examination. The antenna is an important element of the endoscopic capsule that is used for the transmission of the signal containing the recorded image of the inside of the digestive system. Antenna parameters influence also the performance of algorithms that are locating capsule endoscopes based on the analysis of the received signal. The zig-zag conformal antenna for the endoscope capsule is presented in this paper. It was examined both in simulation and tissue simulant liquid. It is then applied to an improved localization system that is based on phase difference analysis of received signals. In this new approach, the algorithm selects five external receivers from the predefined set and uses an adaptive estimation of human body model permittivity. The localization algorithm was verified with computer simulations. Remcom XFdtd software and both simplified and heterogeneous human body models were applied in simulations. The technique which uses automatic selection of the external receiver together with proposed antenna enhanced localization accuracy by about 15% compared with the previous version of this algorithm. Full article

This work presents an inductive wireless power transfer system for powering an endoscopy capsule supplying energy to power electronic devices allocated inside a capsule of ≈26.1 mm × 9 mm. A receiver with three coils in quadrature with dimensions of ≈9 mm × 9 mm × 10 mm is located inside the capsule, moving freely inside a transmitter coil with 380 mm diameter through translations and revolutions. The proposed system tracks the variations of the equivalent magnetic coupling coefficient compensating misalignments between the transmitter and receiver coils. The power on the load is estimated and optimized from the transmitter, and the tracking control is performed by actuating on a capacitance in the matching network and on the voltage source frequency. The proposed system can prevent load overheating by limiting the power via adjusting of the magnitude of voltage source $V_S$. Experimental results with uncertainties analysis reveal that, even at low magnetic coupling coefficients k ranging from (1.7 × 10${}^{-3}$, 3.5 × 10${}^{-3}$), the power on the load can be held within the range of 100–130 mW. These results are achieved with any position of the capsule in the space, limited by the diameter of the transmitter coil and height of 200 mm when adjusting the series capacitance of the transmitter in the range (17.4, 19.4) pF and the frequency of the power source in the range (802.1, 809.5) kHz. Full article

Background: Wireless capsule endoscopy allows the identification of small intestinal protruded lesions, such as polyps, tumors, or venous structures. However, reading wireless capsule endoscopy images or movies is time-consuming, and minute lesions are easy to miss. Computer-aided diagnosis (CAD) has been applied to improve the efficacy of the reading process of wireless capsule endoscopy images or movies. However, there are no studies that systematically determine the performance of CAD models in diagnosing gastrointestinal protruded lesions. Objective: The aim of this study was to evaluate the diagnostic performance of CAD models for gastrointestinal protruded lesions using wireless capsule endoscopic images. Methods: Core databases were searched for studies based on CAD models for the diagnosis of gastrointestinal protruded lesions using wireless capsule endoscopy, and data on diagnostic performance were presented. A systematic review and diagnostic test accuracy meta-analysis were performed. Results: Twelve studies were included. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of protruded lesions were 0.95 (95% confidence interval, 0.93–0.97), 0.89 (0.84–0.92), 0.91 (0.86–0.94), and 74 (43–126), respectively. Subgroup analyses showed robust results. Meta-regression found no source of heterogeneity. Publication bias was not detected. Conclusion: CAD models showed high performance for the optical diagnosis of gastrointestinal protruded lesions based on wireless capsule endoscopy. Full article

Wireless capsule endoscopes take and send photos of the human digestive tract, which are used for medical diagnosis. The capsule’s location enables exact identification of the regions with lesions. This can be carried out by analyzing the parameters of the electromagnetic wave received from the capsule. Because the human body is a complex heterogeneous environment that impacts the propagation of wireless signals, determining the distance between the transmitter and the receiver based on the received power level is challenging. An enhanced approach of identifying the location of endoscope capsules using a wireless signal phase detection algorithm is presented in this paper. For each capsule position, this technique uses adaptive estimation of human body model permittivity. This approach was tested using computer simulations in Remcom XFdtd software using a numerical, heterogeneous human body model, as well as measurements with physical phantom. The type of transmitting antenna employed in the capsule also has a significant impact on the suggested localization method’s accuracy. As a result, the helical antenna, which is smaller than the dipole, was chosen as the signal’s source. For both the numerical and physical phantom studies, the proposed technique with adaptive body model enhances localization accuracy by roughly 30%. Full article