Wireless capsule endoscopy

Computer-Aided Diagnosis of Gastrointestinal Protruded Lesions Using Wireless Capsule Endoscopy: A Systematic Review and Diagnostic Test Accuracy Meta-Analysis

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.

Diffusion of an innovation: growth in video capsule endoscopy in the U.S. Medicare population from 2003 to 2019

Background

Video capsule endoscopy (VCE), approved by the U.S. Food and Drug Administration (FDA) in 2001, represented a disruptive technology that transformed evaluation of the small intestine. Adoption of this technology over time and current use within the U.S. clinical population has not been well described.

Methods

To assess the growth of capsule endoscopy within the U.S. Medicare provider population (absolute growth and on a population-adjusted basis), characterize the providers performing VCE, and describe potential regional differences in use. Medicare summary data from 2003 to 2019 were used to retrospectively analyze capsule endoscopy use in a multiple cross-sectional design. In addition, detailed provider summary files were used from 2012 to 2018 to characterize provider demographics.

Results

VCE use grew rapidly from 2003 to 2008 followed by a plateau from 2008 to 2019. There was significant variation in use of VCE between states, with up to 10-fold variation between states (14.6 to 156.1 per 100,000 enrollees in 2018). During this time, the adjusted VCE use on a population-adjusted basis declined, reflecting saturation of growth.

Conclusions

Growth of VCE use over time follows an S-shaped diffusion of innovation curve demonstrating a successful diffusion of innovation within gastroenterology. The lack of additional growth since 2008 suggests that current levels of use are well matched to overall population need within the constraints of reimbursement. Future studies should examine whether this lack of growth has implications for access and healthcare inequities.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-07780-2.

Automated detection of ulcers and erosions in capsule endoscopy images using a convolutional neural network

Capsule endoscopy (CE) is an important tool in the management of patients with known or suspected inflammatory bowel disease. Ulcers and erosions of the enteric mucosa are prevalent findings in these patients. They frequently occur together, and their identification in CE is crucial for an accurate evaluation of disease severity. Nevertheless, reviewing CE images is a time-consuming task, and the risk of overlooking lesions is significant.Over the last decade, artificial intelligence (AI) has emerged as a means for overcoming these pitfalls. Of all AI methods, convolutional neural networks (CNN), due to their complex multilayer architecture present the best results in medical image analysis, particularly capsule endoscopy. Therefore, we aimed to develop a CNN for the automatic identification of ulcers and erosions in the small bowel mucosa. A total of 1483 CE exams (PillCam SB3®) performed at a single center between 2015 and 2020 were analysed. From these exams, a total of 6130 frames of the enteric mucosa were obtained, 4233 containing enteric ulcers and erosions, and the remaining containing normal mucosa or other findings. Ulcers and erosions were stratified according to Saurin's classification for bleeding potential: P1E-erosions with intermediate bleeding risk; P1U-ulcers with intermediate bleeding risk; P2U-ulcers with high bleeding risk. For automatic identification of these lesions, these images were inserted into a CNN model with transfer learning. The pool of images was divided for constitution of training and validation datasets, comprising 80% and 20% of the total number of images, respectively. The output provided by the CNN was compared to the classification provided by a consensus of specialists. After optimizing the neural architecture of the algorithm, our model was able to automatically detect and distinguish ulcers and erosions (any bleeding potential) in the small intestine mucosa with an accuracy of 95.6%, sensitivity of 90.8%, and a specificity of 97.1%. We believe that our study lays the foundation for the development and application of effective AI tools to CE. These techniques should improve diagnostic accuracy and reading efficiency. Schematic representation of the workflow and summary of the results.

Computer-Aided Diagnosis of Gastrointestinal Ulcer and Hemorrhage Using Wireless Capsule Endoscopy: Systematic Review and Diagnostic Test Accuracy Meta-analysis

BACKGROUND

Interpretation of capsule endoscopy images or movies is operator-dependent and time-consuming. As a result, computer-aided diagnosis (CAD) has been applied to enhance the efficacy and accuracy of the review process. Two previous meta-analyses reported the diagnostic performance of CAD models for gastrointestinal ulcers or hemorrhage in capsule endoscopy. However, insufficient systematic reviews have been conducted, which cannot determine the real diagnostic validity of CAD models.

OBJECTIVE

To evaluate the diagnostic test accuracy of CAD models for gastrointestinal ulcers or hemorrhage using wireless capsule endoscopic images.

METHODS

We conducted core databases searching for studies based on CAD models for the diagnosis of ulcers or hemorrhage using capsule endoscopy and presenting data on diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed.

RESULTS

Overall, 39 studies were included. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of ulcers (or erosions) were .97 (95% confidence interval, .95-.98), .93 (.89-.95), .92 (.89-.94), and 138 (79-243), respectively. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of hemorrhage (or angioectasia) were .99 (.98-.99), .96 (.94-0.97), .97 (.95-.99), and 888 (343-2303), respectively. Subgroup analyses showed robust results. Meta-regression showed that published year, number of training images, and target disease (ulcers vs erosions, hemorrhage vs angioectasia) was found to be the source of heterogeneity. No publication bias was detected.

CONCLUSIONS

CAD models showed high performance for the optical diagnosis of gastrointestinal ulcer and hemorrhage in wireless capsule endoscopy.

PEACE: Perception and Expectations toward Artificial Intelligence in Capsule Endoscopy

Artificial intelligence (AI) has shown promising results in digestive endoscopy, especially in capsule endoscopy (CE). However, some physicians still have some difficulties and fear the advent of this technology. We aimed to evaluate the perceptions and current sentiments toward the use of AI in CE. An online survey questionnaire was sent to an audience of gastroenterologists. In addition, several European national leaders of the International CApsule endoscopy REsearch (I CARE) Group were asked to disseminate an online survey among their national communities of CE readers (CER). The survey included 32 questions regarding general information, perceptions of AI, and its use in daily life, medicine, endoscopy, and CE. Among 380 European gastroenterologists who answered this survey, 333 (88%) were CERs. The mean average time length of experience in CE reading was 9.9 years (0.5-22). A majority of CERs agreed that AI would positively impact CE, shorten CE reading time, and help standardize reporting in CE and characterize lesions seen in CE. Nevertheless, in the foreseeable future, a majority of CERs disagreed with the complete replacement all CE reading by AI. Most CERs believed in the high potential of AI for becoming a valuable tool for automated diagnosis and for shortening the reading time. Currently, the perception is that AI will not replace CE reading.

Artificial intelligence and capsule endoscopy: automatic detection of vascular lesions using a convolutional neural network

Background

Capsule endoscopy (CE) is the first line for evaluation of patients with obscure gastrointestinal bleeding. A wide range of small intestinal vascular lesions with different hemorrhagic potential are frequently found in these patients. Nevertheless, reading CE exams is time-consuming and prone to errors. Convolutional neural networks (CNN) are artificial intelligence tools with high performance levels in image analysis. This study aimed to develop a CNN-based model for identification and differentiation of vascular lesions with distinct hemorrhagic potential in CE images.

Methods

The development of the CNN was based on a database of CE images. This database included images of normal small intestinal mucosa, red spots, and angiectasia/varices. The hemorrhagic risk was assessed by Saurin's classification. For CNN development, 11,588 images (9525 normal mucosa, 1026 red spots, and 1037 angiectasia/varices) were ultimately extracted. Two image datasets were created for CNN training and testing.

Results

The network was 91.8% sensitive and 95.9% specific for detection of vascular lesions, providing accurate predictions in 94.4% of cases. In particular, the CNN had a sensitivity and specificity of 97.1% and 95.3%, respectively, for detection of red spots. Detection of angiectasia/varices occurred with a sensitivity of 94.1% and a specificity of 95.1%. The CNN had a frame reading rate of 145 frames/sec.

Conclusions

The developed algorithm is the first CNN-based model to accurately detect and distinguish enteric vascular lesions with different hemorrhagic risk. CNN-assisted CE reading may improve the diagnosis of these lesions and overall CE efficiency.

Automated Bowel Polyp Detection Based on Actively Controlled Capsule Endoscopy: Feasibility Study

This paper presents an active locomotion capsule endoscope system with 5D position sensing and real-time automated polyp detection for small-bowel and colon applications. An electromagnetic actuation system (EMA) consisting of stationary electromagnets is utilized to remotely control a magnetic capsule endoscope with multi-degree-of-freedom locomotion. For position sensing, an electronic system using a magnetic sensor array is built to track the position and orientation of the magnetic capsule during movement. The system is integrated with a deep learning model, named YOLOv3, which can automatically identify colorectal polyps in real-time with an average precision of 85%. The feasibility of the proposed method concerning active locomotion and localization is validated and demonstrated through in vitro experiments in a phantom duodenum. This study provides a high-potential solution for automatic diagnostics of the bowel and colon using an active locomotion capsule endoscope, which can be applied for a clinical site in the future.

Gastric examination using a novel three-dimensional magnetically assisted capsule endoscope and a hand-held magnetic controller: A porcine model study

Magnetically assisted capsule endoscopy (MACE) is a noninvasive procedure and can overcome passive capsule movement that limits gastric examination. MACE has been studied in many trials as an alternative to upper endoscopy. However, to increase diagnostic accuracy of various gastric lesions, MACE should be able to provide stereoscopic, clear images and to measure the size of a lesion. So, we conducted the animal experiment using a novel three-dimensional (3D) MACE and a new hand-held magnetic controller for gastric examination. The purpose of this study is to assess the performance and safety of 3D MACE and hand-held magnetic controller through the animal experiment. Subsequently, via the dedicated viewer, we evaluate whether 3D reconstruction images and clear images can be obtained and accurate lesion size can be measured. During real-time gastric examination, the maneuverability and visualization of 3D MACE were adequate. A polypoid mass lesion was incidentally observed at the lesser curvature side of the prepyloric antrum. The mass lesion was estimated to be 10.9 x 11.5 mm in the dedicated viewer, nearly the same size and shape as confirmed by upper endoscopy and postmortem examination. Also, 3D and clear images of the lesion were successfully reconstructed. This animal experiment demonstrates the accuracy and safety of 3D MACE. Further clinical studies are warranted to confirm the feasibility of 3D MACE for human gastric examination.

Advances in optical gastrointestinal endoscopy: a technical review

Optical endoscopy is the primary diagnostic and therapeutic tool for management of gastrointestinal (GI) malignancies. Most GI neoplasms arise from precancerous lesions; thus, technical innovations to improve detection and diagnosis of precancerous lesions and early cancers play a pivotal role in improving outcomes. Over the last few decades, the field of GI endoscopy has witnessed enormous and focused efforts to develop and translate accurate, user-friendly, and minimally invasive optical imaging modalities. From a technical point of view, a wide range of novel optical techniques is now available to probe different aspects of light-tissue interaction at macroscopic and microscopic scales, complementing white light endoscopy. Most of these new modalities have been successfully validated and translated to routine clinical practice. Herein, we provide a technical review of the current status of existing and promising new optical endoscopic imaging technologies for GI cancer screening and surveillance. We summarize the underlying principles of light-tissue interaction, the imaging performance at different scales, and highlight what is known about clinical applicability and effectiveness. Furthermore, we discuss recent discovery and translation of novel molecular probes that have shown promise to augment endoscopists' ability to diagnose GI lesions with high specificity. We also review and discuss the role and potential clinical integration of artificial intelligence-based algorithms to provide decision support in real time. Finally, we provide perspectives on future technology development and its potential to transform endoscopic GI cancer detection and diagnosis.

Deep learning and capsule endoscopy: automatic identification and differentiation of small bowel lesions with distinct haemorrhagic potential using a convolutional neural network

OBJECTIVE

Capsule endoscopy (CE) is pivotal for evaluation of small bowel disease. Obscure gastrointestinal bleeding most often originates from the small bowel. CE frequently identifies a wide range of lesions with different bleeding potentials in these patients. However, reading CE examinations is a time-consuming task. Convolutional neural networks (CNNs) are highly efficient artificial intelligence tools for image analysis. This study aims to develop a CNN-based model for identification and differentiation of multiple small bowel lesions with distinct haemorrhagic potential using CE images.

DESIGN

We developed, trained, and validated a denary CNN based on CE images. Each frame was labelled according to the type of lesion (lymphangiectasia, xanthomas, ulcers, erosions, vascular lesions, protruding lesions, and blood). The haemorrhagic potential was assessed by Saurin's classification. The entire dataset was divided into training and validation sets. The performance of the CNN was measured by the area under the receiving operating characteristic curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

RESULTS

A total of 53 555 CE images were included. The model had an overall accuracy of 99%, a sensitivity of 88%, a specificity of 99%, a PPV of 87%, and an NPV of 99% for detection of multiple small bowel abnormalities and respective classification of bleeding potential.

CONCLUSION

We developed and tested a CNN-based model for automatic detection of multiple types of small bowel lesions and classification of the respective bleeding potential. This system may improve the diagnostic yield of CE for these lesions and overall CE efficiency.

Lavage, Simethicone, and Prokinetics-What to Swallow with a Video Capsule

The development of new capsules now allows endoscopic diagnosis in all segments of the gastrointestinal tract and comes with new needs for differentiated preparation regimens. Although the literature is steadily increasing, the results of the conducted trials on preparation are sometimes conflicting. The ingestion of simethicone before gastric and small bowel capsule endoscopy for prevention of air bubbles is established. The value of a lavage before small bowel capsule endoscopy (SBCE) is recommended, although not supported by all studies. Ingestion in the morning before the procedure seems useful for the improvement of mucosa visualization. Lavage after swallowing of the capsule seems to improve image quality, and in some studies also diagnostic yield. Prokinetics has been used with first generation capsules to shorten gastric transit time and increase the rate of complete small bowel visualization. With the massively prolonged battery capacity of the new generation small bowel capsules, prokinetics are only necessary in significantly delayed gastric emptying as documented by a real-time viewer. Lavage is crucial for an effective colon capsule or pan-intestinal capsule endoscopy. Mainly high or low volume polyethylene glycol (PEG) is used. Apart from achieving optimal cleanliness, propulsion of the capsule by ingested boosts is required to obtain a complete passage through the colon within the battery lifetime. Boosts with low volume sodium picosulfate (NaP) or diatrizoate (gastrografin) seem most effective, but potentially have more side effects than PEG. Future research is needed for more patient friendly but effective preparations, especially for colon capsule and pan-intestinal capsule endoscopy.

A 3D-Printed Self-Learning Three-Linked-Sphere Robot for Autonomous Confined-Space Navigation

Reinforcement learning control methods can impart robots with the ability to discover effective behavior, reducing their modeling and sensing requirements, and enabling their ability to adapt to environmental changes. However, it remains challenging for a robot to achieve navigation in confined and dynamic environments, which are characteristic of a broad range of biomedical applications, such as endoscopy with ingestible electronics. Herein, a compact, 3D-printed three-linked-sphere robot synergistically integrated with a reinforcement learning algorithm that can perform adaptable, autonomous crawling in a confined channel is demonstrated. The scalable robot consists of three equally sized spheres that are linearly coupled, in which the extension and contraction in specific sequences dictate its navigation. The ability to achieve bidirectional locomotion across frictional surfaces in open and confined spaces without prior knowledge of the environment is also demonstrated. The synergistic integration of a highly scalable robotic apparatus and the model-free reinforcement learning control strategy can enable autonomous navigation in a broad range of dynamic and confined environments. This capability can enable sensing, imaging, and surgical processes in previously inaccessible confined environments in the human body.

Wireless 3D Surgical Navigation and Tracking System With 100μm Accuracy Using Magnetic-Field Gradient-Based Localization

This paper describes a high-resolution 3D navigation and tracking system using magnetic field gradients, that can replace X-Ray fluoroscopy in high-precision surgeries. Monotonically varying magnetic fields in X, Y and Z directions are created in the field-of-view (FOV) to produce magnetic field gradients, which encode each spatial point uniquely. Highly miniaturized, wireless and battery-less devices, capable of measuring their local magnetic field, are designed to sense the gradient field. One such device can be attached to an implant inside the body and another to a surgical tool, such that both can simultaneously measure and communicate the magnetic field at their respective locations to an external receiver. The relative location of the two devices on a real-time display can enable precise surgical navigation without using X-Rays. A prototype device is designed consisting of a micro-chip fabricated in 65nm CMOS technology, a 3D magnetic sensor and an inductor-coil. Planar electromagnetic coils are designed for creating the 3D magnetic field gradients in a 20×20×10 cm³ of scalable FOV. Unambiguous and orientation-independent spatial encoding is achieved by: (i) using the gradient in the total field magnitude instead of only the Z-component; and (ii) using a combination of the gradient fields to correct for the non-linearity and non-monotonicity in X and Y gradients. The resultant X and Y FOV yield ≥90% utilization of their respective coil-span. The system is tested in vitro to demonstrate a localization accuracy of m in 3D, the highest reported to the best of our knowledge.

Side-view holographic endomicroscopy via a custom-terminated multimode fibre

Microendoscopes based on optical fibres have recently come to the fore as promising candidates allowing in-vivo observations of otherwise inaccessible biological structures in animal models. Despite being still in its infancy, imaging can now be performed at the tip of a single multimode fibre, by relying on powerful holographic methods for light control. Fibre based endoscopy is commonly performed en face, resulting in possible damage of the specimen owing to the direct contact between the distal end of the probe and target. On this ground, we designed an all-fibre probe with an engineered termination that reduces compression and damage to the tissue under investigation upon probe insertion. The geometry of the termination brings the field of view to a plane parallel to the fibre's longitudinal direction, conveying the probe with off-axis imaging capabilities. We show that its focusing ability also benefits from a higher numerical aperture, resulting in imaging with increased spatial resolution. The effect of probe insertion was investigated inside a tissue phantom comprising fluorescent particles suspended in agarose gel, and a comparison was established between the novel side-view probe and the standard en face fibre probe. This new concept paves the way to significantly less invasive deep-tissue imaging.

A Current and Newly Proposed Artificial Intelligence Algorithm for Reading Small Bowel Capsule Endoscopy

Small bowel capsule endoscopy (SBCE) is one of the most useful methods for diagnosing small bowel mucosal lesions. However, it takes a long time to interpret the capsule images. To solve this problem, artificial intelligence (AI) algorithms for SBCE readings are being actively studied. In this article, we analyzed several studies that applied AI algorithms to SBCE readings, such as automatic lesion detection, automatic classification of bowel cleanliness, and automatic compartmentalization of small bowels. In addition to automatic lesion detection using AI algorithms, a new direction of AI algorithms related to shorter reading times and improved lesion detection accuracy should be considered. Therefore, it is necessary to develop an integrated AI algorithm composed of algorithms with various functions in order to be used in clinical practice.

A Descriptive Analysis of Capsule Endoscopy Events in the FDA Manufacturer and User Facility Device Experience (MAUDE) Database

Introduction

The malfunction of capsule endoscopy (CE) devices is a significant reason for the failure of CE procedures, which could hinder and prevent diagnosis. Unfortunately, malfunction-related adverse events (AEs) caused by CE devices are rarely reported in publications. Although most malfunction-related AEs could not lead to physical harm, they could reduce the efficiency of medical care and increase medical costs. The manufacturer and user facility device experience (MAUDE) database, a publicly accessible resource for patient safety, contains not only the common complications of CE but also valuable malfunction-related AEs, which have been underutilized. Therefore, the study aims to discover and analyze the possible AEs associated with CE and demonstrate the utility of the MAUDE reports to promote patient safety.

Materials and Methods

We acquired MAUDE reports of CE systems from January 01, 2008, to July 31, 2020, through a systematic search strategy. We utilized the manufacturers, brand names, and product codes as search terms from which medical device reports including structured data and narrative texts were extracted, followed by a manual review of the narrative texts, reporter occupation, device involved, event type and the phase of the event; finally, patient outcomes were recorded and analyzed as per CE categories and characteristics.

Results

A total of 377 CEs medical device reports were retrieved, and 342 reports were included after reviewing. There were 327 mandatory reports (96%) and 15 voluntary reports (4%). These reports referred to capsule endoscope (n = 213), sensing system (n = 66), patency capsule (n = 38), and capsule delivery device (n = 26). A total of 349 CE-related AEs were identified, including complications (n = 228), malfunction-related AEs (n = 109), and other events (n = 12). The composition of AEs was not the same for the CE devices. Complications were major AEs of capsule endoscope and patency capsule, but malfunction-related AEs were the most common in AEs of sensing systems and capsule delivery devices.

Conclusion

MAUDE serves as an invaluable data source for investigating malfunction-related AEs. In addition to common complications, malfunction of CE devices could threaten patient safety in CE procedures. Improving awareness of the malfunction of CE devices and raising adequate training for staff working in gastrointestinal (GI) endoscopic units could be critical and beneficial in preventing malfunction-related AEs.

Feasibility, complications, and quality of visualization using video capsule endoscopy in 40 dogs with overt or questionable gastrointestinal bleeding

Background

Prospective studies describing video capsule endoscopy (VCE), its feasibility, and complications in dogs are limited.

Objective

To assess VCE, quality of visualization, complications, and risk factors for incomplete studies in dogs with overt or questionable gastrointestinal bleeding (GIB).

Animals

Forty dogs with overt or questionable GIB.

Methods

Prospective, multicenter, interventional study. From August 2017 to March 2020, dogs were examined by VCE (ALICAM) because of overt or questionable GIB. Reported outcomes included diagnostic results of VCE study, quality of visualization, and complications. Risk factors for incomplete studies were evaluated using logistic regression.

Results

In total, 40 dogs (13 overt, 27 questionable GIB) were included. The capsules were administered PO in 29 and endoscopically in 11 dogs (6 duodenum, 5 stomach). One capsule was not retrieved. In 24 of 39 recordings, bleeding lesions were identified (10 overt GIB, 14 questionable GIB). Overall, the quality of visualization was poor to limited in the stomach and colon, and adequate to good in the small intestine. The most common complication was an incomplete study in 15/39 studies, particularly after oral administration (13/28). Risk factors for incomplete study after oral administration included administration of simethicone or opioids, chronic enteropathy, and capsule gastric transit time >6 hours.

Conclusions and Clinical Importance

Video capsule endoscopy can be used to diagnose a variety of lesions causing bleeding in the gastrointestinal tract of dogs with questionable GIB. Incomplete studies are the most common complications in dogs after oral administration of capsules.

Next-generation ingestible devices: sensing, locomotion and navigation

There is significant interest in exploring the human body's internal activities and measuring important parameters to understand, treat and diagnose the digestive system environment and related diseases. Wireless capsule endoscopy (WCE) is widely used for gastrointestinal (GI) tract exploration due to its effectiveness as it provides no pain and is totally tolerated by the patient. Current ingestible sensing technology provides a valuable diagnostic tool to establish a platform for monitoring the physiological and biological activities inside the human body. It is also used for visualizing the GI tract to observe abnormalities by recording the internal cavity while moving. However, the capsule endoscopy is still passive, and there is no successful locomotion method to control its mobility through the whole GI tract. Drug delivery, localization of abnormalities, cost reduction and time consumption are improvements that can be gained from having active ingestible WCEs. In this article, the current technological developments of ingestible devices including sensing, locomotion and navigation are discussed and compared. The main features required to implement next-generation active WCEs are explored. The methods are evaluated in terms of the most important features such as safety, velocity, complexity of design, control, and power consumption.

Diagnostic accuracy of a novel third generation esophageal capsule as a non-invasive detection method for Barrett's esophagus: A pilot study

BACKGROUND AND AIM

Previous two generations of esophageal capsule did not show adequate detection rates for Barrett's esophagus (BE). We assessed the diagnostic accuracy of a novel third generation capsule with an improved frame rate of 35 frames per second for the detection of BE in a pilot study.

METHODS

This was a blinded prospective pilot study conducted at a tertiary medical center. Patients with known BE (at least C0M > 1) who presented for endoscopic surveillance (May to October 2017) were included. All patients underwent novel esophageal capsule (PillCam™ UGI; Medtronic) ingestion using the simplified ingestion protocol followed by standard high-definition upper endoscopy (esophagogastroduodenoscopy [EGD]). Capsule endoscopy findings were interpreted by examiners blinded to endoscopy results and compared with endoscopic findings (gold standard). Following completion of both tests, a subjective questionnaire was provided to all patients regarding their experience.

RESULTS

Twenty patients (95%males, mean age 66.3 [±7.9] years) with BE undergoing surveillance EGD were eligible. The mean BE length was 3.5 (±2.7) cm. Novel esophageal capsule detected BE in 75% patients when images were compared with endoscopy. Novel capsule detected BE in 82% patients when the BE length was ≥2 cm. The mean esophageal transit time was 0.59 s. On a subjective questionnaire, all 20 patients reported novel capsule as being more convenient compared with EGD.

CONCLUSIONS

In this pilot, single-center study, novel esophageal capsule was shown to be not ready for population screening of BE. Studies integrating artificial intelligence into improved quality novel esophageal capsule should be performed for BE screening.

Training, Reading, and Reporting for Small Bowel Video Capsule Endoscopy

Video capsule endoscopy (VCE) is an established modality for examining the small bowel. Formal training in interpretation and reporting of VCE examinations, along with assessment of performance metrics, is advocated for all gastroenterology fellowship programs. This review provides an overview of VCE minimum training requirements and competency assessment, cognitive and technical aspects of interpretation, and standardized reporting of findings. In order to optimize and advance the clinical utility of VCE, efforts must continue to promote and encourage consensus and standardization of training, definition and assessment of competence, enhancements of VCE reading tools, and use of appropriate nomenclature in VCE reports.

Preparation, Timing, Prokinetics, and Surface Agents in Video Capsule Endoscopy

There is a trend in data to support active preparation for video capsule endoscopy (VCE), but the timing of this remains unclear. Split dosing may be the most efficacious preparation. Study methodology continues to evolve, with increased use of standardized scales, with the addition of diagnostic yield as an outcome. The use of adjuncts has not been detrimental, but their value has not been proved to improve outcomes of VCE.

Artificial Intelligence Research and Development for Application in Video Capsule Endoscopy

Artificial intelligence (AI) research for medical applications has expanded quickly. Advancements in computer processing now allow for the development of complex neural network architectures (eg, convolutional neural networks) that are capable of extracting and learning complex features from massive data sets, including large image databases. Gastroenterology and endoscopy are well suited for AI research. Video capsule endoscopy is an ideal platform for AI model research given the large amount of data produced by each capsule examination and the annotated databases that are already available. Studies have demonstrated high performance for applications of capsule-based AI models developed for various pathologic conditions.

A Review on Lossless Compression Techniques for Wireless Capsule Endoscopic Data

BACKGROUND

The videos produced during wireless capsule endoscopy have larger data size causing difficulty in transmission with limited bandwidth. The constraint on wireless capsule endoscopy hinders the performance of the compression module.

OBJECTIVES

The objectives of this paper are as follows: (i) to conduct an extensive review of the lossless compression techniques and (ii) to find out the limitations of the existing system and the possibilities for improvement.

METHODS

The literature review was conducted with a focus on the compression schemes satisfying minimum computational complexity, less power dissipation and low memory requirements for hardware implementation. A thorough study of various lossless compression techniques was conducted under two perspectives, i.e., techniques applied to Bayer CFA and RGB images. The detail of the various stages of wireless capsule endoscopy compression was investigated to have a better understanding. The suitable performance metrics for evaluating the compression techniques were listed from various literature studies.

RESULTS

In addition to the Gastrolab database, WEO clinical endoscopy atlas and Gastrointestinal atlas were found to be better alternatives for experimentation. Pre-processing operations, especially new subsampling patterns need to be given more focus to exploit the redundancies in the images. Investigations showed that encoder module can be modified to bring more improvement towards compression. The real-time endoscopy still exists as a promising area for exploration.

CONCLUSION

This review presents a research update on the details of wireless capsule endoscopy compression together with the findings as an eye-opener and guidance for further research.

High pooled performance of convolutional neural networks in computer-aided diagnosis of GI ulcers and/or hemorrhage on wireless capsule endoscopy images: a systematic review and meta-analysis

BACKGROUND AND AIMS

Diagnosis of GI ulcers and/or hemorrhage by wireless capsule endoscopy (WCE) is limited by the physician-dependent, tedious, time-consuming process of image and/ or video classification. Computer-aided diagnosis (CAD) by convolutional neural network (CNN)-based machine learning may help reduce this burden. Our aim was to conduct a meta-analysis and appraise the reported data.

METHODS

Multiple databases were searched (from inception to November 2019), and studies that reported on the performance of CNN in the diagnosis of GI ulcerations and/or hemorrhage on WCE were selected. A random-effects model was used to calculate the pooled rates. In cases where multiple 2 × 2 contingency tables were provided for different thresholds, we assumed the data tables were independent from each other. Heterogeneity was assessed by I2% and 95% prediction intervals.

RESULTS

Nine studies were included in our final analysis that evaluated the performance of CNN-based CAD of GI ulcers and/or hemorrhage by WCE. The pooled accuracy was 95.4% (95% confidence interval [CI], 94.3-96.3), sensitivity was 95.5% (95% CI, 94-96.5), specificity was 95.8% (95% CI, 94.7-96.6), positive predictive value was 95.8% (95% CI, 90.5-98.2), and negative predictive value was 96.8% (95% CI, 94.9-98.1). I2% heterogeneity was negligible except for the pooled positive predictive value.

CONCLUSIONS

Based on our meta-analysis, CNN-based CAD of GI ulcerations and/or hemorrhage on WCE achieves a high-level performance. The quality of the evidence is robust, and therefore CNN-based CAD has the potential to become the first choice of machine learning to optimize WCE image/video reading.

Artificial intelligence and deep learning for small bowel capsule endoscopy

Capsule endoscopy is ideally suited to artificial intelligence-based interpretation given its reliance on pattern recognition in still images. Time saving viewing modes and lesion detection features currently available rely on machine learning algorithms, a form of artificial intelligence. Current software necessitates close human supervision given poor sensitivity relative to an expert reader. However, with the advent of deep learning, artificial intelligence is becoming increasingly reliable and will be increasingly relied upon. We review the major advances in artificial intelligence for capsule endoscopy in recent publications and briefly review artificial intelligence development for historical understanding. Importantly, recent advancements in artificial intelligence have not yet been incorporated into practice and it is immature to judge the potential of this technology based on current platforms. Remaining regulatory and standardization hurdles are being overcome and artificial intelligence-based clinical applications are likely to proliferate rapidly.

Artificial intelligence in small bowel capsule endoscopy - current status, challenges and future promise

Neural network-based solutions are under development to alleviate physicians from the tedious task of small-bowel capsule endoscopy reviewing. Computer-assisted detection is a critical step, aiming to reduce reading times while maintaining accuracy. Weakly supervised solutions have shown promising results; however, video-level evaluations are scarce, and no prospective studies have been conducted yet. Automated characterization (in terms of diagnosis and pertinence) by supervised machine learning solutions is the next step. It relies on large, thoroughly labeled databases, for which preliminary "ground truth" definitions by experts are of tremendous importance. Other developments are under ways, to assist physicians in localizing anatomical landmarks and findings in the small bowel, in measuring lesions, and in rating bowel cleanliness. It is still questioned whether artificial intelligence will enter the market with proprietary, built-in or plug-in software, or with a universal cloud-based service, and how it will be accepted by physicians and patients.

Video Capsule Endoscopy and Ingestible Electronics: Emerging Trends in Sensors, Circuits, Materials, Telemetry, Optics, and Rapid Reading Software

Real-time monitoring of the gastrointestinal tract in a safe and comfortable manner is valuable for the diagnosis and therapy of many diseases. Within this realm, our review captures the trends in ingestible capsule systems with a focus on hardware and software technologies used for capsule endoscopy and remote patient monitoring. We introduce the structure and functions of the gastrointestinal tract, and the FDA guidelines for ingestible wireless telemetric medical devices. We survey the advanced features incorporated in ingestible capsule systems, such as microrobotics, closed-loop feedback, physiological sensing, nerve stimulation, sampling and delivery, panoramic imaging with adaptive frame rates, and rapid reading software. Examples of experimental and commercialized capsule systems are presented with descriptions of their sensors, devices, and circuits for gastrointestinal health monitoring. We also show the recent research in biocompatible materials and batteries, edible electronics, and alternative energy sources for ingestible capsule systems. The results from clinical studies are discussed for the assessment of key performance indicators related to the safety and effectiveness of ingestible capsule procedures. Lastly, the present challenges and outlook are summarized with respect to the risks to health, clinical testing and approval process, and technology adoption by patients and clinicians.

Artificial intelligence in gastrointestinal endoscopy

BACKGROUND AND AIMS

Artificial intelligence (AI)-based applications have transformed several industries and are widely used in various consumer products and services. In medicine, AI is primarily being used for image classification and natural language processing and has great potential to affect image-based specialties such as radiology, pathology, and gastroenterology (GE). This document reviews the reported applications of AI in GE, focusing on endoscopic image analysis.

METHODS

The MEDLINE database was searched through May 2020 for relevant articles by using key words such as machine learning, deep learning, artificial intelligence, computer-aided diagnosis, convolutional neural networks, GI endoscopy, and endoscopic image analysis. References and citations of the retrieved articles were also evaluated to identify pertinent studies. The manuscript was drafted by 2 authors and reviewed in person by members of the American Society for Gastrointestinal Endoscopy Technology Committee and subsequently by the American Society for Gastrointestinal Endoscopy Governing Board.

RESULTS

Deep learning techniques such as convolutional neural networks have been used in several areas of GI endoscopy, including colorectal polyp detection and classification, analysis of endoscopic images for diagnosis of Helicobacter pylori infection, detection and depth assessment of early gastric cancer, dysplasia in Barrett's esophagus, and detection of various abnormalities in wireless capsule endoscopy images.

CONCLUSIONS

The implementation of AI technologies across multiple GI endoscopic applications has the potential to transform clinical practice favorably and improve the efficiency and accuracy of current diagnostic methods.

Artificial intelligence that determines the clinical significance of capsule endoscopy images can increase the efficiency of reading

Artificial intelligence (AI), which has demonstrated outstanding achievements in image recognition, can be useful for the tedious capsule endoscopy (CE) reading. We aimed to develop a practical AI-based method that can identify various types of lesions and tried to evaluate the effectiveness of the method under clinical settings. A total of 203,244 CE images were collected from multiple centers selected considering the regional distribution. The AI based on the Inception-Resnet-V2 model was trained with images that were classified into two categories according to their clinical significance. The performance of AI was evaluated with a comparative test involving two groups of reviewers with different experiences. The AI summarized 67,008 (31.89%) images with a probability of more than 0.8 for containing lesions in 210,100 frames of 20 selected CE videos. Using the AI-assisted reading model, reviewers in both the groups exhibited increased lesion detection rates compared to those achieved using the conventional reading model (experts; 34.3%–73.0%; p = 0.029, trainees; 24.7%–53.1%; p = 0.029). The improved result for trainees was comparable to that for the experts (p = 0.057). Further, the AI-assisted reading model significantly shortened the reading time for trainees (1621.0–746.8 min; p = 0.029). Thus, we have developed an AI-assisted reading model that can detect various lesions and can successfully summarize CE images according to clinical significance. The assistance rendered by AI can increase the lesion detection rates of reviewers. Especially, trainees could improve their efficiency of reading as a result of reduced reading time using the AI-assisted model.

Clinical Performance of New Software to Automatically Detect Angioectasias in Small Bowel Capsule Endoscopy

Background

Video capsule endoscopy (VCE) revolutionized the diagnosis and management of obscure gastrointestinal bleeding, though the rate of detection of small bowel lesions by the physician is still disappointing. Our group developed a novel algorithm (CMEMS-Uminho) to automatically detect angioectasias which display greater accuracy in VCE static frames than other methods previously published. We aimed to evaluate the algorithm overall performance and assess its diagnostic yield and usability in clinical practice.

Methods

Algorithm overall performance was determined using 54 full-length VCE recordings. To assess its diagnostic yield and usability in clinical practice, 38 VCE examinations with the clinical diagnosis of angioectasias consecutively performed (2017-2018) were evaluated by three physicians with different experiences. The CMEMS-Uminho algorithm was also applied. The performance of the CMEMS-Uminho algorithm was defined by a positive concordance between a frame automatically selected by the software and a study independent capsule endoscopist.

Results

Overall performance in complete VCE recordings was 77.7%, and diagnostic yield was 94.7%. There were significant differences between physicians in regard to global detection rate (p < 0.001), detection rate per capsule (p < 0.001), diagnostic yield (p = 0.007), true positive rate (p < 0.001), time (p < 0.001), and speed viewing (p < 0.001). The application of CMEMS-Uminho algorithm significantly enhanced all readers' global detection rate (p < 0.001) and the differences between them were no longer observed.

Conclusion

The CMEMS-Uminho algorithm detained a good overall performance and was able to enhance physicians' performance, suggesting a potential usability of this tool in clinical practice.

A New Active Locomotion Capsule Endoscopy under Magnetic Control and Automated Reading Program

Capsule endoscopy (CE) is the first-line diagnostic modality for detecting small bowel lesions. CE is non-invasive and does not require sedation, but its movements cannot be controlled, it requires a long time for interpretation, and it has lower image quality compared to wired endoscopy. With the rapid advancement of technology, several methods to solve these problems have been developed. This article describes the ongoing developments regarding external CE locomotion using magnetic force, artificial intelligence-based interpretation, and image-enhancing technologies with the CE system.

The Future of Capsule Endoscopy: The Role of Artificial Intelligence and Other Technical Advancements

Capsule endoscopy has revolutionized the management of small-bowel diseases owing to its convenience and noninvasiveness. Capsule endoscopy is a common method for the evaluation of obscure gastrointestinal bleeding, Crohn’s disease, small-bowel tumors, and polyposis syndrome. However, the laborious reading process, oversight of small-bowel lesions, and lack of locomotion are major obstacles to expanding its application. Along with recent advances in artificial intelligence, several studies have reported the promising performance of convolutional neural network systems for the diagnosis of various small-bowel lesions including erosion/ulcers, angioectasias, polyps, and bleeding lesions, which have reduced the time needed for capsule endoscopy interpretation. Furthermore, colon capsule endoscopy and capsule endoscopy locomotion driven by magnetic force have been investigated for clinical application, and various capsule endoscopy prototypes for active locomotion, biopsy, or therapeutic approaches have been introduced. In this review, we will discuss the recent advancements in artificial intelligence in the field of capsule endoscopy, as well as studies on other technological improvements in capsule endoscopy.

Automatic Detection of Intestinal Bleeding using an Optical Sensor for Wireless Capsule Endoscopy

Wireless capsule endoscopy (WCE) has been an effective and safe way to diagnose gastrointestinal (GI) disorders, such as, colon cancers, polyps and bleeding. The detection of bleeding and other anomalies is currently determined through conventional visual inspection of the WCE images by the physicians. An on-chip bleeding sensor is thus required, that can perform an automatic prescreening of the bleeding areas in real-time using blood's optical properties to assist the diagnosis. In this study, a spectrophotometer was initially used to evaluate the chromatic properties of blood. It is found that the reflection ratio pairs of 700 nm to 630 nm and 480 nm to 530 nm provide important statistics to separate blood from non-blood samples. It has been implemented hardware using small LEDs and photodiodes to validate the results. Therefore, the proposed sensor system works as a good candidate to be integrated in a WCE device to detect GI bleeding quickly and in real-time.

Automatic detection of blood content in capsule endoscopy images based on a deep convolutional neural network

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.

Clinical usefulness of a deep learning-based system as the first screening on small-bowel capsule endoscopy reading

BACKGROUND AND AIM

To examine whether our convolutional neural network (CNN) system based on deep learning can reduce the reading time of endoscopists without oversight of abnormalities in the capsule-endoscopy reading process.

METHODS

Twenty videos of the entire small-bowel capsule endoscopy procedure were prepared, each of which included 0-5 lesions of small-bowel mucosal breaks (erosions or ulcerations). At another institute, two reading processes were compared: (A) endoscopist-alone readings and (B) endoscopist readings after the first screening by the proposed CNN. In process B, endoscopists read only images detected by CNN. Two experts and four trainees independently read 20 videos each (10 for process A and 10 for process B). Outcomes were reading time and detection rate of mucosal breaks by endoscopists. Gold standard was findings at the original institute by two experts.

RESULTS

Mean reading time of small-bowel sections by endoscopists was significantly shorter during process B (expert, 3.1 min; trainee, 5.2 min) compared to process A (expert, 12.2 min; trainee, 20.7 min) (P < 0.001). For 37 mucosal breaks, detection rate by endoscopists did not significantly decrease in process B (expert, 87%; trainee, 55%) compared to process A (expert, 84%; trainee, 47%). Experts detected all eight large lesions (>5 mm), but trainees could not, even when supported by the CNN.

CONCLUSIONS

Our CNN-based system for capsule endoscopy videos reduced the reading time of endoscopists without decreasing the detection rate of mucosal breaks. However, the reading level of endoscopists should be considered when using the system.

Prospective multicenter study to evaluate capsule endoscopy competency using a validated assessment tool

BACKGROUND AND AIMS

Capsule endoscopy (CE) is an established, noninvasive modality for examining the small bowel. Minimum training requirements are based primarily on guidelines and expert opinion. A validated tool to assess the competence of CE is lacking. In this prospective, multicenter study, we determined the minimum number of CE procedures required to achieve competence during gastroenterology fellowship; validated a capsule competency test (CapCT); and evaluated any correlation between CE competence and endoscopy experience.

METHODS

We included second- and third-year gastroenterology fellows from 3 institutions between 2013 and 2018 in a structured CE training program with supervised CE interpretation. Fellows completed the CapCT with a maximal score of 100. For comparison, expert faculty completed the same CapCT. Trainee competence was defined as a score ≥90% compared with the mean expert score. Fellows were tested after 15, 25, and 35 supervised CE interpretations. CapCT was validated using expert consensus and item analysis. Data were collected on the number of previous endoscopies.

RESULTS

A total of 68 trainees completed 102 CapCTs. Fourteen CE experts completed the CapCT with a mean score of 94. Mean scores for fellows after 15, 25, and 35 cases were 83, 86, and 87, respectively. Fellows with at least 25 interpretations achieved a mean score ≥84 in all 3 institutions. CapCT item analysis showed high interobserver agreement among expert faculty (k = 0.85). There was no correlation between the scores and the number of endoscopies performed.

CONCLUSION

After a structured CE training program, gastroenterology fellows should complete a minimum of 25 supervised CE interpretations before assessing competence using the validated CapCT, regardless of endoscopy experience.

Compact wide-angle capsule endoscopic lens design

Capsule endoscopes require a high-quality imaging system in terms of the wide field of view (FOV), image brightness, and resolution to provide accurate diagnostic information. However, because of the wide-angle lens design, the first element of the lens inevitably becomes larger in diameter, making it difficult to reduce the overall size of the lens. In this study, the compact wide-angle lens for a capsule endoscope is reported. The proposed system allows the first element to be compact in diameter and increases image quality by utilizing all aspheric surfaces for optical aberration control. The specification of the proposed capsule endoscope lens shows the wide FOV of 160 deg, F-number 2.8, and total track length of 5 mm. In the overall FOV, relative illumination is still over 60%. To achieve a high image quality in the proposed system, the modulation transfer function is over 30% at 180 lp/mm for a${1920} \times {1080}$1920×1080 1/6 inch CMOS image sensor in a pixel size of 1.4 µm.

3D reconstruction of small bowel lesions using stereo camera-based capsule endoscopy

Three-dimensional (3D) reconstruction of capsule endoscopic images has been attempted for a long time to obtain more information on small bowel structures. Due to the limited hardware resources of capsule size and battery capacity, software approaches have been studied but have mainly exhibited inherent limitations. Recently, stereo camera-based capsule endoscopy, which can perform hardware-enabled 3D reconstruction, has been developed. We aimed to evaluate the feasibility of newly developed 3D capsule endoscopy in clinical practice. This study was a prospective, single-arm, feasibility study conducted at two university-affiliated hospitals in South Korea. Small bowel evaluation was performed using a newly developed 3D capsule endoscope for patients with obscure gastrointestinal bleeding, suspected or established Crohn's disease, small bowel tumors, and abdominal pain of unknown origin. We assessed the technical limitations, performance, and safety of the new capsule endoscope. Thirty-one patients (20 men and 11 women; mean age: 44.5 years) were enrolled. There was no technical defect preventing adequate visualization of the small bowel. The overall completion rate was 77.4%, the detection rate was 64.5%, and there was no capsule retention. All capsule endoscopic procedures were completed uneventfully. In conclusion, newly developed 3D capsule endoscopy was safe and feasible, showing similar performance as conventional capsule endoscopy. Newly added features of 3D reconstruction and size measurement are expected to be useful in the characterization of subepithelial tumours.

Ultrasound Capsule Endoscopy With a Mechanically Scanning Micro-ultrasound: A Porcine Study

Wireless capsule endoscopy has been used for the clinical examination of the gastrointestinal (GI) tract for two decades. However, most commercially available devices only utilise optical imaging to examine the GI wall surface. Using this sensing modality, pathology within the GI wall cannot be detected. Micro-ultrasound (μUS) using high-frequency (>20 MHz) ultrasound can provide a means of transmural or cross-sectional image of the GI tract. Depth of imaging is approximately 10 mm with a resolution of between 40-120 μm that is sufficient to differentiate between subsurface histologic layers of the various regions of the GI tract. Ultrasound capsule endoscopy (USCE) uses a capsule equipped with μUS transducers that are capable of imaging below the GI wall surface, offering thereby a complementary sensing technique to optical imaging capsule endoscopy. In this work, a USCE device integrated with a ∼30 MHz ultrasonic transducer was developed to capture a full 360° image of the lumen. The performance of the device was initially evaluated using a wire phantom, indicating an axial resolution of 69.0 μm and lateral resolution of 262.5 μm. Later, in vivo imaging performance was characterised in the oesophagus and small intestine of anaesthetized pigs. The reconstructed images demonstrate clear layer differentiation of the lumen wall. The tissue thicknesses measured from the B-scan images show good agreement with ex vivo images from the literature. The high-resolution ultrasound images in the in vivo porcine model achieved with this device is an encouraging preliminary step in the translation of these devices toward future clinical use.

Colored Video Analysis in Wireless Capsule Endoscopy: A Survey of State-of-the-Art

Wireless Capsule Endoscopy (WCE) is a highly promising technology for gastrointestinal (GI) tract abnormality diagnosis. However, low image resolution and low frame rates are challenging issues in WCE. In addition, the relevant frames containing the features of interest for accurate diagnosis only constitute 1% of the complete video information. For these reasons, analyzing the WCE videos is still a time consuming and laborious examination for the gastroenterologists, which reduces WCE system usability. This leads to the emergent need to speed-up and automates the WCE video process for GI tract examinations. Consequently, the present work introduced the concept of WCE technology, including the structure of WCE systems, with a focus on the medical endoscopy video capturing process using image sensors. It discussed also the significant characteristics of the different GI tract for effective feature extraction. Furthermore, video approaches for bleeding and lesion detection in the WCE video were reported with computer-aided diagnosis systems in different applications to support the gastroenterologist in the WCE video analysis. In image enhancement, WCE video review time reduction is also discussed, while reporting the challenges and future perspectives, including the new trend to employ the deep learning models for feature Learning, polyp recognition, and classification, as a new opportunity for researchers to develop future WCE video analysis techniques.

Synchronized Biventricular Heart Pacing in a Closed-chest Porcine Model based on Wirelessly Powered Leadless Pacemakers

About 30% of patients with impaired cardiac function have ventricular dyssynchrony and seek cardiac resynchronization therapy (CRT). In this study, we demonstrate synchronized biventricular (BiV) pacing in a leadless fashion by implementing miniaturized and wirelessly powered pacemakers. With their flexible form factors, two pacemakers were implanted epicardially on the right and left ventricles of a porcine model and were inductively powered at 13.56 MHz and 40.68 MHz industrial, scientific, and medical (ISM) bands, respectively. The power consumption of these pacemakers is reduced to µW-level by a novel integrated circuit design, which considerably extends the maximum operating distance. Leadless BiV pacing is demonstrated for the first time in both open-chest and closed-chest porcine settings. The clinical outcomes associated with different interventricular delays are verified through electrophysiologic and hemodynamic responses. The closed-chest pacing only requires the external source power of 0.3 W and 0.8 W at 13.56 MHz and 40.68 MHz, respectively, which leads to specific absorption rates (SARs) 2–3 orders of magnitude lower than the safety regulation limit. This work serves as a basis for future wirelessly powered leadless pacemakers that address various cardiac resynchronization challenges.

Diagnosis and Management of Non-Variceal Gastrointestinal Hemorrhage: A Review of Current Guidelines and Future Perspectives

Non-variceal gastrointestinal bleeding (GIB) is a significant cause of mortality and morbidity worldwide which is encountered in the ambulatory and hospital settings. Hemorrhage form the gastrointestinal (GI) tract is categorized as upper GIB, small bowel bleeding (also formerly referred to as obscure GIB) or lower GIB. Although the etiologies of GIB are variable, a strong, consistent risk factor is use of non-steroidal anti-inflammatory drugs. Advances in the endoscopic diagnosis and treatment of GIB have led to improved outcomes. We present an updated review of the current practices regarding the diagnosis and management of non-variceal GIB, and possible future directions.

Esophageal Capsule Endoscopy in Children and Young Adults With Portal Hypertension

OBJECTIVES

Variceal hemorrhage (VH) is a serious complication of portal hypertension (PH). We evaluated the feasibility, safety, and clinical impact of esophageal capsule endoscopy (ECE) in pediatric and young adult patients with known or suspected PH.

METHODS

Children and young adults with PH at Boston Children's Hospital (2005-2017) were offered ECE for variceal screening or surveillance. Patient histories, ECE findings, and clinical outcomes were reviewed retrospectively.

RESULTS

One hundred and forty-nine ECE studies were performed in 98 patients (57.1% male patients) using 3 ECE devices for variceal screening (66.5%) or surveillance (33.5%). Three readers interpreted the studies (88.3%, 10.3%, and 1.4%, respectively). Median age was 16 years (IQR 13.7-18.5). One hundred and three ECE studies involved patients <18 years (69.1%). Fifteen patients (29 ECE studies) had a gastrointestinal (GI) bleeding (GIB) history, 5 in the preceding 12 months.Sixty-two ECE studies (44.9%) detected varices: 59 esophageal (40 small, 19 medium/large), 17 gastric, 6 duodenal. Other findings included: portal gastropathy (25, 18.1%), esophagitis (20, 14.5%), ulcers (5, 3.6%), erosions (31, 22.5%), heterotopic tissue (13, 9.4%), blood flecks (23, 16.7%), and mucosal scars (17, 12.3%). There were 2 transient capsule retentions and no major adverse events.ECE led to follow-up EGD in 11 (7 variceal banding) and medication initiation in 12 (4 proton-pump inhibitor, 7 nonselective beta blocker, 2 other) cases. Four patients had GIB within 12 months of ECE.

CONCLUSION

ECE is a feasible alternative to EGD for screening and surveillance of esophageal varices in children and young adults.

A Multi-site Heart Pacing Study Using Wirelessly Powered Leadless Pacemakers

In this work, we report an energy-efficient switched capacitor based millimeter-scale pacemaker (5 mm ×7.5 mm) and a multi-receiver wireless energy transfer system operating at around 200 MHz, and use them in a proof-of-concept multi-site heart pacing study. Two pacemakers were placed on two beating Langendorff rodent heart models separately. By utilizing a single transmitter positioned 20-30 cm away, both Langendorff hearts captured the stimuli simultaneously and were electromechanically coupled. This study provides an insight for future energy-efficient and distributed cardiac pacemakers that can offer cardiac resynchronization therapies.

Capsule endoscopy for small-intestinal disorders: Current status

Small-bowel capsule endoscopy (SBCE) is used widely because of its non-invasive and patient-friendly nature. SBCE can visualize entire small-intestinal mucosa and facilitate detection of small-intestinal abnormalities. In this review article, we focus on the current status of SBCE. Several platforms for SBCE are available worldwide. Third-generation SBCE (PillCam® SB3) has a high-resolution camera equipped with an adaptive frame rate system. Several software modes have been developed to reduce the reading time for capsule endoscopy and to minimize the possibility of missing lesions. The main complication of SBCE is capsule retention. Thus, the main contraindication for SBCE is known or suspected gastrointestinal obstruction unless intestinal patency is proven. Possible indications for SBCE are obscure gastrointestinal bleeding, Crohn's disease, small-intestinal polyps and tumors, and celiac disease. Colon capsule endoscopy (CCE) can observe inflamed colonic mucosa non-invasively, and allows for the continuous and non-invasive observation of the entire intestinal tract (pan-endoscopy). Recently, application of CCE as pan-enteric endoscopy for inflammatory bowel diseases (including Crohn's disease) has been reported. In the near future, reading for CE will be assisted by artificial intelligence, and reading CE videos for long periods will not be required.