Capsule endoscopy - Recent developments and future directions

Postoperative small bowel Crohn's disease: how to diagnose, manage and treat

PURPOSE OF REVIEW

Crohn's disease is a relapsing inflammatory condition and disease recurrence after surgery is common. Significant variation in clinical practice remains despite progress in management of postoperative Crohn's disease. In this review, we summarise current management strategies and guidelines, unmet needs, and research progress in this field.

RECENT FINDINGS

There has been real progress in risk stratifying individuals' postsurgery and tailoring therapies based on their risk; this has been incorporated into current management guidelines in the USA, UK, and Europe. Furthermore, novel noninvasive monitoring tools such as intestinal ultrasound have shown high sensitivity and specificity at detecting disease recurrence and are an attractive point-of-care test. Recent studies are also investigating multiomic biomarkers to prognosticate postoperative Crohn's disease. However, given the heterogeneity within this condition, large multicentre clinical validation across all age groups is needed for clinical translation in the future.

SUMMARY

Ongoing progress in research and the development of novel prognostic and noninvasive disease monitoring tools offers hope for personalised therapy tailored to individual recurrence risk in postoperative Crohn's disease.

Magnetically driven capsules with multimodal response and multifunctionality for biomedical applications

Untethered capsules hold clinical potential for the diagnosis and treatment of gastrointestinal diseases. Although considerable progress has been achieved recently in this field, the constraints imposed by the narrow spatial structure of the capsule and complex gastrointestinal tract environment cause many open-ended problems, such as poor active motion and limited medical functions. In this work, we describe the development of small-scale magnetically driven capsules with a distinct magnetic soft valve made of dual-layer ferromagnetic soft composite films. A core technological advancement achieved is the flexible opening and closing of the magnetic soft valve by using the competitive interactions between magnetic gradient force and magnetic torque, laying the foundation for the functional integration of both drug release and sampling. Meanwhile, we propose a magnetic actuation strategy based on multi-frequency response control and demonstrate that it can achieve effective decoupled regulation of the capsule's global motion and local responses. Finally, through a comprehensive approach encompassing ideal models, animal ex vivo models, and in vivo assessment, we demonstrate the versatility of the developed magnetic capsules and their multiple potential applications in the biomedical field, such as targeted drug delivery and sampling, selective dual-drug release, and light/thermal-assisted therapy.

Improving Respiratory Infection Diagnosis with Deep Learning and Combinatorial Fusion: A Two-Stage Approach Using Chest X-ray Imaging

The challenges of respiratory infections persist as a global health crisis, placing substantial stress on healthcare infrastructures and necessitating ongoing investigation into efficacious treatment modalities. The persistent challenge of respiratory infections, including COVID-19, underscores the critical need for enhanced diagnostic methodologies to support early treatment interventions. This study introduces an innovative two-stage data analytics framework that leverages deep learning algorithms through a strategic combinatorial fusion technique, aimed at refining the accuracy of early-stage diagnosis of such infections. Utilizing a comprehensive dataset compiled from publicly available lung X-ray images, the research employs advanced pre-trained deep learning models to navigate the complexities of disease classification, addressing inherent data imbalances through methodical validation processes. The core contribution of this work lies in its novel application of combinatorial fusion, integrating select models to significantly elevate diagnostic precision. This approach not only showcases the adaptability and strength of deep learning in navigating the intricacies of medical imaging but also marks a significant step forward in the utilization of artificial intelligence to improve outcomes in healthcare diagnostics. The study's findings illuminate the path toward leveraging technological advancements in enhancing diagnostic accuracies, ultimately contributing to the timely and effective treatment of respiratory diseases.

Smart capsules for sensing and sampling the gut: status, challenges and prospects

Smart capsules are developing at a tremendous pace with a promise to become effective clinical tools for the diagnosis and monitoring of gut health. This field emerged in the early 2000s with a successful translation of an endoscopic capsule from laboratory prototype to a commercially viable clinical device. Recently, this field has accelerated and expanded into various domains beyond imaging, including the measurement of gut physiological parameters such as temperature, pH, pressure and gas sensing, and the development of sampling devices for better insight into gut health. In this review, the status of smart capsules for sensing gut parameters is presented to provide a broad picture of these state-of-the-art devices while focusing on the technical and clinical challenges the devices need to overcome to realise their value in clinical settings. Smart capsules are developed to perform sensing operations throughout the length of the gut to better understand the body's response under various conditions. Furthermore, the prospects of such sensing devices are discussed that might help readers, especially health practitioners, to adapt to this inevitable transformation in healthcare. As a compliment to gut sensing smart capsules, significant amount of effort has been put into the development of robotic capsules to collect tissue biopsy and gut microbiota samples to perform in-depth analysis after capsule retrieval which will be a game changer for gut health diagnosis, and this advancement is also covered in this review. The expansion of smart capsules to robotic capsules for gut microbiota collection has opened new avenues for research with a great promise to revolutionise human health diagnosis, monitoring and intervention.

Interobserver agreement on landmark and flexure identification in colon capsule endoscopy

PURPOSE

When an optical colonoscopy is carried out, Scope Guide can assist the endoscopist in determining the localization. In colon capsule endoscopy (CCE), this support is not available. To our knowledge, the interobserver agreement on landmark identification has never been studied. This study aims to investigate the interobserver agreement on landmark identification in CCE.

METHODS

An interobserver study was carried out comparing the landmark identification (the ileocecal valve, hepatic flexure, splenic flexure, and anus) in CCE investigations between an external private contractor and three in-house CCE readers with different levels of experience. All CCE investigations analyzed in this study were carried out as a part of the Danish screening program for colorectal cancer. Patients were between 50 and 74 years old with a positive fecal immunochemical test (FIT). A random sample of 20 CCE investigations was taken from the total sample of more than 800 videos.

RESULTS

Overall interobserver agreement on all landmarks was 51%. Interobserver agreement on the first cecal image (ileocecal valve), hepatic flexure, splenic flexure, and last rectal image (anus) was 72%, 29%, 22%, and 83%, respectively. The overall interobserver agreement, including only examinations with adequate bowel preparation (n = 16), was 54%, and for individual landmarks, 73%, 32%, 24%, and 85%.

CONCLUSION

Overall interobserver agreement on all four landmarks from CCE was poor. Measures are needed to improve landmark identification in CCE investigations. Artificial intelligence could be a possible solution to this problem.

Public Imaging Datasets of Gastrointestinal Endoscopy for Artificial Intelligence: a Review

With the advances in endoscopic technologies and artificial intelligence, a large number of endoscopic imaging datasets have been made public to researchers around the world. This study aims to review and introduce these datasets. An extensive literature search was conducted to identify appropriate datasets in PubMed, and other targeted searches were conducted in GitHub, Kaggle, and Simula to identify datasets directly. We provided a brief introduction to each dataset and evaluated the characteristics of the datasets included. Moreover, two national datasets in progress were discussed. A total of 40 datasets of endoscopic images were included, of which 34 were accessible for use. Basic and detailed information on each dataset was reported. Of all the datasets, 16 focus on polyps, and 6 focus on small bowel lesions. Most datasets (n = 16) were constructed by colonoscopy only, followed by normal gastrointestinal endoscopy and capsule endoscopy (n = 9). This review may facilitate the usage of public dataset resources in endoscopic research.

The Saudi Gastroenterology Association guidelines for quality indicators in gastrointestinal endoscopic procedures

The quality and safety of gastrointestinal endoscopy varies considerably across regions and facilities worldwide. In this field, quality management has traditionally focused on individual performance of endoscopists, with most indicators addressing process measures and limited evidence of improvement in health outcomes. Indicators of quality can be classified according to their nature and sequence. The various professional societies and organizations have proposed many systems of indicators, but a universal system is necessary so that healthcare professionals are not overburdened and confused with a variety of quality improvement approaches. In this paper, we propose guidelines by the Saudi Gastroenterology Association pertaining to quality in endoscopic procedures aiming to improve the awareness of endoscopy unit staff toward important quality indications to enhance and standardize quality of care provided to our patients.

Clinicians’ Guide to Artificial Intelligence in Colon Capsule Endoscopy—Technology Made Simple

Artificial intelligence (AI) applications have become widely popular across the healthcare ecosystem. Colon capsule endoscopy (CCE) was adopted in the NHS England pilot project following the recent COVID pandemic’s impact. It demonstrated its capability to relieve the national backlog in endoscopy. As a result, AI-assisted colon capsule video analysis has become gastroenterology’s most active research area. However, with rapid AI advances, mastering these complex machine learning concepts remains challenging for healthcare professionals. This forms a barrier for clinicians to take on this new technology and embrace the new era of big data. This paper aims to bridge the knowledge gap between the current CCE system and the future, fully integrated AI system. The primary focus is on simplifying the technical terms and concepts in machine learning. This will hopefully address the general “fear of the unknown in AI” by helping healthcare professionals understand the basic principle of machine learning in capsule endoscopy and apply this knowledge in their future interactions and adaptation to AI technology. It also summarises the evidence of AI in CCE and its impact on diagnostic pathways. Finally, it discusses the unintended consequences of using AI, ethical challenges, potential flaws, and bias within clinical settings.

Bioelectronic devices for light-based diagnostics and therapies

Light has broad applications in medicine as a tool for diagnosis and therapy. Recent advances in optical technology and bioelectronics have opened opportunities for wearable, ingestible, and implantable devices that use light to continuously monitor health and precisely treat diseases. In this review, we discuss recent progress in the development and application of light-based bioelectronic devices. We summarize the key features of the technologies underlying these devices, including light sources, light detectors, energy storage and harvesting, and wireless power and communications. We investigate the current state of bioelectronic devices for the continuous measurement of health and on-demand delivery of therapy. Finally, we highlight major challenges and opportunities associated with light-based bioelectronic devices and discuss their promise for enabling digital forms of health care.

Artificial intelligence-based diagnosis of abnormalities in small-bowel capsule endoscopy

BACKGROUND : Further development of deep learning-based artificial intelligence (AI) technology to automatically diagnose multiple abnormalities in small-bowel capsule endoscopy (SBCE) videos is necessary. We aimed to develop an AI model, to compare its diagnostic performance with doctors of different experience levels, and to further evaluate its auxiliary role for doctors in diagnosing multiple abnormalities in SBCE videos. METHODS : The AI model was trained using 280 426 images from 2565 patients, and the diagnostic performance was validated in 240 videos. RESULTS : The sensitivity of the AI model for red spots, inflammation, blood content, vascular lesions, protruding lesions, parasites, diverticulum, and normal variants was 97.8 %, 96.1 %, 96.1 %, 94.7 %, 95.6 %, 100 %, 100 %, and 96.4 %, respectively. The specificity was 86.0 %, 75.3 %, 87.3 %, 77.8 %, 67.7 %, 97.5 %, 91.2 %, and 81.3 %, respectively. The accuracy was 95.0 %, 88.8 %, 89.2 %, 79.2 %, 80.8 %, 97.5 %, 91.3 %, and 93.3 %, respectively. For junior doctors, the assistance of the AI model increased the overall accuracy from 85.5 % to 97.9 % (P  < 0.001, Bonferroni corrected), comparable to that of experts (96.6 %, P > 0.0125, Bonferroni corrected). CONCLUSIONS : This well-trained AI diagnostic model automatically diagnosed multiple small-bowel abnormalities simultaneously based on video-level recognition, with potential as an excellent auxiliary system for less-experienced endoscopists.

A novel intelligent chromo capsule endoscope for the diagnosis of neoplastic lesions in the gastrointestinal tract

Background

Chromoendoscopy has not been fully integrated into capsule endoscopy. This study aimded to develop and validate a novel intelligent chromo capsule endoscope (ICCE).

Methods

The ICCE has two modes: a white-light imaging (WLI) mode and an intelligent chromo imaging (ICI) mode. The performance of the ICCE in observing colors, animal tissues, and early gastrointestinal (GI) neoplastic lesions in humans was evaluated. Images captured by the ICCE were analysed using variance of Laplacian (VoL) values or image contrast evaluation.

Results

For color observation, conventional narrow-band imaging endoscopes and the ICI mode of the ICCE have similar spectral distributions. Compared with the WLI mode, the ICI mode had significantly higher VoL values for animal tissues (2.154 ± 1.044 vs 3.800 ± 1.491, P = 0.003), gastric precancerous lesions and early gastric cancers (2.242 ± 0.162 vs 6.642 ± 0.919, P < 0.001), and colon tumors (3.896 ± 1.430 vs 11.882 ± 7.663, P < 0.001), and significantly higher contrast for differentiating tumor and non-tumor areas (0.069 ± 0.046 vs 0.144 ± 0.076, P = 0.005). More importantly, the sensitivity, specificity, and accuracy of the ICI mode for early GI tumors were 95.83%, 91.67%, and 94.64%, respectively, which were significantly higher than the values of the WLI mode (78.33% [P < 0.001], 77.08% [P = 0.01], and 77.98% [P < 0.001], respectively).

Conclusions

We successfully integrated ICI into the capsule endoscope. The ICCE is an innovative and useful tool for differential diagnosis based on contrast-enhanced images and thus has great potential as a superior diagnostic tool for early GI tumor detection.

Artificial intelligence to improve polyp detection and screening time in colon capsule endoscopy

Colon Capsule Endoscopy (CCE) is a minimally invasive procedure which is increasingly being used as an alternative to conventional colonoscopy. Videos recorded by the capsule cameras are long and require one or more experts' time to review and identify polyps or other potential intestinal problems that can lead to major health issues. We developed and tested a multi-platform web application, AI-Tool, which embeds a Convolution Neural Network (CNN) to help CCE reviewers. With the help of artificial intelligence, AI-Tool is able to detect images with high probability of containing a polyp and prioritize them during the reviewing process. With the collaboration of 3 experts that reviewed 18 videos, we compared the classical linear review method using RAPID Reader Software v9.0 and the new software we present. Applying the new strategy, reviewing time was reduced by a factor of 6 and polyp detection sensitivity was increased from 81.08 to 87.80%.

Edge artificial intelligence wireless video capsule endoscopy

Gastrointestinal (GI) tract diseases are responsible for substantial morbidity and mortality worldwide, including colorectal cancer, which has shown a rising incidence among adults younger than 50. Although this could be alleviated by regular screening, only a small percentage of those at risk are screened comprehensively, due to shortcomings in accuracy and patient acceptance. To address these challenges, we designed an artificial intelligence (AI)-empowered wireless video endoscopic capsule that surpasses the performance of the existing solutions by featuring, among others: (1) real-time image processing using onboard deep neural networks (DNN), (2) enhanced visualization of the mucous layer by deploying both white-light and narrow-band imaging, (3) on-the-go task modification and DNN update using over-the-air-programming and (4) bi-directional communication with patient's personal electronic devices to report important findings. We tested our solution in an in vivo setting, by administrating our endoscopic capsule to a pig under general anesthesia. All novel features, successfully implemented on a single platform, were validated. Our study lays the groundwork for clinically implementing a new generation of endoscopic capsules, which will significantly improve early diagnosis of upper and lower GI tract diseases.

The Role of Capsule Endoscopy in Crohn's Disease: A Review

Crohn’s disease (CD) is a chronic inflammatory disorder with a predilection for the small bowel. Although awareness of this disorder has increased over the years, it remains a diagnostic challenge for many physicians. This is exacerbated by the rising incidence and high recurrence rate following therapy in certain individuals. It is currently agreed that a multimodality approach is the best one, but with the advent of new modalities, that could be changing. Furthermore, given its impact on the mental health of patients and the cost of treatment, it is pertinent that we arrive at not only convenient but accurate modalities in its diagnosis and management. Among these investigative modalities is the relatively novel capsule endoscopy (CE) that not only provides a more patient-friendly alternative but avoids the need for invasiveness. Asides from its diagnostic capability, its influence on therapy and monitoring of known CD patients following treatment has been shown. This article has reviewed the current literature comparing the relevance of CE with other available modalities in diagnosing CD patients. We explored its therapeutic impact and how it influences monitoring post-treatment in CD. This article also discusses the complications of CE and the possible solutions to these complications in the future.

A method for evaluating sensitivity of electromagnetic localization systems for wireless capsule endoscopes

This paper studies the use of electromagnetic induction in localization of wireless capsule endoscopes (WECs). There is still currently a need for an accurate localization system to enable localizing possible findings in the gastrointestinal tract, and to develop an active steering system for the capsule. Developing an optimal localization system requires the sensitivity of the system to be analyzed. In this paper, three different coil geometries are modelled with a computer simulation platform, and their sensitivities and target responses are compared. In order to do that, a formulation for the sensitivity based on the dipole model approximation is presented. The first coil array is based on literature and is used as a reference. The second array presents how having more transmit-receive channels in the array effects the sensitivity. The third coil array simulates the effect of increasing the field excitation intensity in different directions by using a three-axial Helmholtz array. In addition, both proposed coil arrays utilize larger coils than the reference. As a result, it seems that both increasing the coil size and the number of field projections interrogating the target increase the overall sensitivity in the region of interest and the target response. The findings suggest that an optimal coil array could utilize both large coils and multiple transmit-receive channels to increase the number of independent fields incident onto the target.

Capsule Endoscopy for Gastric Evaluation

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

Capsule Endoscopy: Pitfalls and Approaches to Overcome

Capsule endoscopy of the gastrointestinal tract is an innovative technology that serves to replace conventional endoscopy. Wireless capsule endoscopy, which is mainly used for small bowel examination, has recently been used to examine the entire gastrointestinal tract. This method is promising for its usefulness and development potential and enhances convenience by reducing the side effects and discomfort that may occur during conventional endoscopy. However, capsule endoscopy has fundamental limitations, including passive movement via bowel peristalsis and space restriction. This article reviews the current scientific aspects of capsule endoscopy and discusses the pitfalls and approaches to overcome its limitations. This review includes the latest research results on the role and potential of capsule endoscopy as a non-invasive diagnostic and therapeutic device.

Implementation of European Society of Gastrointestinal Endoscopy (ESGE) recommendations for small-bowel capsule endoscopy into clinical practice: Results of an official ESGE survey

BACKGROUND

We aimed to document international practices in small-bowel capsule endoscopy (SBCE), measuring adherence to European Society of Gastrointestinal Endoscopy (ESGE) technical and clinical recommendations.

METHODS

Participants reached through the ESGE contact list completed a 52-item web-based survey.

RESULTS

217 responded from 47 countries (176 and 41, respectively, from countries with or without a national society affiliated to ESGE). Of respondents, 45 % had undergone formal SBCE training. Among SBCE procedures, 91 % were performed with an ESGE recommended indication, obscure gastrointestinal bleeding (OGIB), iron-deficiency anemia (IDA), and suspected/established Crohn's disease being the commonest and with higher rates of positive findings (49.4 %, 38.2 % and 53.5 %, respectively). A watchful waiting strategy after a negative SBCE for OGIB or IDA was preferred by 46.7 % and 70.3 %, respectively. SBCE was a second-line exam for evaluation of extent of new Crohn's disease for 62.2 % of respondents. Endoscopists adhered to varying extents to ESGE technical recommendations regarding bowel preparation ( > 60 %), use in those with pacemaker holders (62.5 %), patency capsule use (51.2 %), and use of a validated scale for bowel preparation assessment (13.3 %). Of the respondents, 67 % read and interpreted the exams themselves and 84 % classified exams findings as relevant or irrelevant. Two thirds anticipated future increase in SBCE demand. Inability to obtain tissue (78.3 %) and high cost (68.1 %) were regarded as the main limitations, and implementation of artificial intelligence as the top development priority (56.2 %).

CONCLUSIONS

To some extent, endoscopists follow ESGE guidelines on using SBCE in clinical practice. However, variations in practice have been identified, whose implications require further evaluation.

Kvasir-Capsule, a video capsule endoscopy dataset

Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology.