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

Drugs Effective for Nonsteroidal Anti-inflammatory Drugs or Aspirin-induced Small Bowel Injuries: A Systematic Review and Meta-analysis of Randomized Controlled Trials

OBJECTIVE

The frequency of small bowel (SB) injuries has increased due to the increased use of nonsteroidal anti-inflammatory drugs (NSAIDs) or aspirin. This study was a systematic review and meta-analysis to compare drugs effective for SB injuries caused by NSAIDs or aspirin use.

METHODS

We searched MEDLINE, Embase, and Cochrane registries for randomized controlled trials through February 2023. The extracted data included changes in the number of erosions or ulcers in the jejunum or ileum observed through capsule endoscopy in patients taking NSAIDs or aspirin and administration of various mucoprotectants. We investigated the therapeutic or preventive efficacy of these drugs. The methodological bias was evaluated using Risk of Bias 2.0.

RESULTS

Eighteen randomized controlled trials of drugs effective for NSAIDs or aspirin-induced SB injuries were included and analyzed. The agents used to treat or prevent SB injuries were rebamipide, misoprostol, geranylgeranylacetone, and probiotics. In the meta-analysis, the mucoprotectants that showed a significant effect in treating NSAID users, who developed SB injuries, were misoprostol (mean difference: -9.88; 95% CI: -13.26 to -6.50). Meanwhile, the mucoprotectant that can prevent SB injuries caused by NSAIDs or aspirin in the general population was rebamipide (mean difference: -1.85; 95% CI: -2.74 to -0.96).

CONCLUSIONS

Misoprostol was effective in treating SB injuries caused by NSAIDs or aspirin (CRD42023410946).

Self-supervised out-of-distribution detection in wireless capsule endoscopy images

While deep learning has displayed excellent performance in a broad spectrum of application areas, neural networks still struggle to recognize what they have not seen, i.e., out-of-distribution (OOD) inputs. In the medical field, building robust models that are able to detect OOD images is highly critical, as these rare images could show diseases or anomalies that should be detected. In this study, we use wireless capsule endoscopy (WCE) images to present a novel patch-based self-supervised approach comprising three stages. First, we train a triplet network to learn vector representations of WCE image patches. Second, we cluster the patch embeddings to group patches in terms of visual similarity. Third, we use the cluster assignments as pseudolabels to train a patch classifier and use the Out-of-Distribution Detector for Neural Networks (ODIN) for OOD detection. The system has been tested on the Kvasir-capsule, a publicly released WCE dataset. Empirical results show an OOD detection improvement compared to baseline methods. Our method can detect unseen pathologies and anomalies such as lymphangiectasia, foreign bodies and blood with AUROC>0.6. This work presents an effective solution for OOD detection models without needing labeled images.

Artificial Intelligence in Inflammatory Bowel Disease Endoscopy: Implications for Clinical Trials

Artificial intelligence shows promise for clinical research in inflammatory bowel disease endoscopy. Accurate assessment of endoscopic activity is important in clinical practice and inflammatory bowel disease clinical trials. Emerging artificial intelligence technologies can increase efficiency and accuracy of assessing the baseline endoscopic appearance in patients with inflammatory bowel disease and the impact that therapeutic interventions may have on mucosal healing in both of these contexts. In this review, state-of-the-art endoscopic assessment of mucosal disease activity in inflammatory bowel disease clinical trials is described, covering the potential for artificial intelligence to transform the current paradigm, its limitations, and suggested next steps. Site-based artificial intelligence quality evaluation and inclusion of patients in clinical trials without the need for a central reader is proposed; for following patient progress, a second reading using AI alongside a central reader with expedited reading is proposed. Artificial intelligence will support precision endoscopy in inflammatory bowel disease and is on the threshold of advancing inflammatory bowel disease clinical trial recruitment.

Computer vision-based solutions to overcome the limitations of wireless capsule endoscopy

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

Application of capsule endoscopy in patients with chronic and recurrent abdominal pain: Abbreviated running title: capsule endoscopy in abdominal pain

OBJECTIVE

The incidence of chronic and recurrent abdominal pain increases every year, while the diagnosis is still unsatisfactory even after a number of check-ups. This study aimed to evaluate the diagnosis value of capsule endoscopy in patients suffering from chronic and recurrent abdominal pain.

METHODS

A retrospective case study was performed in 80 chronic and recurrent abdominal pain patients at Xiangyang Central Hospital from January 2013 to November 2017. Meanwhile, diagnoses by capsule endoscopy were collected for analysis.

RESULTS

Abnormal findings were found in 54 of 80 (67.5%) patients. The findings in chronic and recurrent abdominal pain patients include small intestinal erosion and congestion, small intestinal ulcers, small intestinal parasites, small intestinal vascular malformations, small intestinal polyps, small intestinal diverticulum, and small intestinal lymphangiectasia. There were no immediate significant side effects without being reported up to 1 month after ingestion of the capsule. The capsule was evacuated by all patients.

CONCLUSIONS

Capsule endoscopy has a great value in the diagnosis of chronic and recurrent abdominal pain with satisfactory safety and less pain for patients. Inflammatory lesions and ulcers in the small intestine account for the majority of positive findings in these patients.

The scientific progress and prospects of artificial intelligence in digestive endoscopy: A comprehensive bibliometric analysis

Artificial intelligence (AI) has been used for diagnosis and outcome prediction in clinical practice. Furthermore, AI in digestive endoscopy has attracted much attention and shown promising and stimulating results. This study aimed to determine the development trends and research hotspots of AI in digestive endoscopy by visualizing articles. Publications on AI in digestive endoscopy research were retrieved from the Web of Science Core Collection on April 25, 2022. VOSviewer and CiteSpace were used to assess and plot the research outputs. This analytical research was based on original articles and reviews. A total of 524 records of AI research in digestive endoscopy, published between 2005 and 2022, were retrieved. The number of articles has increased 27-fold from 2017 to 2021. Fifty-one countries and 994 institutions contributed to all publications. Asian countries had the highest number of publications. China, the USA, and Japan were consistently the leading driving forces and mainly contributed (26%, 21%, and 14.31%, respectively). With a solid academic reputation in this area, Japan has the highest number of citations per article. Tada Tomohiro published the most articles and received the most citations.. Gastrointestinal endoscopy published the largest number of publications, and 4 of the top 10 cited papers were published in this journal. "The Classification," "ulcerative colitis," "capsule endoscopy," "polyp detection," and "early gastric cancer" were the leading research hotspots. Our study provides systematic elaboration for researchers to better understand the development of AI in gastrointestinal endoscopy.

The optimal use of colon capsule endoscopes in clinical practice

Colon capsule endoscopy (CCE) has been available for nearly two decades but has grappled with being an equal diagnostic alternative to optical colonoscopy (OC). Due to the COVID-19 pandemic, CCE has gained more foothold in clinical practice. In this cutting-edge review, we aim to present the existing knowledge on the pros and cons of CCE and discuss whether the modality is ready for a larger roll-out in clinical settings. We have included clinical trials and reviews with the most significant impact on the current position of CCE in clinical practice and discuss the challenges that persist and how they could be addressed to make CCE a more sustainable imaging modality with an adenoma detection rate equal to OC and a low re-investigation rate by a proper preselection of suitable populations. CCE is embedded with a very low risk of severe complications and can be performed in the patient's home as a pain-free procedure. The diagnostic accuracy is found to be equal to OC. However, a significant drawback is low completion rates eliciting a high re-investigation rate. Furthermore, the bowel preparation before CCE is extensive due to the high demand for clean mucosa. CCE is currently not suitable for large-scale implementation in clinical practice mainly due to high re-investigation rates. By a better preselection before CCE and the implantation of artificial intelligence for picture and video analysis, CCE could be the alternative to OC needed to move away from in-hospital services and relieve long-waiting lists for OC.

Key research questions for implementation of artificial intelligence in capsule endoscopy

BACKGROUND

Artificial intelligence (AI) is rapidly infiltrating multiple areas in medicine, with gastrointestinal endoscopy paving the way in both research and clinical applications. Multiple challenges associated with the incorporation of AI in endoscopy are being addressed in recent consensus documents.

OBJECTIVES

In the current paper, we aimed to map future challenges and areas of research for the incorporation of AI in capsule endoscopy (CE) practice.

DESIGN

Modified three-round Delphi consensus online survey.

METHODS

The study design was based on a modified three-round Delphi consensus online survey distributed to a group of CE and AI experts. Round one aimed to map out key research statements and challenges for the implementation of AI in CE. All queries addressing the same questions were merged into a single issue. The second round aimed to rank all generated questions during round one and to identify the top-ranked statements with the highest total score. Finally, the third round aimed to redistribute and rescore the top-ranked statements.

RESULTS

Twenty-one (16 gastroenterologists and 5 data scientists) experts participated in the survey. In the first round, 48 statements divided into seven themes were generated. After scoring all statements and rescoring the top 12, the question of AI use for identification and grading of small bowel pathologies was scored the highest (mean score 9.15), correlation of AI and human expert reading-second (9.05), and real-life feasibility-third (9.0).

CONCLUSION

In summary, our current study points out a roadmap for future challenges and research areas on our way to fully incorporating AI in CE reading.

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%.

Artificial intelligence in gastroenterology: A state-of-the-art review

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.

Follow-up outcomes in patients with negative initial colon capsule endoscopy findings

BACKGROUND

Colon capsule endoscopy (CCE), which became clinically applicable in 2006, is a simple and noninvasive procedure to evaluate colonic diseases; the accuracy of second-generation CCE, introduced in 2009, has dramatically improved. Currently, CCE is used as an alternative method for colorectal cancer screening, as well as for evaluating the mucosal lesions of inflammatory bowel disease, in cases where performing colonoscopy (CS) is difficult. However, the outcomes of CCE are uncertain.

AIM

To investigate the outcomes of Japanese patients with negative findings (no polyps or colorectal cancer) on initial CCE.

METHODS

This retrospective, single-center study was conducted at the Endoscopic Center at Aishinkai Nakae Hospital. This study included patients who underwent continuous CCE between November 2013 and August 2019, that exhibited no evidence of polyps or colorectal cancer at the initial CCE, and could be followed up using either the fecal immunochemical test (FIT), CS, or CCE. The observational period, follow-up method, presence or absence of polyps and colorectal cancer, pathological diagnosis, and number of colorectal cancer deaths were evaluated.

RESULTS

Thirty-one patients (mean age, 60.4 ± 15.6 years; range, 28–84 years; 14 men and 17 women) were enrolled in this study. The reasons for performing the first CCE were screening in 12, a positive FIT in six, lower abdominal pain in nine, diarrhea in two, and anemia in two patients. The mean total water volume at the time of examination was 3460 ± 602 mL (2250–4800 mL), and a total CS was performed in 28 patients (90%). The degree of cleanliness was excellent in 15 patients and good in 16, and no poor cases were observed. No adverse events, such as retention or capsule aspiration, were observed in any of the patients. The mean follow-up period was 3.1 ± 1.5 years (range, 0.3–5.5 years). Follow-up included FIT in nine, CS in 20, and CCE in four patients (including duplicate patients). The FIT was positive in two patients, while CS revealed five polyp lesions (three in the ascending colon, one in the transverse colon, and one in the descending colon), with sizes ranging between 2 mm and 8 mm. Histopathological findings revealed a hyperplastic polyp in one patient, and adenoma with low grade dysplasia in four patients; colorectal cancers were not recognized. In the follow-up example by CCE, polyps and colorectal cancer could not be recognized. During the follow-up period, there were no deaths due to colorectal cancer in any of the patients.

CONCLUSION

We determined the outcomes in patients with negative initial CCE findings.

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.

Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease

Assessment of endoscopic disease activity can be difficult in patients with inflammatory bowel disease (IBD) [comprises Crohn's disease (CD) and ulcerative colitis (UC)]. Endoscopic assessment is currently the foundation of disease evaluation and the grading is pivotal for the initiation of certain treatments. Yet, disharmony is found among experts; even when reassessed by the same expert. Some studies have demonstrated that the evaluation is no better than flipping a coin. In UC, the greatest achieved consensus between physicians when assessing endoscopic disease activity only reached a Kappa value of 0.77 (or 77% agreement adjustment for chance/accident). This is unsatisfactory when dealing with patients at risk of surgery or disease progression without proper care. Lately, across all medical specialities, computer assistance has become increasingly interesting. Especially after the emanation of machine learning – colloquially referred to as artificial intelligence (AI). Compared to other data analysis methods, the strengths of AI lie in its capability to derive complex models from a relatively small dataset and its ability to learn and optimise its predictions from new inputs. It is therefore evident that with such a model, one hopes to be able to remove inconsistency among humans and standardise the results across educational levels, nationalities and resources. This has manifested in a handful of studies where AI is mainly applied to capsule endoscopy in CD and colonoscopy in UC. However, due to its recent place in IBD, there is a great inconsistency between the results, as well as the reporting of the same. In this opinion review, we will explore and evaluate the method and results of the published studies utilising AI within IBD (with examples), and discuss the future possibilities AI can offer within IBD.

Role of Artificial Intelligence in Video Capsule Endoscopy

Capsule endoscopy (CE) has been increasingly utilised in recent years as a minimally invasive tool to investigate the whole gastrointestinal (GI) tract and a range of capsules are currently available for evaluation of upper GI, small bowel, and lower GI pathology. Although CE is undoubtedly an invaluable test for the investigation of small bowel pathology, it presents considerable challenges and limitations, such as long and laborious reading times, risk of missing lesions, lack of bowel cleansing score and lack of locomotion. Artificial intelligence (AI) seems to be a promising tool that may help improve the performance metrics of CE, and consequently translate to better patient care. In the last decade, significant progress has been made to apply AI in the field of endoscopy, including CE. Although it is certain that AI will find soon its place in day-to-day endoscopy clinical practice, there are still some open questions and barriers limiting its widespread application. In this review, we provide some general information about AI, and outline recent advances in AI and CE, issues around implementation of AI in medical practice and potential future applications of AI-aided CE.

Artificial intelligence in gastrointestinal diseases

Artificial intelligence (AI) applications are growing in medicine. It is important to understand the current state of the AI applications prior to utilizing in disease research and treatment. In this review, AI application in the diagnosis and treatment of gastrointestinal diseases are studied and summarized. In most cases, AI studies had large amounts of data, including images, to learn to distinguish disease characteristics according to a human’s perspectives. The detailed pros and cons of utilizing AI approaches should be investigated in advance to ensure the safe application of AI in medicine. Evidence suggests that the collaborative usage of AI in both diagnosis and treatment of diseases will increase the precision and effectiveness of medicine. Recent progress in genome technology such as genome editing provides a specific example where AI has revealed the diagnostic and therapeutic possibilities of RNA detection and targeting.

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.

How should we do colon capsule endoscopy reading: a practical guide

In this article, we aim to provide general principles as well as personal views for colonic capsule endoscopy. To allow an in-depth understanding of the recommendations, we also present basic technological characteristics and specifications, with emphasis on the current as well as the previous version of colonic capsule endoscopy and relevant software. To date, there is no scientific proof to support the optimal way of reading a colonic capsule endoscopy video, or any standards or guidelines exist. Hence, any advice is a mixture of recommendations by the capsule manufacturer and experts’ opinion. Furthermore, there is a paucity of data regarding the use of term(s) (pre-reader/reader-validator) in colonic capsule endoscopy. We also include a couple of handy tables in order to get info at a glance.

Artificial intelligence for cancer detection of the upper gastrointestinal tract

In recent years, artificial intelligence (AI) has been found to be useful to physicians in the field of image recognition due to three elements: deep learning (that is, CNN, convolutional neural network), a high-performance computer, and a large amount of digitized data. In the field of gastrointestinal endoscopy, Japanese endoscopists have produced the world's first achievements of CNN-based AI system for detecting gastric and esophageal cancers. This study reviews papers on CNN-based AI for gastrointestinal cancers, and discusses the future of this technology in clinical practice. Employing AI-based endoscopes would enable early cancer detection. The better diagnostic abilities of AI technology may be beneficial in early gastrointestinal cancers in which endoscopists have variable diagnostic abilities and accuracy. AI coupled with the expertise of endoscopists would increase the accuracy of endoscopic diagnosis.

WCE polyp detection with triplet based embeddings

Wireless capsule endoscopy is a medical procedure used to visualize the entire gastrointestinal tract and to diagnose intestinal conditions, such as polyps or bleeding. Current analyses are performed by manually inspecting nearly each one of the frames of the video, a tedious and error-prone task. Automatic image analysis methods can be used to reduce the time needed for physicians to evaluate a capsule endoscopy video. However these methods are still in a research phase. In this paper we focus on computer-aided polyp detection in capsule endoscopy images. This is a challenging problem because of the diversity of polyp appearance, the imbalanced dataset structure and the scarcity of data. We have developed a new polyp computer-aided decision system that combines a deep convolutional neural network and metric learning. The key point of the method is the use of the Triplet Loss function with the aim of improving feature extraction from the images when having small dataset. The Triplet Loss function allows to train robust detectors by forcing images from the same category to be represented by similar embedding vectors while ensuring that images from different categories are represented by dissimilar vectors. Empirical results show a meaningful increase of AUC values compared to state-of-the-art methods. A good performance is not the only requirement when considering the adoption of this technology to clinical practice. Trust and explainability of decisions are as important as performance. With this purpose, we also provide a method to generate visual explanations of the outcome of our polyp detector. These explanations can be used to build a physician's trust in the system and also to convey information about the inner working of the method to the designer for debugging purposes.