Evaluation of performance of the Omni mode for detecting video capsule endoscopy images: A multicenter randomized controlled trial

Quality indicators for capsule endoscopy and deep enteroscopy

BACKGROUND AND AIMS

Capsule endoscopy (CE) and deep enteroscopy (DE) can be useful for diagnosing and treating suspected small-bowel disease. Guidelines and detailed recommendations exist for the use of CE/DE, but comprehensive quality indicators are lacking. The goal of this task force was to develop quality indicators for appropriate use of CE/DE by using a modified RAND/UCLA Appropriateness Method.

METHODS

An expert panel of 7 gastroenterologists with diverse practice experience was assembled to identify quality indicators. A literature review was conducted to develop a list of proposed quality indicators applicable to preprocedure, intraprocedure, and postprocedure periods. The panelists reviewed the literature; identified and modified proposed quality indicators; rated them on the basis of scientific evidence, validity, and necessity; and determined proposed performance targets. Agreement and consensus with the proposed indicators were verified using the RAND/UCLA Appropriateness Method.

RESULTS

The voting procedure to prioritize metrics emphasized selecting measures to improve quality and overall patient care. Panelists rated indicators on the perceived appropriateness and necessity for clinical practice. After voting and discussion, 2 quality indicators ranked as inappropriate or uncertain were excluded. Each quality indicator was categorized by measure type, performance target, and summary of evidence. The task force identified 13 quality indicators for CE and DE.

CONCLUSIONS

Comprehensive quality indicators have not existed for CE or DE. The task force identified quality indicators that can be incorporated into clinical practice. The panel also addressed existing knowledge gaps and posed research questions to better inform future research and quality guidelines for these procedures.

Quality Indicators for Capsule Endoscopy and Deep Enteroscopy

INTRODUCTION

Capsule endoscopy (CE) and deep enteroscopy (DE) can be useful for diagnosing and treating suspected small-bowel disease. Guidelines and detailed recommendations exist for the use of CE/DE, but comprehensive quality indicators are lacking. The goal of this task force was to develop quality indicators for appropriate use of CE/DE by using a modified RAND/UCLA Appropriateness Method.

METHODS

An expert panel of 7 gastroenterologists with diverse practice experience was assembled to identify quality indicators. A literature review was conducted to develop a list of proposed quality indicators applicable to preprocedure, intraprocedure, and postprocedure periods. The panelists reviewed the literature; identified and modified proposed quality indicators; rated them on the basis of scientific evidence, validity, and necessity; and determined proposed performance targets. Agreement and consensus with the proposed indicators were verified using the RAND/UCLA Appropriateness Method.

RESULTS

The voting procedure to prioritize metrics emphasized selecting measures to improve quality and overall patient care. Panelists rated indicators on the perceived appropriateness and necessity for clinical practice. After voting and discussion, 2 quality indicators ranked as inappropriate or uncertain were excluded. Each quality indicator was categorized by measure type, performance target, and summary of evidence. The task force identified 13 quality indicators for CE and DE.

DISCUSSION

Comprehensive quality indicators have not existed for CE or DE. The task force identified quality indicators that can be incorporated into clinical practice. The panel also addressed existing knowledge gaps and posed research questions to better inform future research and quality guidelines for these procedures.

New Generation Express View: An Artificial Intelligence Software Effectively Reduces Capsule Endoscopy Reading Times

BACKGROUND: Reading capsule endoscopy (CE) is time-consuming. The Express View (EV) (IntroMedic, Seoul, Korea) software was designed to shorten CE video reading. Our primary aim was to evaluate the diagnostic accuracy of EV in detecting significant small-bowel (SB) lesions. We also compared the reading times with EV mode and standard reading (SR). METHODS: 126 patients with suspected SB bleeding and/or suspected neoplasia were prospectively enrolled and underwent SB CE (MiroCam®1200, IntroMedic, Seoul, Korea). CE evaluation was performed in standard and EV mode. In case of discrepancies between SR and EV readings, a consensus was reached after reviewing the video segments and the findings were re-classified. RESULTS: The completion rate of SB CE in our cohort was 86.5% and no retention occurred. The per-patient analysis of sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of EV compared to SR were 86%, 86%, 90%, 81%, and 86%, respectively, before consensus. After consensus, they increased to 97%, 100%, 100%, 96%, and 98%, respectively. The median reading time with SR and EV was 71 min (range 26−340) and 13 min (range 3−85), respectively (p < 0.001). CONCLUSIONS: The new-generation EV shows high diagnostic accuracy and significantly reduces CE reading times.

Development of a Deep-Learning Algorithm for Small Bowel-Lesion Detection and a Study of the Improvement in the False-Positive Rate

Deep learning has recently been gaining attention as a promising technology to improve the identification of lesions, and deep-learning algorithms for lesion detection have been actively developed in small-bowel capsule endoscopy (SBCE). We developed a detection algorithm for abnormal findings by deep learning (convolutional neural network) the SBCE imaging data of 30 cases with abnormal findings. To enable the detection of a wide variety of abnormal findings, the training data were balanced to include all major findings identified in SBCE (bleeding, angiodysplasia, ulceration, and neoplastic lesions). To reduce the false-positive rate, "findings that may be responsible for hemorrhage" and "findings that may require therapeutic intervention" were extracted from the images of abnormal findings and added to the training dataset. For the performance evaluation, the sensitivity and the specificity were calculated using 271 detectable findings in 35 cases. The sensitivity was calculated using 68,494 images of non-abnormal findings. The sensitivity and specificity were 93.4% and 97.8%, respectively. The average number of images detected by the algorithm as having abnormal findings was 7514. We developed an image-reading support system using deep learning for SBCE and obtained a good detection performance.

Indications and Limitations Associated with the Patency Capsule Prior to Capsule Endoscopy

The retention of the capsule used during small bowel capsule endoscopy (SBCE) is a serious complication that can occur in patients with known or suspected small bowel stenosis, and a prior evaluation of the patency of the gastrointestinal (GI) tract is therefore essential. Patency capsule (PC) is a non-diagnostic capsule the same size as the diagnostic SBCE. To date, there are no clear guidelines regarding the contraindications for undergoing a PC evaluation prior to SBCE. Each small bowel disorder has specific occasions to inhibit the progress of PC and SBCE, even though they do not have any stenotic symptoms or abnormalities on imaging. In this review, we summarize the indications and limitations of PC prior to SBCE, especially the contraindications, and discuss clinical scenarios in which even PC should be avoided, and therefore such areas of stenosis should be evaluated by alternative modalities. We thus propose this new algorithm to evaluate the patency of the GI tract for patients with suspected and known small bowel stenosis in order that they may undergo SBCE safely.

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.

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.

Video capsule endoscopy: pushing the boundaries with software technology

Video capsule endoscopy (VCE) has transformed imaging of the small bowel as it is a non-invasive and well tolerated modality with excellent diagnostic capabilities. The way we read VCE has not changed much since its introduction nearly two decades ago. Reading is still very time intensive and prone to reader error. This review outlines the evidence regarding software enhancements which aim to address these challenges. These include the suspected blood indicator (SBI), automated fast viewing modes including QuickView, lesion characterization tools such Fuji Intelligent Color Enhancement, and three-dimensional (3D) representation tools. We also outline the exciting new evidence of artificial intelligence (AI) and deep learning (DL), which promises to revolutionize capsule reading. DL algorithms have been developed for identifying organs of origin, intestinal motility events, active bleeding, coeliac disease, polyp detection, hookworms and angioectasias, all with impressively high sensitivity and accuracy. More recently, an algorithm has been created to detect multiple abnormalities with a sensitivity of 99.9% and reading time of only 5.9 minutes. These algorithms will need to be validated robustly. However, it will not be long before we see this in clinical practice, aiding the clinician in rapid and accurate diagnosis.

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.

Use of rapid reading software to reduce capsule endoscopy reading times while maintaining accuracy

BACKGROUNDS AND AIMS

A typical capsule endoscopy (CE) case generates tens of thousands of images, with abnormalities often confined to a just few frames. Omni Mode is a novel EndoCapsule software algorithm (Olympus, Tokyo, Japan) that proposes to intelligently remove duplicate images while maintaining accuracy in lesion detection.

METHODS

This prospective multicenter study took place across 9 European centers. Consecutive, unselected CE cases were read conventionally in normal mode, with every captured frame reviewed. Cases were subsequently anonymized and randomly allocated to another center where they were read using Omni Mode. Detected lesions and reading times were recorded, with findings compared between both viewing modes. The clinical significance of lesions was described according to the P classification (P0, P1, and P2). Where a discrepancy in lesion detection in either mode was found, expert blinded review at a consensus meeting was undertaken.

RESULTS

The patient population undergoing CE had a mean age of 49.5 years (range, 18-91), with the investigation of anemia or GI bleeding accounting for 71.8% of cases. The average small-bowel transit time was 4 hours, 26 minutes. The mean reading time in normal mode was 42.5 minutes. The use of Omni Mode was significantly faster (P < .0001), with an average time saving of 24.6 minutes (95% confidence interval, 22.8-26.9). The 2127 lesions were identified and classified according to the P classification as P0 (1234), P1 (656), and P2 (237). Lesions were identified using both reading modes in 40% (n = 936), and 1186 lesions were identified by either normal or Omni Mode alone. Normal mode interpretation was associated with 647 lesions being missed, giving an accuracy of .70. Omni Mode interpretation led to 539 lesions being missed, with an accuracy of .75. There was no significant difference in clinical conclusions made between either reading mode.

CONCLUSIONS

This study shows that CE reading times can be reduced by an average of 40%, without any reduction in clinical accuracy.

Evaluation of the sensitivity of the Express View function in the Mirocam® capsule endoscopy software

Background: A new computer algorithm called Express-View has recently been introduced by Mirocam, but data concerning its application and efficacy are scarce.Objective: To evaluate the lesion detection rate, per-patient sensitivity and the diagnostic accuracy using Express-View.Methods: All patients who performed CE between January 2018 and June 2019, whose indication was obscure gastrointestinal bleeding (OGIB) and with findings on CE, were included. Lesions identified in conventional reading were selected and considered as reference.Results: Eighty-nine patients were included, 50.6% male, with a mean age of 68.4 years-old (±12.3). The Express-View mode detected 85.5% of lesions previously detected by conventional reading (524 out of 613). There were 89 missed lesions, mainly erosions or ulcers (44.9%) and angioectasias (38.2%). The lesion detection rate was found to be lower in the jejunum and ileum compared to extra-small bowel locations and duodenum (p = .04). Although Express-View had a per-patient sensitivity for all lesions of 56.2% and a per-patient sensitivity for all clinically significant lesions of 83.1%, it achieved a diagnostic accuracy of 91%.Conclusions: The per-patient sensitivity for all lesions was shown to be below expectations, although the lesion detection rate, the per-patient sensitivity for all clinically significant lesions, and the diagnostic accuracy were shown to be higher.

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.

Follow-up on: optimizing lesion detection in small bowel capsule endoscopy and beyond: from present problems to future solutions

This review presents noteworthy advances in clinical and experimental Capsule Endoscopy (CE), focusing on the progress that has been reported over the last 5 years since our previous review on the subject. Areas covered: This study presents the commercially available CE platforms, as well as the advances made in optimizing the diagnostic capabilities of CE. The latter includes recent concept and prototype capsule endoscopes, medical approaches to improve diagnostic yield, and progress in software for enhancing visualization, abnormality detection, and lesion localization. Expert commentary: Currently, moving through the second decade of CE evolution, there are still several open issues and remarkable challenges to overcome.

Optimising the performance and interpretation of small bowel capsule endoscopy

Small bowel capsule endoscopy has become a commonly used tool in the investigation of gastrointestinal symptoms and is now widely available in clinical practice. In contrast to conventional endoscopy, there is a lack of clear consensus on when competency is achieved or the way in which capsule endoscopy should be performed in order to maintain quality and clinical accuracy. Here we explore the evidence on the key factors that influence the quality of small bowel capsule endoscopy services.

A global perspective on capsule endoscopy

A search of the internet today to quantify the estimated value of capsules from a global perspective, easily delivers figures stating around $200 million in 2014 to about $400 million by 2020, which would be approximately 10% of the gastrointestinal endoscopic market. Is this a steep rise within just six years or could the capsule market do even better? What chances does this offer and what are the key aspects for future success? By 2020, more than 1 billion people are aged sixty or older and around one third of them will live in what the UN calls "more developed regions". Naturally, this brings an increased demand for colorectal cancer screening and surgery. But keeping in mind that basically every healthcare system, in any country, is already operating at its limits, how do we secure future treatment for a growing community? Surely more competition will steadily bring down prices for capsules. However, that does not ease the amount of time that is spent to properly read any video and issue a valid diagnosis for every patient. This article intends to give an overview about the current global market for capsule endoscopy (CE) with a perspective on typical patients, their indications, and how the capsules are used and by whom. Further aspects, such as standardization of training, reading and future trends will also be elaborated on.

An update on small bowel endoscopy

PURPOSE OF REVIEW

The breakthrough success of capsule endoscopy and device-assisted enteroscopy has inspired researchers to test and push the boundary of these technologies. The authors herein summarize the latest and most significant studies with clinical impact.

RECENT FINDINGS

Competing capsule endoscopy models have enriched the platform of this wireless device. The role of capsule endoscopy in Crohn's disease is expanding as we learn more of the significance of disease distribution and response to treatment. The benefit of capsule endoscopy in abdominal pain has previously been sceptical, but may have a role. Device-assisted enteroscopy demonstrates significant benefit in the management of patients with Crohn's disease and Peutz-Jeghers syndrome. On the contrary, long-term data suggest that endotherapy to small bowel angioectasia may not be as beneficial to patients as we once thought. The role of device-assisted enteroscopy in novel territory, including coeliac disease and endoscopic retrograde cholangiopancreatography, continues to be tested.

SUMMARY

The limit of capsule endoscopy and enteroscopy is yet to be reached. Accumulating long-term data alludes to the benefits of our current practice while spawning novel indications for small bowel endoscopy.