Novel Clinical Applications and Technical Developments in Video Capsule Endoscopy

Toward automated small bowel capsule endoscopy reporting using a summarizing machine learning algorithm: The SUM UP study

BACKGROUND AND OBJECTIVES

Deep learning (DL) algorithms demonstrate excellent diagnostic performance for the detection of vascular lesions via small bowel (SB) capsule endoscopy (CE), including vascular abnormalities with high (P2), intermediate (P1) or low (P0) bleeding potential, while dramatically decreasing the reading time. We aimed to improve the performance of a DL algorithm by characterizing vascular abnormalities using a machine learning (ML) classifier, and selecting the most relevant images for insertion into reports.

MATERIALS AND METHODS

A training dataset of 75 SB CE videos was created, containing 401 sequences of interest that encompassed 1,525 images of various vascular lesions. Several image classification algorithms were tested, to discriminate "typical angiodysplasia" (P2/P1) and "other vascular lesion" (P0) and to select the most relevant image within sequences with repetitive images. The performances of the best-fitting algorithms were subsequently assessed on an independent test dataset of 73 full-length SB CE video recordings.

RESULTS

Following DL detection, a random forest (RF) method demonstrated a specificity of 91.1 %, an area under the receiving operating characteristic curve of 0.873, and an accuracy of 84.2 % for discriminating P2/P1 from P0 lesions while allowing an 83.2 % reduction in the number of reported images. In the independent testing database, after RF was applied, the output number decreased by 91.6 %, from 216 (IQR 108-432) to 12 (IQR 5-33). The RF algorithm achieved 98.0 % agreement with initial, conventional (human) reporting. Following DL detection, the RF method allowed better characterization and accurate selection of images of relevant (P2/P1) SB vascular abnormalities for CE reporting without impairing diagnostic accuracy. These findings pave the way for automated SB CE reporting.

Capsule robots for the monitoring, diagnosis, and treatment of intestinal diseases

Current evidence suggests that the intestine as the new frontier for human health directly impacts both our physical and mental health. Therefore, it is highly desirable to develop the intelligent tool for the enhanced diagnosis and treatment of intestinal diseases. During the past 20 years, capsule robots have opened new avenues for research and clinical applications, potentially revolutionizing human health monitor, disease diagnosis and treatment. In this review, we summarize the research progress of edible multifunctional capsule robots in intestinal diseases. To begin, we introduce the correlation between the intestinal microbiome, intestinal gas and human diseases. After that, we focus on the technical structure of edible multifunctional robots. Subsequently, the biomedical applications in the monitoring, diagnosis and treatment of intestinal diseases are discussed in detail. Last but not least, the main challenges of multifunctional capsule robots during the development process are summarized, followed by a vision for future development opportunities.

Endoscopic capsule robot-based diagnosis, navigation and localization in the gastrointestinal tract

The proliferation of video capsule endoscopy (VCE) would not have been possible without continued technological improvements in imaging and locomotion. Advancements in imaging include both software and hardware improvements but perhaps the greatest software advancement in imaging comes in the form of artificial intelligence (AI). Current research into AI in VCE includes the diagnosis of tumors, gastrointestinal bleeding, Crohn’s disease, and celiac disease. Other advancements have focused on the improvement of both camera technologies and alternative forms of imaging. Comparatively, advancements in locomotion have just started to approach clinical use and include onboard controlled locomotion, which involves miniaturizing a motor to incorporate into the video capsule, and externally controlled locomotion, which involves using an outside power source to maneuver the capsule itself. Advancements in locomotion hold promise to remove one of the major disadvantages of VCE, namely, its inability to obtain targeted diagnoses. Active capsule control could in turn unlock additional diagnostic and therapeutic potential, such as the ability to obtain targeted tissue biopsies or drug delivery. With both advancements in imaging and locomotion has come a corresponding need to be better able to process generated images and localize the capsule’s position within the gastrointestinal tract. Technological advancements in computation performance have led to improvements in image compression and transfer, as well as advancements in sensor detection and alternative methods of capsule localization. Together, these advancements have led to the expansion of VCE across a number of indications, including the evaluation of esophageal and colon pathologies including esophagitis, esophageal varices, Crohn’s disease, and polyps after incomplete colonoscopy. Current research has also suggested a role for VCE in acute gastrointestinal bleeding throughout the gastrointestinal tract, as well as in urgent settings such as the emergency department, and in resource-constrained settings, such as during the COVID-19 pandemic. VCE has solidified its role in the evaluation of small bowel bleeding and earned an important place in the practicing gastroenterologist’s armamentarium. In the next few decades, further improvements in imaging and locomotion promise to open up even more clinical roles for the video capsule as a tool for non-invasive diagnosis of lumenal gastrointestinal pathologies.