Abstract
Introduction
Colon capsule endoscopy (CCE) has evolved from its initial applications into a transformative diagnostic tool for colorectal pathology, bridging gaps in clinical capabilities with innovative imaging and sensor technologies. Despite challenges, this technology represents a cornerstone for future non-invasive diagnostics.
Recent technological advancements have introduced several new tools for investigating the colon. Capsule-based platforms have expanded clinical indications and improved diagnostic capabilities. Although these technologies’ full potential has yet to be realized, clinicians can now address previously unattainable challenges due to their application. It has been nearly 20 years since the introduction of the first CCE. 1 This special collection presents a compelling overview of how capsule-based platforms have revolutionized—and continue to do so—the field of colonic investigation.
Contributions to the collection
Each contribution to this Special Collection offers valuable insights into the development and challenges of capsule-based platforms. Cardoso et al. highlight the role of artificial intelligence (AI), emphasizing the push for automated diagnostic accuracy. In addition, Jalayeri Nia et al. discuss smart capsule technologies, illustrating how multisensory platforms could transform clinical diagnostics. Lastly, their detailed examination of the practical challenges associated with capsule endoscopy (CE) and potential improvements provides practical strategies for promoting broader use in routine practice.
In the first article, Jalayeri Nia et al. 2 explore the vast field of smart capsules that utilize other-than-light technologies to detect colonic pathology and monitor gut health. Integrating various sensors in capsule models has opened new diagnostic possibilities. 3 Sensing capsules can measure body temperature, pressure, pH, and intestinal gases. While some of these technologies are still in the early stages of development, and some have been discontinued, many of these platforms aim to provide additional information beyond what can be obtained through standard visual examination of the colon. However, the full development and integration of these tools into clinical practice is still to be determined.
From a clinical perspective, the possibilities offered by these devices are extremely appealing. Expanding our horizons beyond light-based technologies is just a glimpse of what may become “routine” in the future.
The second paper by Cardoso et al. 4 evaluates the reliability of a convolutional neural network (CNN)-based model to provide an automated scoring system for the evaluation of inflammatory activity in Crohn’s disease patients by the analysis of panenteric CE videos. The Lewis, CECDAI (capsule endoscopy Crohn’s disease activity index), and Eliakim scores are commonly used in reading stations’ software. Still, they require manual selection of important video frames, which is time-consuming. The AI system proposed by the authors automatically selects frames from the video and, after incorporating clinical data, provides an automated score for the small bowel and colon separately. This CNN-based system shows a strong correlation with the results obtained by the validated scores (Spearman’s r = 0.751 for Lewis, r = 0.707 for CECDAI, r = 0.655 for Eliakim), with statistical significance (p = 0.001 in all cases).
As AI systems are transforming the reading process in CE, this preliminary study provides significant evidence in this field. The need for standardization and uniformity in the CE staging and monitoring of Crohn’s disease, especially in an era of multiple new therapies, appears to be a low-hanging fruit for AI-driven systems. In terms of complexity, the automatization of disease scoring is a path worth pursuing to help both clinicians and patients obtain the desired outcomes.
In the third paper, Jalayeri Nia et al. 5 provide a comprehensive overview of CCE applications, discussing current challenges and potential areas for future development. Although the clinical efficacy of CCE is well established,6,7 several factors hinder this method’s broader applicability and adoption. These include the need for special bowel preparations, difficulties achieving satisfactory completion rates, and some resistance by clinicians in colonoscopy-centric environments. The authors suggest implementing several strategies to increase the widespread clinical adoption of this method, including better patient selection, reducing the need for re-investigation, and expanding the use of out-of-clinic environments.
The strength of this paper lies in its ability to clearly summarize the pitfalls of CCE, not only in terms of technological application but mainly regarding its environment. When evaluating or assessing the potential of technology, we as clinicians need to remember taking a step back is usually more helpful than closely examining it with a magnifying glass.
Conclusion
Targeted research efforts are needed to address reliability and standardization in clinical settings to ensure capsule-based technologies achieve their potential. Innovations in patient preparation, capsule design, interpretive software, and AI integration will be critical. The path ahead involves overcoming current barriers to bring these systems into widespread clinical use, ensuring accessibility and cost-effectiveness.
The papers in this Special Collection offer new insights into potential future uses of capsule-based platforms for examining the colon, going beyond the typical scope of CCE. These technological advancements have the potential to significantly reduce clinicians’ workloads and optimize available resources through non-invasive, patient-friendly methods. CCE was previously overlooked, but recent events have spotlighted it. CCE has been found to provide high diagnostic accuracy and could be a cost-efficient alternative to colonoscopy for specific patient groups. Ongoing research in AI and improvements in patient selection and pre-test algorithms show promise in advancing CCE’s effectiveness. 8 However, further improvements are needed to enhance the reliability of CCE outside of academic clinical trials. While the full potential of CCE has yet to be realized, continuous improvement of this technology is essential for it to become an integral part of future gastrointestinal diagnostic processes.
