Colon Capsule Endoscopy

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Optical Diagnostics".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 2556

Special Issue Editors


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Guest Editor
1. University Hospital of Coventry and Warwickshire, Coventry CV2 2DX, UK
2. Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
3. Health, Biological & Experimental Sciences, University of Coventry, Coventry CV1 5FB, UK
4. School of Health Sciences, University of Leicester, Leicester LE1 7RH, UK
Interests: colon cancer biomarker; volatile organic compounds; bile acid pathophysiology and artificial intelligence
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Special Issue Information

Dear Colleagues, 

Colon capsule endoscopy, which, until recently, was regarded as the 'neglected relative' of the wireless capsule endoscopy family, has attracted revived interest in the wake of the COVID-19 pandemic and the spiraling effect of artificial intelligence. This does not mean, however, that the old problems tantalizing the modality have disappeared. The search for the 'ideal' prep and patient group, appealing to clinical researchers, manufacturers, and the general public alike, is still under heated debate. Furthermore, the emergence of new double-headed capsule models and the accumulation of real-world data from the UK promises to attract additional interest in this field.

We invite you to share your work and ideas in the form of narrative or systematic reviews, interesting images, and clinical or developmental studies in this Special Issue on colon and panenteric capsule endoscopy.

Prof. Dr. Anastasios Koulaouzidis
Prof. Dr. Ramesh P. Arasaradnam
Guest Editors

Manuscript Submission Information

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Published Papers (1 paper)

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Review

18 pages, 3181 KiB  
Review
Clinicians’ Guide to Artificial Intelligence in Colon Capsule Endoscopy—Technology Made Simple
by Ian I. Lei, Gohar J. Nia, Elizabeth White, Hagen Wenzek, Santi Segui, Angus J. M. Watson, Anastasios Koulaouzidis and Ramesh P. Arasaradnam
Diagnostics 2023, 13(6), 1038; https://doi.org/10.3390/diagnostics13061038 - 08 Mar 2023
Cited by 3 | Viewed by 1917
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Colon Capsule Endoscopy)
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