Optimising the performance and interpretation of small bowel capsule endoscopy

Beg, Sabina; Parra‐Blanco, Adolfo; Ragunath, Krish

doi:10.1136/flgastro-2017-100878

Cited by 4 publications

(2 citation statements)

References 67 publications

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“…However, standards in capsule endoscopy reporting remain to be defined – relating both to the optimal methodology of reading the investigation as well as what level of quality control is required to monitor clinician competence. In this edition Beg and colleagues comprehensively review the evidence to address these issues 2. The paper spans the range of the topic from patient preparation to choice of equipment, reporting and maintenance of competence.…”

Section: Standardising Pouchoscopy and Capsule Endoscopymentioning

confidence: 99%

Excellence is a habit, not an act

Emmanuel¹

2018

Frontline Gastroenterol

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Section: Standardising Pouchoscopy and Capsule Endoscopymentioning

confidence: 99%

Excellence is a habit, not an act

Emmanuel¹

2018

Frontline Gastroenterol

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“…Variations on this allow greater reductions and although exclusive use of modes, such as QuickView (PillCam), similar picture elimination (OMOM) or Express or Omni modes (EndoCapsule), clearly cut the time taken for manual review of SB CE videos, this is at the sacrifice of sensitivity, with more than half of pathology being missed in some studies. 22 Although a helpful tool when used appropriately, reliance on these would be ill-advised.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for the detection of polyps or cancer with colon capsule endoscopy

Robertson

Seguí

Wenzek

et al. 2021

Clin Med Insights Gastroenterol

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Colorectal cancer is common and can be devastating, with long-term survival rates vastly improved by early diagnosis. Colon capsule endoscopy (CCE) is increasingly recognised as a reliable option for colonic surveillance, but widespread adoption has been slow for several reasons, including the time-consuming reading process of the CCE recording. Automated image recognition and artificial intelligence (AI) are appealing solutions in CCE. Through a review of the currently available and developmental technologies, we discuss how AI is poised to deliver at the forefront of CCE in the coming years. Current practice for CCE reporting often involves a two-step approach, with a ‘pre-reader’ and ‘validator’. This requires skilled and experienced readers with a significant time commitment. Therefore, CCE is well-positioned to reap the benefits of the ongoing digital innovation. This is likely to initially involve an automated AI check of finished CCE evaluations as a quality control measure. Once felt reliable, AI could be used in conjunction with a ‘pre-reader’, before adopting more of this role by sending provisional results and abnormal frames to the validator. With time, AI would be able to evaluate the findings more thoroughly and reduce the input required from human readers and ultimately autogenerate a highly accurate report and recommendation of therapy, if required, for any pathology identified. As with many medical fields reliant on image recognition, AI will be a welcome aid in CCE. Initially, this will be as an adjunct to ‘double-check’ that nothing has been missed, but with time will hopefully lead to a faster, more convenient diagnostic service for the screening population.

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