Optimizing lesion detection in small-bowel capsule endoscopy: from present problems to future solutions

Koulaouzidis, Anastasios; Iakovidis, Dimitris K.; Karargyris, Alexandros; Plevris, John

doi:10.1586/17474124.2014.952281

Cited by 59 publications

(39 citation statements)

References 155 publications

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“…However, this potential is limited by human perception capabilities. 77 Furthermore, the validity of some quick visualization approaches has been q uestioned, 78,79 as informative VCE frames could be skipped.…”

Section: Visualizationmentioning

confidence: 99%

Software for enhanced video capsule endoscopy: challenges for essential progress

Iakovidis

Koulaouzidis

2015

Nat Rev Gastroenterol Hepatol

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186

113

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Video capsule endoscopy (VCE) has revolutionized the diagnostic work-up in the field of small bowel diseases. Furthermore, VCE has the potential to become the leading screening technique for the entire gastrointestinal tract. Computational methods that can be implemented in software can enhance the diagnostic yield of VCE both in terms of efficiency and diagnostic accuracy. Since the appearance of the first capsule endoscope in clinical practice in 2001, information technology (IT) research groups have proposed a variety of such methods, including algorithms for detecting haemorrhage and lesions, reducing the reviewing time, localizing the capsule or lesion, assessing intestinal motility, enhancing the video quality and managing the data. Even though research is prolific (as measured by publication activity), the progress made during the past 5 years can only be considered as marginal with respect to clinically significant outcomes. One thing is clear-parallel pathways of medical and IT scientists exist, each publishing in their own area, but where do these research pathways meet? Could the proposed IT plans have any clinical effect and do clinicians really understand the limitations of VCE software? In this Review, we present an in-depth critical analysis that aims to inspire and align the agendas of the two scientific groups.

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Section: Visualizationmentioning

confidence: 99%

Software for enhanced video capsule endoscopy: challenges for essential progress

Iakovidis

Koulaouzidis

2015

Nat Rev Gastroenterol Hepatol

Self Cite

186

113

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“…Technical characteristics of these systems can be found elsewhere in the literature [19,20]. Bowel preparation, where used, was polyethylene glycol (PEG) 2 or 4 lt. Prokinetics, where used, was in the form of domperidone (5-10 mg orally) and/or metoclopramide (10 mg intramuscularly) [21].…”

Section: Patients and Ce Proceduresmentioning

confidence: 99%

Association Between Fecal Calprotectin Levels and Small-bowel Inflammation Score in Capsule Endoscopy: A Multicenter Retrospective Study

et al. 2016

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“…The author [11] has reviewed some current CAD methods employed in enhanced video capsule endoscopy. Various hardware and software problems have been highlighted in a review article [12] for detecting lesions in the small bowel.…”

Section: Background and Literaturementioning

confidence: 99%

Feature Selection and Classification of Ulcerated Lesions Using Statistical Analysis for WCE Images

et al. 2017

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Abstract:Wireless capsule endoscopy (WCE) is a technology developed to inspect the whole gastrointestinal tract (especially the small bowel area that is unreachable using the traditional endoscopy procedure) for various abnormalities in a non-invasive manner. However, visualization of a massive number of images is a very time-consuming and tedious task for physicians (prone to human error). Thus, an automatic scheme for lesion detection in WCE videos is a potential solution to alleviate this problem. In this work, a novel statistical approach was chosen for differentiating ulcer and non-ulcer pixels using various color spaces (or more specifically using relevant color bands). The chosen feature vector was used to compute the performance metrics using SVM with grid search method for maximum efficiency. The experimental results and analysis showed that the proposed algorithm was robust in detecting ulcers. The performance in terms of accuracy, sensitivity, and specificity are 97.89%, 96.22%, and 95.09%, respectively, which is promising.

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Optimizing lesion detection in small-bowel capsule endoscopy: from present problems to future solutions

Cited by 59 publications

References 155 publications

Software for enhanced video capsule endoscopy: challenges for essential progress

Software for enhanced video capsule endoscopy: challenges for essential progress

Association Between Fecal Calprotectin Levels and Small-bowel Inflammation Score in Capsule Endoscopy: A Multicenter Retrospective Study

Feature Selection and Classification of Ulcerated Lesions Using Statistical Analysis for WCE Images

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