de Groof, Jeroen;
van der Sommen, Fons;
van der Putten, Joost;
Struyvenberg, Maarten R;
Zinger, Sveta;
Curvers, Wouter L;
Pech, Oliver;
Meining, Alexander;
Neuhaus, Horst;
Bisschops, Raf;
Schoon, Erik J;
de With, Peter H;
Bergman, Jacques J
The Argos project: The development of a computer‐aided detection system to improve detection of Barrett's neoplasia on white light endoscopy
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Media type:
E-Article
Title:
The Argos project: The development of a computer‐aided detection system to improve detection of Barrett's neoplasia on white light endoscopy
Contributor:
de Groof, Jeroen;
van der Sommen, Fons;
van der Putten, Joost;
Struyvenberg, Maarten R;
Zinger, Sveta;
Curvers, Wouter L;
Pech, Oliver;
Meining, Alexander;
Neuhaus, Horst;
Bisschops, Raf;
Schoon, Erik J;
de With, Peter H;
Bergman, Jacques J
Published:
Wiley, 2019
Published in:
United European Gastroenterology Journal, 7 (2019) 4, Seite 538-547
Language:
English
DOI:
10.1177/2050640619837443
ISSN:
2050-6406;
2050-6414
Origination:
Footnote:
Description:
BackgroundComputer‐aided detection (CAD) systems might assist endoscopists in the recognition of Barrett's neoplasia.AimTo develop a CAD system using endoscopic images of Barrett's neoplasia.MethodsWhite light endoscopy (WLE) overview images of 40 neoplastic Barrett's lesions and 20 non‐dysplastic Barret's oesophagus (NDBO) patients were prospectively collected. Experts delineated all neoplastic images.The overlap area of at least four delineations was labelled as the ‘sweet spot’. The area with at least one delineation was labelled as the ‘soft spot’. The CAD system was trained on colour and texture features. Positive features were taken from the sweet spot and negative features from NDBO images. Performance was evaluated using leave‐one‐out cross‐validation. Outcome parameters were diagnostic accuracy of the CAD system per image, and localization of the expert soft spot by CAD delineation (localization score) and its indication of preferred biopsy location (red‐flag indication score).ResultsAccuracy, sensitivity and specificity for detection were 92, 95 and 85%, respectively. The system localized and red‐flagged the soft spot in 100 and 90%, respectively.ConclusionThis uniquely trained and validated CAD system detected and localized early Barrett's neoplasia on WLE images with high accuracy. This is an important step towards real‐time automated detection of Barrett's neoplasia.