Open AccessThe Argos project: The development of a computer‐aided detection system to improve detection of Barrett's neoplasia on white light endoscopy
Author(s) -
Groof Jeroen,
Sommen Fons,
Putten Joost,
Struyvenberg Maarten R,
Zinger Sveta,
Curvers Wouter L,
Pech Oliver,
Meining Alexander,
Neuhaus Horst,
Bisschops Raf,
Schoon Erik J,
With Peter H,
Bergman Jacques J
Publication year - 2019
Publication title -
ueg journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.667
H-Index - 35
eISSN - 2050-6414
pISSN - 2050-6406
DOI - 10.1177/2050640619837443
Subject(s) - medicine , cad , biopsy , dysplasia , radiology , artificial intelligence , pathology , computer science , engineering drawing , engineering
Background Computer‐aided detection (CAD) systems might assist endoscopists in the recognition of Barrett's neoplasia. Aim To develop a CAD system using endoscopic images of Barrett's neoplasia. Methods White 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). Results Accuracy, 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. Conclusion This 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.