TY - JOUR
T1 - The Argos project
T2 - the development of a computer-aided detection system to improve detection of Barrett's neoplasia on white light endoscopy
AU - Groof, Jeroen de
AU - van der Sommen, Fons
AU - van der Putten, Joost
AU - Struyvenberg, Maarten R.
AU - Zinger, Sveta
AU - Curvers, Wouter L.
AU - Pech, Oliver
AU - Meining, Alexander
AU - Neuhaus, Horst
AU - Bisschops, Raf
AU - Schoon, Erik J.
AU - de With, Peter H.
AU - Bergman, Jacques J.
PY - 2019/5/1
Y1 - 2019/5/1
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - Barrett's neoplasia
KW - Barrett's oesophagus
KW - computer-aided detection
KW - endoscopy
UR - http://www.scopus.com/inward/record.url?scp=85062656145&partnerID=8YFLogxK
U2 - 10.1177/2050640619837443
DO - 10.1177/2050640619837443
M3 - Article
C2 - 31065371
AN - SCOPUS:85062656145
SN - 2050-6406
VL - 7
SP - 538
EP - 547
JO - United European Gastroenterology Journal
JF - United European Gastroenterology Journal
IS - 4
ER -