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The Argos project: The development of a computer-aided detection system to improve detection of Barrett's neoplasia on white light endoscopy.
United European Gastroenterol J 2019; 7(4):538-547UE

Abstract

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.

Authors+Show Affiliations

Department of Gastroenterology and Hepatology, University of Amsterdam, Amsterdam, The Netherlands.Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.Department of Gastroenterology and Hepatology, University of Amsterdam, Amsterdam, The Netherlands.Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.Department of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven, The Netherlands.Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder, Regensburg, Germany.Center of Internal Medicine, Ulm University, Ulm, Germany.Internal Medicine, Evangelisches Krankenhaus Düsseldorf, Düsseldorf, Germany.Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium.Department of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven, The Netherlands.Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.Department of Gastroenterology and Hepatology, University of Amsterdam, Amsterdam, The Netherlands.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31065371

Citation

de Groof, Jeroen, et al. "The Argos Project: the Development of a Computer-aided Detection System to Improve Detection of Barrett's Neoplasia On White Light Endoscopy." United European Gastroenterology Journal, vol. 7, no. 4, 2019, pp. 538-547.
de Groof J, van der Sommen F, van der Putten J, et al. The Argos project: The development of a computer-aided detection system to improve detection of Barrett's neoplasia on white light endoscopy. United European Gastroenterol J. 2019;7(4):538-547.
de Groof, J., van der Sommen, F., van der Putten, J., Struyvenberg, M. R., Zinger, S., Curvers, W. L., ... Bergman, J. J. (2019). The Argos project: The development of a computer-aided detection system to improve detection of Barrett's neoplasia on white light endoscopy. United European Gastroenterology Journal, 7(4), pp. 538-547. doi:10.1177/2050640619837443.
de Groof J, et al. The Argos Project: the Development of a Computer-aided Detection System to Improve Detection of Barrett's Neoplasia On White Light Endoscopy. United European Gastroenterol J. 2019;7(4):538-547. PubMed PMID: 31065371.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - The Argos project: The development of a computer-aided detection system to improve detection of Barrett's neoplasia on white light endoscopy. AU - de Groof,Jeroen, 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, Y1 - 2019/03/06/ PY - 2018/12/19/received PY - 2019/02/02/accepted PY - 2019/5/9/entrez PY - 2019/5/9/pubmed PY - 2019/5/9/medline KW - Barrett's neoplasia KW - Barrett's oesophagus KW - artificial intelligence KW - computer-aided detection KW - endoscopy SP - 538 EP - 547 JF - United European gastroenterology journal JO - United European Gastroenterol J VL - 7 IS - 4 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. SN - 2050-6406 UR - https://www.unboundmedicine.com/medline/citation/31065371/The_Argos_project:_The_development_of_a_computer_aided_detection_system_to_improve_detection_of_Barrett's_neoplasia_on_white_light_endoscopy_ L2 - http://journals.sagepub.com/doi/full/10.1177/2050640619837443?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub=pubmed DB - PRIME DP - Unbound Medicine ER -