Endoscopy 2019; 51(04): S5-S6
DOI: 10.1055/s-0039-1681185
ESGE Days 2019 oral presentations
Friday, April 5, 2019 08:30 – 10:30: Artificial intelligence Club A
Georg Thieme Verlag KG Stuttgart · New York

COMPUTER-AIDED DIAGNOSIS OF GASTRIC LESIONS USING MAGNIFYING NARROW BAND IMAGING ENDOSCOPY

S Kashin
1   Yaroslavl Regional Cancer Hospital, Yaroslavl, Russian Federation
,
R Kuvaev
1   Yaroslavl Regional Cancer Hospital, Yaroslavl, Russian Federation
2   Pirogov Russian National Research Medical University, Moscow, Russian Federation
,
E Kraynova
1   Yaroslavl Regional Cancer Hospital, Yaroslavl, Russian Federation
,
H Edelsbrunner
3   Institute of Science and Technology Austria, Klosterneuburg, Austria
,
O Dunaeva
4   P.G. Demidov Yaroslavl State University, Yaroslavl, Russian Federation
,
A Rusakov
4   P.G. Demidov Yaroslavl State University, Yaroslavl, Russian Federation
,
E Nikonov
2   Pirogov Russian National Research Medical University, Moscow, Russian Federation
› Author Affiliations
Further Information

Publication History

Publication Date:
18 March 2019 (online)

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Aims:

The aims of this study were to develop and evaluate a computer-aided diagnosis system for histology prediction of gastric lesions using magnifying narrow band imaging (M-NBI) endoscopy.

Methods:

We selected and analyzed 265 endoscopy M-NBI images of gastric lesions from 128 patients who underwent upper M-NBI endoscopy (Olympus Exera GIF Q160Z, Lucera GIF Q260Z). All images were divided into four classes: (1) type A (n = 46): non-neoplastic and non-metaplastic lesions with regular circular microsurface (MS) and regular microvascular (MV) patterns; (2) Type B (n = 90): intestinal metaplasia with tubulo-villous MS and regular MV patterns; (3) Type C (n = 74) neoplastic lesions with irregular MS or MV pattern; (4) artifacts (n = 55). During automated classification quadrant areas were calculated on the image, geometrical and topological features were computed for every fragment. Using the greedy forward selection algorithm, the set of five most significant features were selected: three geometric features (the compactness of the MS pattern, the perimeter of the MS pattern, the average of area of the component of the MV pattern) two topological features (the kurtosis of the histogram of the 0-th persistence diagram of the image, the first norm of the 0-th persistence diagram of the signed distance function). Support vector machine (SVM) classifier was used for 4-class automated diagnosis. Training and testing were performed for every image by a k-fold method (k = 10).

Results:

The average percentage of correctly recognized areas was 91.4%. Classification precision (positive predictive value), recall (sensitivity), F-score for class A were 96.5 90.4 93.3 for class B were 93.7, 92.0, 92.9, respectively, for class C were 83.3, 91.3, 87.1, respectively, and for artifacts were 99.2, 91.7, 95.3, respectively.

Conclusions:

The designed system based on the extraction of the geometrical and topological features from M-NBI image and analysis by SVM could provide effective recognition of three types of gastric mucosal changes.