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Endoscopy 2022; 54(02): 180-184
DOI: 10.1055/a-1372-0419
Innovations and brief communications

Performance of a new integrated computer-assisted system (CADe/CADx) for detection and characterization of colorectal neoplasia

Jochen Weigt
1   Department of Gastroenterology, Hepatology and Infectious Diseases, Otto-v. Guericke University, Magdeburg, Germany
,
Alessandro Repici
2   Endoscopy Unit, Humanitas Clinical and Research Center – IRCCS, Milan, Italy
3   Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
,
Giulio Antonelli
4   Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome, Italy
,
Ahmed Afifi
1   Department of Gastroenterology, Hepatology and Infectious Diseases, Otto-v. Guericke University, Magdeburg, Germany
,
Leon Kliegis
1   Department of Gastroenterology, Hepatology and Infectious Diseases, Otto-v. Guericke University, Magdeburg, Germany
,
Loredana Correale
2   Endoscopy Unit, Humanitas Clinical and Research Center – IRCCS, Milan, Italy
,
Cesare Hassan
4   Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome, Italy
,
Helmut Neumann 
5   Department of Interdisciplinary Endoscopy, University Hospital Mainz, Mainz, Germany
6   GastroZentrum Lippe, Interventional Endoscopy, Bad Salzuflen, Germany
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Abstract

Background Use of artificial intelligence may increase detection of colorectal neoplasia at colonoscopy by improving lesion recognition (CADe) and reduce pathology costs by improving optical diagnosis (CADx).

Methods A multicenter library of ≥ 200 000 images from 1572 polyps was used to train a combined CADe/CADx system. System testing was performed on two independent image sets (CADe: 446 with polyps, 234 without; CADx: 267) from 234 polyps, which were also evaluated by six endoscopists (three experts, three non-experts).

Results CADe showed sensitivity, specificity, and accuracy of 92.9 %, 90.6 %, and 91.7 %, respectively. Experts showed significantly higher accuracy and specificity, and similar sensitivity, while non-experts + CADe showed comparable sensitivity but lower specificity and accuracy than CADe and experts. CADx showed sensitivity, specificity, and accuracy of 85.0 %, 79.4 %, and 83.6 %, respectively. Experts showed comparable performance, whereas non-experts + CADx showed comparable accuracy but lower specificity than CADx and experts.

Conclusions The high accuracy shown by CADe and CADx was similar to that of experts, supporting further evaluation in a clinical setting. When using CAD, non-experts achieved a similar performance to experts, with suboptimal specificity.

Appendix 1s, Tables 1s, 2s, Fig. 1s



Publication History

Received: 16 July 2020

Accepted: 25 January 2021

Accepted Manuscript online:
25 January 2021

Article published online:
20 April 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

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