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New Generation Express View: An Artificial Intelligence Software Effectively Reduces Capsule Endoscopy Reading Times

Authors :
Stefania Piccirelli
Alessandro Mussetto
Angelo Bellumat
Renato Cannizzaro
Marco Pennazio
Alessandro Pezzoli
Alessandra Bizzotto
Nadia Fusetti
Flavio Valiante
Cesare Hassan
Silvia Pecere
Anastasios Koulaouzidis
Cristiano Spada
Source :
Diagnostics, Vol 12, Iss 8, p 1783 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

BACKGROUND: Reading capsule endoscopy (CE) is time-consuming. The Express View (EV) (IntroMedic, Seoul, Korea) software was designed to shorten CE video reading. Our primary aim was to evaluate the diagnostic accuracy of EV in detecting significant small-bowel (SB) lesions. We also compared the reading times with EV mode and standard reading (SR). METHODS: 126 patients with suspected SB bleeding and/or suspected neoplasia were prospectively enrolled and underwent SB CE (MiroCam®1200, IntroMedic, Seoul, Korea). CE evaluation was performed in standard and EV mode. In case of discrepancies between SR and EV readings, a consensus was reached after reviewing the video segments and the findings were re-classified. RESULTS: The completion rate of SB CE in our cohort was 86.5% and no retention occurred. The per-patient analysis of sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of EV compared to SR were 86%, 86%, 90%, 81%, and 86%, respectively, before consensus. After consensus, they increased to 97%, 100%, 100%, 96%, and 98%, respectively. The median reading time with SR and EV was 71 min (range 26–340) and 13 min (range 3–85), respectively (p < 0.001). CONCLUSIONS: The new-generation EV shows high diagnostic accuracy and significantly reduces CE reading times.

Details

Language :
English
ISSN :
20754418
Volume :
12
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Diagnostics
Publication Type :
Academic Journal
Accession number :
edsdoj.7e565125cd19409ca51d2c706f3b3c86
Document Type :
article
Full Text :
https://doi.org/10.3390/diagnostics12081783