TY - JOUR
T1 - A computer-assisted algorithm for narrow-band imaging-based tissue characterization in Barrett's esophagus
AU - Struyvenberg, Maarten R.
AU - de Groof, Albert J.
AU - van der Putten, Joost
AU - van der Sommen, Fons
AU - Baldaque-Silva, Francisco
AU - Omae, Masami
AU - Pouw, Roos
AU - Bisschops, Raf
AU - Vieth, Michael
AU - Schoon, Erik J.
AU - Curvers, Wouter L.
AU - de With, Peter H.
AU - Bergman, Jacques J.
N1 - Funding Information: DISCLOSURE: Dr Bergmann has received research support from NinePoint Medical and speaker fees from Fujifilm. Dr Baldaque-Silva has received research support from Boston Scientific. Dr Bisschops has received research support, consulting fees, and speaker fees from Fujifilm. Dr Vieth has received honoraria for lecturing from Falk, Shire, and Olympus. All other authors disclosed no financial relationships. Publisher Copyright: © 2021 American Society for Gastrointestinal Endoscopy Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/1
Y1 - 2021/1
N2 - Background and Aims: The endoscopic evaluation of narrow-band imaging (NBI) zoom imagery in Barrett's esophagus (BE) is associated with suboptimal diagnostic accuracy and poor interobserver agreement. Computer-aided diagnosis (CAD) systems may assist endoscopists in the characterization of Barrett's mucosa. Our aim was to demonstrate the feasibility of a deep-learning CAD system for tissue characterization of NBI zoom imagery in BE. Methods: The CAD system was first trained using 494,364 endoscopic images of general endoscopic imagery. Next, 690 neoplastic BE and 557 nondysplastic BE (NDBE) white-light endoscopy overview images were used for refinement training. Subsequently, a third dataset of 112 neoplastic and 71 NDBE NBI zoom images with histologic correlation was used for training and internal validation. Finally, the CAD system was further trained and validated with a fourth, histologically confirmed dataset of 59 neoplastic and 98 NDBE NBI zoom videos. Performance was evaluated using fourfold cross-validation. The primary outcome was the diagnostic performance of the CAD system for classification of neoplasia in NBI zoom videos. Results: The CAD system demonstrated accuracy, sensitivity, and specificity for detection of BE neoplasia using NBI zoom images of 84%, 88%, and 78%, respectively. In total, 30,021 individual video frames were analyzed by the CAD system. Accuracy, sensitivity, and specificity of the video-based CAD system were 83% (95% confidence interval [CI], 78%-89%), 85% (95% CI, 76%-94%), and 83% (95% CI, 76%-90%), respectively. The mean assessment speed was 38 frames per second. Conclusion: We have demonstrated promising diagnostic accuracy of predicting the presence/absence of Barrett's neoplasia on histologically confirmed unaltered NBI zoom videos with fast corresponding assessment time.
AB - Background and Aims: The endoscopic evaluation of narrow-band imaging (NBI) zoom imagery in Barrett's esophagus (BE) is associated with suboptimal diagnostic accuracy and poor interobserver agreement. Computer-aided diagnosis (CAD) systems may assist endoscopists in the characterization of Barrett's mucosa. Our aim was to demonstrate the feasibility of a deep-learning CAD system for tissue characterization of NBI zoom imagery in BE. Methods: The CAD system was first trained using 494,364 endoscopic images of general endoscopic imagery. Next, 690 neoplastic BE and 557 nondysplastic BE (NDBE) white-light endoscopy overview images were used for refinement training. Subsequently, a third dataset of 112 neoplastic and 71 NDBE NBI zoom images with histologic correlation was used for training and internal validation. Finally, the CAD system was further trained and validated with a fourth, histologically confirmed dataset of 59 neoplastic and 98 NDBE NBI zoom videos. Performance was evaluated using fourfold cross-validation. The primary outcome was the diagnostic performance of the CAD system for classification of neoplasia in NBI zoom videos. Results: The CAD system demonstrated accuracy, sensitivity, and specificity for detection of BE neoplasia using NBI zoom images of 84%, 88%, and 78%, respectively. In total, 30,021 individual video frames were analyzed by the CAD system. Accuracy, sensitivity, and specificity of the video-based CAD system were 83% (95% confidence interval [CI], 78%-89%), 85% (95% CI, 76%-94%), and 83% (95% CI, 76%-90%), respectively. The mean assessment speed was 38 frames per second. Conclusion: We have demonstrated promising diagnostic accuracy of predicting the presence/absence of Barrett's neoplasia on histologically confirmed unaltered NBI zoom videos with fast corresponding assessment time.
UR - http://www.scopus.com/inward/record.url?scp=85090157074&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.gie.2020.05.050
DO - https://doi.org/10.1016/j.gie.2020.05.050
M3 - Article
C2 - 32504696
SN - 0016-5107
VL - 93
SP - 89
EP - 98
JO - Gastrointestinal Endoscopy
JF - Gastrointestinal Endoscopy
IS - 1
ER -