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ABSTRACT
Background & aims
The aim of this study is to develop and externally validate a computer aided detection (CADe) system for detection and localization of Barrett’s neoplasia and assess its performance compared to general endoscopists in a statistically powered multi-center study using real-time video sequences.
Methods
In phase-1, the hybrid VGG16-SegNet model was trained using 75,198 images and videos (96 patients) of neoplastic and 1,014,973 images/videos (65 patients) of non-neoplastic Barrett’s. In phase-2, image-based validation was performed on a separate dataset of 107 images (20 patients) of neoplastic and 364 images (14 patients) of non-neoplastic Barrett’s. In phase-3 (video-based external validation) we designed a real-time video-based study with 32 neoplastic videos (32 patients) and 43 non-neoplastic (43 patients) Barrett’s videos from four European centers to compare the performance of the CADe model to that of six non-expert endoscopists. The primary end point was the sensitivity of CADe diagnosis of Barrett’s neoplasia.
Results
In phase 2, CADe detected Barrett’s neoplasia with sensitivity, specificity and accuracy of 95.3%, 94.5% and 94.7% respectively. In phase 3, the CADe system detected Barrett’s neoplasia with sensitivity, specificity, NPV and accuracy of 93.8%, 90.7%, 95.1% and 92.0% respectively compared to the endoscopists’ performance of 63.5%, 77.9%, 74.2% and 71.8% respectively (p<0.05 in all parameters). The CADe system localized neoplastic lesions with accuracy, mean precision and mean IoU of 100%, 0.62, 0.54 respectively when compared to at least one of the expert markings. The processing speed of the CADe detection and localization were 5ms/image and 33ms/image respectively.
Conclusion
This is the first study describing external (multi-center) validation of AI algorithms for Barrett’s neoplasia detection on real-time endoscopic videos. The CADe system in this study significantly outperformed non-expert endoscopists on real-time video-based assessment achieving >90% sensitivity for neoplasia detection. This needs to be validated during real-time endoscopic assessment.
Article info
Publication history
Accepted:
October 16,
2022
Received in revised form:
September 30,
2022
Received:
June 13,
2022
Publication stage
In Press Journal Pre-ProofFootnotes
Contributorship Statement
MA: study concept and design, data acquisition, drafting of the manuscript, analysis and interpretation of data, and critical revision of the manuscript.
MS: software implementation, analysis and interpretation of technical data, and revision of the manuscript.
NM: software implementation, analysis and interpretation of technical data, and revision of the manuscript.
EH: data acquisition, and revision of the manuscript.
AA: data acquisition, and critical revision of the manuscript.
SS: data acquisition, and critical revision of the manuscript.
AP: data acquisition, and critical revision of the manuscript.
AY: data acquisition, and critical revision of the manuscript.
EC: data acquisition, and critical revision of the manuscript.
AR: data acquisition, and critical revision of the manuscript.
PB: study concept and design, drafting of the manuscript, analysis and interpretation of data, and critical revision of the manuscript.
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Copyright
© 2022 by the American Society for Gastrointestinal Endoscopy
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