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Deep learning algorithm detection of Barrett’s neoplasia with high accuracy during live endoscopic procedures: a pilot study (with video) - 19/05/20

Doi : 10.1016/j.gie.2019.12.048 
Albert J. de Groof, MD 1, Maarten R. Struyvenberg, MD 1, Kiki N. Fockens, MD 1, Joost van der Putten, MSc 2, Fons van der Sommen, PhD 2, Tim G. Boers, MSc 2, Sveta Zinger, PhD 2, Raf Bisschops, MD, PhD 3, Peter H. de With, PhD 2, Roos E. Pouw, MD, PhD 1, Wouter L. Curvers, MD, PhD 4, Erik J. Schoon, MD, PhD 4, Jacques J.G. H.M. Bergman, MD, PhD 1,
1 Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands 
2 Department of Electrical Engineering, VCA Group, Eindhoven University of Technology, Eindhoven, the Netherlands 
3 Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium 
4 Department of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven, the Netherlands 

Reprint requests: Jacques J. G. H. M. Bergman, MD, PhD, Amsterdam UMC, location Academic Medical Center, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands.Amsterdam UMClocation Academic Medical CenterMeibergdreef 9AZ Amsterdam1105The Netherlands

Abstract

Background and Aims

We assessed the preliminary diagnostic accuracy of a recently developed computer-aided detection (CAD) system for detection of Barrett’s neoplasia during live endoscopic procedures.

Methods

The CAD system was tested during endoscopic procedures in 10 patients with nondysplastic Barrett’s esophagus (NDBE) and 10 patients with confirmed Barrett’s neoplasia. White-light endoscopy images were obtained at every 2-cm level of the Barrett’s segment and immediately analyzed by the CAD system, providing instant feedback to the endoscopist. At every level, 3 images were evaluated by the CAD system. Outcome measures were diagnostic performance of the CAD system per level and per patient, defined as accuracy, sensitivity, and specificity (ground truth was established by expert assessment and corresponding histopathology), and concordance of 3 sequential CAD predictions per level.

Results

Accuracy, sensitivity, and specificity of the CAD system in a per-level analyses were 90%, 91%, and 89%, respectively. Nine of 10 neoplastic patients were correctly diagnosed. The single lesion not detected by CAD showed NDBE in the endoscopic resection specimen. In only 1 NDBE patient, the CAD system produced false-positive predictions. In 75% of all levels, the CAD system produced 3 concordant predictions.

Conclusions

This is one of the first studies to evaluate a CAD system for Barrett’s neoplasia during live endoscopic procedures. The system detected neoplasia with high accuracy, with only a small number of false-positive predictions and with a high concordance rate between separate predictions. The CAD system is thereby ready for testing in larger, multicenter trials. (Clinical trial registration number: NL7544.)

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Abbreviations : BE, CAD, EAC, NDBE, SD


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 DISCLOSURE: The following authors disclosed financial relationships: R. Bisschops: Research support from and consultant and speaker for Fujifilm. J.J. Bergman: Research support from Research support from Fujifilm and NinePoint Medical; speaker for Fujifilm. E.J. Schoon: Research support and speaker fees from Fujifilm. All other authors disclosed no financial relationships. Research support for this study was provided by the Dutch Cancer Society and Technology Foundation STW, as part of their joint strategic research program “Technology for Oncology.”


© 2020  American Society for Gastrointestinal Endoscopy. Pubblicato da Elsevier Masson SAS. Tutti i diritti riservati.
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Vol 91 - N° 6

P. 1242-1250 - giugno 2020 Ritorno al numero
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