Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett’s esophagus (with video) - 19/05/20
Abstract |
Background and Aims |
The visual detection of early esophageal neoplasia (high-grade dysplasia and T1 cancer) in Barrett’s esophagus (BE) with white-light and virtual chromoendoscopy still remains challenging. The aim of this study was to assess whether a convolutional neural artificial intelligence network can aid in the recognition of early esophageal neoplasia in BE.
Methods |
Nine hundred sixteen images from 65 patients of histology-proven early esophageal neoplasia in BE containing high-grade dysplasia or T1 cancer were collected. The area of neoplasia was masked using image annotation software. Nine hundred nineteen control images were collected of BE without high-grade dysplasia. A convolutional neural network (CNN) algorithm was pretrained on ImageNet and then fine-tuned with the goal of providing the correct binary classification of “dysplastic” or “nondysplastic.” We developed an object detection algorithm that drew localization boxes around regions classified as dysplasia.
Results |
The CNN analyzed 458 test images (225 dysplasia and 233 nondysplasia) and correctly detected early neoplasia with sensitivity of 96.4%, specificity of 94.2%, and accuracy of 95.4%. With regard to the object detection algorithm for all images in the validation set, the system was able to achieve a mean average precision of .7533 at an intersection over union of .3
Conclusions |
In this pilot study, our artificial intelligence model was able to detect early esophageal neoplasia in BE images with high accuracy. In addition, the object detection algorithm was able to draw a localization box around the areas of dysplasia with high precision and at a speed that allows for real-time implementation.
Le texte complet de cet article est disponible en PDF.Abbreviations : AI, BE, CNN, DL, mAP, NBI, WLI
Plan
| DISCLOSURE: The following authors disclosed financial relationships: J. Requa: Employee of Docbot Inc. D. Tyler, A. Ninh, W. E. Karnes: Co-founder and equity holder of Docbot Inc. K. J. Chang: Consultant for Olympus, Cook Medical, Medtronics, Endogastric Solution, Erbe, Apollo, Mederi, Ovesco, Mauna Kea, and Pentax. J. Samarasena: Co-founder and equity holder of Docbot Inc; consultant for Medtronic, Olympus, US Endoscopy, Mauna Kea, Motus, and Pentax. All other authors disclosed no financial relationships. |
|
| If you would like to chat with an author of this article, you may contact Dr Samarasena at jsamaras@hs.uci.edu. |
Vol 91 - N° 6
P. 1264 - juin 2020 Retour au numéro
Article précédent
- A prediction model for detection of esophageal squamous cell cancer: A new beginning or more of the same?
- Yaseen Perbtani, Bashar J. Qumseya
- Diagnostic findings and long-term prognosis in children with anemia undergoing GI endoscopies
- Marleena Repo, Teemu Rajalahti, Pauliina Hiltunen, Antti Sotka, Laura Kivelä, Heini Huhtala, Katri Kaukinen, Katri Lindfors, Kalle Kurppa
Bienvenue sur EM-consulte, la référence des professionnels de santé.
L’accès au texte intégral de cet article nécessite un abonnement.
Déjà abonné à cette revue ?