Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett's esophagus (with video)
| Autor: | Tyler Dao, Daniel Mai, Michael Lugo, Elise Tran, Kenneth J. Chang, Andrew Ninh, Williams Karnes, Nabil El Hage Chehade, Jason B. Samarasena, Rintaro Hashimoto, James Requa |
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| Rok vydání: | 2019 |
| Předmět: |
Esophageal Neoplasms
Video Recording Pilot Projects Convolutional neural network Chromoendoscopy 03 medical and health sciences Barrett Esophagus 0302 clinical medicine medicine Humans Radiology Nuclear Medicine and imaging Artificial neural network business.industry Deep learning Gastroenterology medicine.disease Object detection Binary classification Dysplasia 030220 oncology & carcinogenesis Barrett's esophagus 030211 gastroenterology & hepatology Artificial intelligence Esophagoscopy Neural Networks Computer business |
| Zdroj: | Gastrointestinal endoscopy. 91(6) |
| ISSN: | 1097-6779 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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