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Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett's esophagus (with video).
Gastrointestinal Endoscopy ( IF 7.7 ) Pub Date : 2020-01-11 , DOI: 10.1016/j.gie.2019.12.049 Rintaro Hashimoto 1 , James Requa 2 , Tyler Dao 2 , Andrew Ninh 2 , Elise Tran 1 , Daniel Mai 1 , Michael Lugo 1 , Nabil El-Hage Chehade 1 , Kenneth J Chang 1 , Williams E Karnes 1 , Jason B Samarasena 1
Gastrointestinal Endoscopy ( IF 7.7 ) Pub Date : 2020-01-11 , DOI: 10.1016/j.gie.2019.12.049 Rintaro Hashimoto 1 , James Requa 2 , Tyler Dao 2 , Andrew Ninh 2 , Elise Tran 1 , Daniel Mai 1 , Michael Lugo 1 , Nabil El-Hage Chehade 1 , Kenneth J Chang 1 , Williams E Karnes 1 , Jason B Samarasena 1
Affiliation
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.
中文翻译：
使用卷积神经网络的人工智能实时检测巴雷特食管中的早期食道肿瘤（带视频）。
背景与目的用白光和虚拟彩色内窥镜对巴雷特食管（BE）的早期食道肿瘤（高度不典型增生和T1癌）进行视觉检测仍然具有挑战性。这项研究的目的是评估卷积神经人工智能网络是否可以帮助识别BE早期食道肿瘤。方法收集65例经组织学证实为BE的早期食管肿瘤的916幅图像，其中包含高度不典型增生或T1癌。使用图像注释软件掩盖瘤形成区域。收集了119例没有高度不典型增生的BE对照图像。在ImageNet上对卷积神经网络（CNN）算法进行了预训练，然后对其进行了微调，目的是提供“发育异常”或“发育异常”的正确二进制分类。我们开发了一种对象检测算法，该算法在分类为发育不良的区域周围绘制了定位框。结果CNN分析了458张测试图像（225个不典型增生和233个非典型增生），并正确检测了早期肿瘤，敏感性为96.4％，特异性为94.2％，准确度为95.4％。关于验证集中所有图像的对象检测算法，该系统在.3的并集上的交点处能够实现.7533的平均平均精度。结论：在该初步研究中，我们的人工智能模型能够在BE图像中以高准确度检测早期食道肿瘤。此外，
更新日期：2020-01-11
中文翻译：
使用卷积神经网络的人工智能实时检测巴雷特食管中的早期食道肿瘤（带视频）。
背景与目的用白光和虚拟彩色内窥镜对巴雷特食管（BE）的早期食道肿瘤（高度不典型增生和T1癌）进行视觉检测仍然具有挑战性。这项研究的目的是评估卷积神经人工智能网络是否可以帮助识别BE早期食道肿瘤。方法收集65例经组织学证实为BE的早期食管肿瘤的916幅图像，其中包含高度不典型增生或T1癌。使用图像注释软件掩盖瘤形成区域。收集了119例没有高度不典型增生的BE对照图像。在ImageNet上对卷积神经网络（CNN）算法进行了预训练，然后对其进行了微调，目的是提供“发育异常”或“发育异常”的正确二进制分类。我们开发了一种对象检测算法，该算法在分类为发育不良的区域周围绘制了定位框。结果CNN分析了458张测试图像（225个不典型增生和233个非典型增生），并正确检测了早期肿瘤，敏感性为96.4％，特异性为94.2％，准确度为95.4％。关于验证集中所有图像的对象检测算法，该系统在.3的并集上的交点处能够实现.7533的平均平均精度。结论：在该初步研究中，我们的人工智能模型能够在BE图像中以高准确度检测早期食道肿瘤。此外，
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