Background and Aims

The endoscopic evaluation of narrow-band imaging (NBI) zoom imagery in Barrett’s esophagus (BE) is associated with suboptimal diagnostic accuracy and poor interobserver agreement. Computer-aided diagnosis (CAD) systems may assist endoscopists in the characterization of Barrett’s mucosa. Our aim was to demonstrate the feasibility of a deep-learning CAD system for tissue characterization of NBI zoom imagery in BE.

Methods

The CAD system was first trained using 494,364 endoscopic images of general endoscopic imagery. Next, 690 neoplastic BE and 557 nondysplastic BE (NDBE) white-light endoscopy overview images were used for refinement training. Subsequently, a third dataset of 112 neoplastic and 71 NDBE NBI zoom images with histologic correlation was used for training and internal validation. Finally, the CAD system was further trained and validated with a fourth, histologically confirmed dataset of 59 neoplastic and 98 NDBE NBI zoom videos. Performance was evaluated using fourfold cross-validation. The primary outcome was the diagnostic performance of the CAD system for classification of neoplasia in NBI zoom videos.

Results

The CAD system demonstrated accuracy, sensitivity, and specificity for detection of BE neoplasia using NBI zoom images of 84%, 88%, and 78%, respectively. In total, 30,021 individual video frames were analyzed by the CAD system. Accuracy, sensitivity, and specificity of the video-based CAD system were 83% (95% confidence interval [CI], 78%-89%), 85% (95% CI, 76%-94%), and 83% (95% CI, 76%-90%), respectively. The mean assessment speed was 38 frames per second.

Conclusion

We have demonstrated promising diagnostic accuracy of predicting the presence/absence of Barrett’s neoplasia on histologically confirmed unaltered NBI zoom videos with fast corresponding assessment time.

中文翻译：

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Barrett食管中基于窄带成像的组织表征的计算机辅助算法。

背景和目标

内窥镜评估Barrett食管（BE）中的窄带成像（NBI）放大图像与诊断精度不佳和观察者之间的一致性差有关。计算机辅助诊断（CAD）系统可以协助内镜医师对Barrett粘膜进行表征。我们的目的是证明深度学习CAD系统用于BE中NBI变焦图像的组织表征的可行性。

方法

首先使用普通内窥镜影像的494,364内窥镜影像对CAD系统进行了训练。接下来，将690枚肿瘤性BE和557枚非发育异常性BE（NDBE）白光内窥镜概览图像用于精细训练。随后，将具有组织学相关性的112例肿瘤和71 NDBE NBI变焦图像的第三数据集用于训练和内部验证。最终，对CAD系统进行了进一步的训练，并用组织学证实的第四组数据集进行了验证，该数据集包含59个赘生物和98个NDBE NBI变焦视频。使用四重交叉验证评估性能。主要结果是CAD系统对NBI变焦视频中肿瘤形成的分类的诊断性能。

结果

CAD系统使用NBI缩放图像分别显示84％，88％和78％的BE瘤形成的准确性，敏感性和特异性。CAD系统总共分析了30,021个单独的视频帧。基于视频的CAD系统的准确性，敏感性和特异性分别为83％（95％置信区间[CI]，78％-89％），85％（95％CI，76％-94％）和83％（ 95％CI，76％-90％）。平均评估速度为每秒38帧。

结论

我们已经证明了在组织学确认的未改变的NBI变焦视频上以快速相应的评估时间预测Barrett瘤形成/不存在的诊断准确性。