BACKGROUND & AIMS We aimed to develop and validate a deep-learning computer-aided detection (CAD) system, suitable for use in real time in clinical practice, to improve endoscopic detection of early neoplasia in patients with Barrett's esophagus (BE). METHODS We developed a hybrid ResNet-UNet model CAD system using 5 independent endoscopy data sets. We performed pretraining using 494,364 labeled endoscopic images collected from all intestinal segments. Then, we used 1704 unique esophageal high-resolution images of rigorously confirmed early-stage neoplasia in BE and nondysplastic BE, derived from 669 patients. System performance was assessed by using data sets 4 and 5. Data set 5 was also scored by 53 general endoscopists with a wide range of experience from 4 countries to benchmark CAD system performance. Coupled with histopathology findings, scoring of images that contained early-stage neoplasia in data sets 2-5 were delineated in detail for neoplasm position and extent by multiple experts whose evaluations served as the ground truth for segmentation. RESULTS The CAD system classified images as containing neoplasms or nondysplastic BE with 89% accuracy, 90% sensitivity, and 88% specificity (data set 4, 80 patients and images). In data set 5 (80 patients and images) values for the CAD system vs those of the general endoscopists were 88% vs 73% accuracy, 93% vs 72% sensitivity, and 83% vs 74% specificity. The CAD system achieved higher accuracy than any of the individual 53 nonexpert endoscopists, with comparable delineation performance. CAD delineations of the area of neoplasm overlapped with those from the BE experts in all detected neoplasia in data sets 4 and 5. The CAD system identified the optimal site for biopsy of detected neoplasia in 97% and 92% of cases (data sets 4 and 5, respectively). CONCLUSIONS We developed, validated, and benchmarked a deep-learning computer-aided system for primary detection of neoplasia in patients with BE. The system detected neoplasia with high accuracy and near-perfect delineation performance. The Netherlands National Trials Registry, Number: NTR7072.

中文翻译：

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深度学习系统通过基准测试的多步训练和验证研究，比内镜检查员能够更准确地检测Barrett食管患者的肿瘤形成。

背景与目的我们旨在开发和验证一种适用于临床实践的深度学习计算机辅助检测（CAD）系统，以改善Barrett食管（BE）患者早期内膜瘤的内窥镜检测。方法我们使用5个独立的内窥镜数据集开发了一个ResNet-UNet混合模型CAD系统。我们使用从所有肠段收集的494,364标记内窥镜图像进行了预训练。然后，我们使用了来自669名患者的1704幅独特的食管高分辨率图像，这些图像经过严格确认的BE和非增生性BE早期肿瘤形成。系统性能是通过使用数据集4和5进行评估的。数据集5也由53位具有4个国家/地区的丰富经验的基准内镜医师对CAD系统性能进行了评分。结合组织病理学发现，由多位专家详细描述了肿瘤位置和范围，对数据集2-5中包含早期肿瘤的图像评分，这些专家的评估是进行分割的基础。结果CAD系统将图像分类为包含肿瘤或非增生性BE，其准确性为89％，敏感性为90％，特异性为88％（数据集4、80例患者和图像）。在数据集5（80例患者和图像）中，CAD系统与普通内镜医师的准确性分别为88％vs 73％，敏感性93％vs 72％和83％vs 74％。该CAD系统具有比单独的53位非专业内镜医师更高的准确性，并具有可比的描绘性能。在数据集4和5中，在所有检测到的赘生物中，肿​​瘤区域的CAD描绘与来自BE专家的描述相重叠。CAD系统在97％和92％的病例（数据集4和5）。结论我们开发，验证并确定了一个深度学习计算机辅助系统的基准，该系统可用于BE患者的肿瘤形成的初步检测。该系统以高精度和近乎完美的描绘性能检测到肿瘤。荷兰国家审判注册处，编号：NTR7072。并建立了一个用于深度学习的计算机辅助系统，用于BE患者的肿瘤形成的初步检测。该系统以高精度和近乎完美的描绘性能检测到肿瘤。荷兰国家审判注册处，编号：NTR7072。并建立了一个用于深度学习的计算机辅助系统，用于BE患者的肿瘤形成的初步检测。该系统以高精度和近乎完美的描绘性能检测到肿瘤。荷兰国家审判注册处，编号：NTR7072。