Deep learning systems detect dysplasia with human-like accuracy using histopathology and probe-based confocal laser endomicroscopy.

Probe-based confocal laser endomicroscopy (pCLE) allows for real-time diagnosis of dysplasia and cancer in Barrett’s esophagus (BE) but is limited by low sensitivity. Even the gold standard of histopathology is hindered by poor agreement between pathologists. We deployed deep-learning-based image and video analysis in order to improve diagnostic accuracy of pCLE videos and biopsy images. Blinded experts categorized biopsies and pCLE videos as squamous, non-dysplastic BE, or dysplasia/cancer, and deep learning models were trained to classify the data into these three categories. Biopsy classification was conducted using two distinct approaches—a patch-level model and a whole-slide-image-level model. Gradient-weighted class activation maps (Grad-CAMs) were extracted from pCLE and biopsy models in order to determine tissue structures deemed relevant by the models. 1970 pCLE videos, 897,931 biopsy patches, and 387 whole-slide images were used to train, test, and validate the models. In pCLE analysis, models achieved a high sensitivity for dysplasia (71%) and an overall accuracy of 90% for all classes. For biopsies at the patch level, the model achieved a sensitivity of 72% for dysplasia and an overall accuracy of 90%. The whole-slide-image-level model achieved a sensitivity of 90% for dysplasia and 94% overall accuracy. Grad-CAMs for all models showed activation in medically relevant tissue regions. Our deep learning models achieved high diagnostic accuracy for both pCLE-based and histopathologic diagnosis of esophageal dysplasia and its precursors, similar to human accuracy in prior studies. These machine learning approaches may improve accuracy and efficiency of current screening protocols.

基于探针的共聚焦激光显微内镜检查（pCLE）可对巴雷特食管（BE）的异型增生和癌症进行实时诊断，但灵敏度较低。即使组织病理学的金标准也因病理学家之间的一致性差而受阻。我们采用基于深度学习的图像和视频分析，以提高pCLE视频和活检图像的诊断准确性。不知情的专家将活检组织和pCLE视频分类为鳞状、非异型增生性BE或异型增生/癌症，并训练深度学习模型将数据分为这三类。活检分类采用两种不同的方法——切片级别模型和整张切片图像级别模型。从pCLE和活检模型中提取梯度加权类激活图（Grad - CAMs），以确定模型认为相关的组织结构。使用1970个pCLE视频、897931个活检切片和387张整张切片图像来训练、测试和验证模型。在pCLE分析中，模型对异型增生的灵敏度较高（71%），所有类别总体准确率为90%。对于切片级别的活检，模型对异型增生的灵敏度为72%，总体准确率为90%。整张切片图像级别模型对异型增生的灵敏度为90%，总体准确率为94%。所有模型的Grad - CAMs在医学相关组织区域均有激活。我们的深度学习模型在基于pCLE和组织病理学对食管异型增生及其前体的诊断中均取得了较高的诊断准确性，与先前研究中的人类准确性相似。这些机器学习方法可能会提高当前筛查方案的准确性和效率。