Department of Pathology, National Taiwan University Hospital, Taipei, Taiwan.
aetherAI Co., Ltd., Taipei, Taiwan.
Histopathology. 2023 Nov;83(5):771-781. doi: 10.1111/his.15018. Epub 2023 Jul 31.
Helicobacter pylori (HP) infection is the most common cause of chronic gastritis worldwide. Due to the small size of HP and limited resolution, diagnosing HP infections is more difficult when using digital slides.
We developed a two-tier deep-learning-based model for diagnosing HP gastritis. A whole-slide model was trained on 885 whole-slide images (WSIs) with only slide-level labels (positive or negative slides). An auxiliary model was trained on 824 areas with HP in nine positive WSIs and 446 negative WSIs for localizing HP. The whole-slide model performed well, with an area under the receiver operating characteristic curve (AUC) of 0.9739 (95% confidence interval [CI], 0.9545-0.9932). The calculated sensitivity and specificity were 93.3% and 90.1%, respectively, whereas those of pathologists were 93.3% and 84.2%, respectively. Using the auxiliary model, the highlighted areas of the localization maps had an average precision of 0.5796.
HP gastritis can be diagnosed on haematoxylin-and-eosin-stained WSIs with human-level accuracy using a deep-learning-based model trained on slide-level labels and an auxiliary model for localizing HP and confirming the diagnosis. This two-tiered model can shorten the diagnostic process and reduce the need for special staining.
幽门螺杆菌（HP）感染是全球范围内导致慢性胃炎的最常见原因。由于 HP 体积小且分辨率有限，因此在使用数字切片时，诊断 HP 感染更加困难。
我们开发了一种基于深度学习的两级模型，用于诊断 HP 胃炎。全幻灯片模型仅在幻灯片级别标签（阳性或阴性幻灯片）上，在 885 张全幻灯片图像（WSI）上进行训练。辅助模型在 824 个区域进行训练，这些区域包含在 9 张阳性 WSI 中的 446 个阴性 WSI 中的 9 个阳性 WSI 中，用于定位 HP。全幻灯片模型表现良好，接收者操作特征曲线下的面积（AUC）为 0.9739（95%置信区间[CI]，0.9545-0.9932）。计算出的敏感性和特异性分别为 93.3%和 90.1%，而病理学家的敏感性和特异性分别为 93.3%和 84.2%。使用辅助模型，定位图中突出显示的区域的平均精度为 0.5796。
使用基于深度学习的模型，在基于幻灯片级标签和用于定位 HP 并确认诊断的辅助模型上进行训练，可以在苏木精和伊红染色的 WSI 上以与人类相当的准确性诊断 HP 胃炎。这种两级模型可以缩短诊断过程并减少对特殊染色的需求。
