用于脐带胎儿炎症反应诊断的深度学习
Deep Learning for Fetal Inflammatory Response Diagnosis in the Umbilical Cord
AI总结：
本研究利用基于注意力的全切片学习模型，对4100张H&E染色的脐带切片图像进行胎儿炎症反应（FIR）分类，发现基于组织病理学预训练模型（UNI）的集成模型准确率达0.836，优于ImageNet预训练模型，有助于减少病理学家间的诊断差异。
摘要：
脐带炎症可被视为上行性宫内感染或其他炎症刺激的结果。急性胎儿炎症反应（FIR）的特征是胎儿中性粒细胞浸润脐带，可能与新生儿败血症或胎儿炎症反应综合征相关。数字病理学中深度学习的最新进展已在诊断和预后等广泛的临床任务中展现出良好的性能。在本研究中，我们从全切片图像（WSI）中对FIR进行了分类。我们将4100张经苏木精和伊红（H&E）染色的脐带组织学切片数字化，并从电子健康记录中提取了胎盘诊断信息。我们使用基于注意力的全切片学习模型构建了模型。我们比较了两种特征提取策略：一种是在非医学图像（ImageNet）上预训练的模型（ConvNeXtXLarge），另一种是使用组织病理学图像预训练的模型（UNI）。我们对每个模型进行了多次迭代训练，并将它们组合成一个集成模型。使用UNI训练的集成模型在测试数据集上实现了0.836的总体平衡准确率。相比之下，使用ConvNeXtXLarge的集成预测平衡准确率较低，为0.7209。由最高准确率模型生成的热图在FIR 2病例中恰当地突显了动脉炎。在FIR 1中，表现最佳的模型对Wharton胶中活化样间质区域赋予了高注意力。然而，其他高性能模型则将注意力分配给了脐带血管。我们开发了用于从胎盘组织学图像中诊断FIR的模型，有助于减少病理学家之间的观察者间差异。未来的工作可以探索这些模型在识别有全身性炎症反应或早发型新生儿败血症风险的婴儿方面的效用。
英文摘要：
Inflammation of the umbilical cord can be seen as a result of ascending intrauterine infection or other inflammatory stimuli. Acute fetal inflammatory response (FIR) is characterized by infiltration of the umbilical cord by fetal neutrophils, and can be associated with neonatal sepsis or fetal inflammatory response syndrome. Recent advances in deep learning in digital pathology have demonstrated favorable performance across a wide range of clinical tasks, such as diagnosis and prognosis. In this study we classified FIR from whole slide images (WSI). We digitized 4100 histological slides of umbilical cord stained with hematoxylin and eosin(H&E) and extracted placental diagnoses from the electronic health record. We build models using attention-based whole slide learning models. We compared strategies between features extracted by a model (ConvNeXtXLarge) pretrained on non-medical images (ImageNet), and one pretrained using histopathology images (UNI). We trained multiple iterations of each model and combined them into an ensemble. The predictions from the ensemble of models trained using UNI achieved an overall balanced accuracy of 0.836 on the test dataset. In comparison, the ensembled predictions using ConvNeXtXLarge had a lower balanced accuracy of 0.7209. Heatmaps generated from top accuracy model appropriately highlighted arteritis in cases of FIR 2. In FIR 1, the highest performing model assigned high attention to areas of activated-appearing stroma in Wharton's Jelly. However, other high-performing models assigned attention to umbilical vessels. We developed models for diagnosis of FIR from placental histology images, helping reduce interobserver variability among pathologists. Future work may examine the utility of these models for identifying infants at risk of systemic inflammatory response or early onset neonatal sepsis.
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