Lupus Nephritis Neural Network, LuNN

高达60%的成人和80%的儿童系统性红斑狼疮（SLE）发展 肾炎（LN），10-30%进展为终末期肾病（ESRD）。金本位 诊断LN的方法是肾活检。组织学参数仍然是最好的预测因子 终末期肾病尽管是金标准，LN的组织学诊断有几个缺点。 在迄今为止报告的多项观察者间肾脏病理学评估研究中， 在评估大多数组织学参数时，病理学家相关系数或一致性 都是次优的这为目前的提议提供了动力。 我们建议利用计算机视觉和深度学习的力量来构建一个分类器， 在使用目前的诊断方法对LN进行组织学诊断方面， 诊断标准我们建议训练一个深度卷积神经网络来区分 不同的LN类别，并确定用于诊断的组织学属性的全谱。 我们将比较新生成的神经网络在评分方面的表现 肾小球/肾小管间质特征和LN分类，与一组人肾 病理学家最后，我们建议建立一个可以预测临床结果的神经网络 基于基线肾脏病理学。可靠和可重复的LN分类可以 显著改善患者管理和长期肾脏和患者生存。

Up to 60% of adults and 80% of children with systemic lupus erythematosus (SLE) develop nephritis (LN), with 10–30% progressing to end-stage renal disease (ESRD). The gold standard for diagnosis of LN is a renal biopsy. Histological parameters remain the best predictors of ESRD. Despite being the gold standard, histological diagnosis of LN has several shortcomings. In multiple inter-observer renal pathology assessment studies reported thus far, the inter- pathologist correlation coefficients, or concordance, in assessing most histological parameters have been sub-optimal. This has provided the impetus for the current proposal. We propose to leverage the power of computer vision and deep learning to build a classifier that rivals the best-trained renal pathologists in making a histological diagnosis of LN using current diagnostic criteria. We propose to train a deep convolutional neural network to distinguish the different LN classes, and to identify a full spectrum of histological attributes useful for diagnosis. We will compare the performance of the newly generated neural network in scoring glomerular/tubulo-interstitial features and LN classes, against a panel of human renal pathologists. Finally, we propose to build a neural network that can predict clinical outcome based on baseline renal pathology. Reliable and reproducible classification of LN could dramatically improve patient management and long-term renal and patient survival.