Lupus Nephritis Neural Network, LuNN

狼疮性肾炎神经网络，LuNN

• 批准号: 10246669
• 负责人: CHANDRA MOHAN
• 金额: $ 10.08万
• 依托单位: UNIVERSITY OF HOUSTON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10246669
• 关键词: Adult; Algorithms; Automobile Driving; Cellular Structures; Child; Chronic; Classification; Computer Vision Systems; Diagnosis; Diagnostic; End stage renal failure; Feedback; Gold; Histologic; Human; Image; Kidney; Lupus; Lupus Nephritis; Machine Learning; Mus; Nephritis; Outcome; Outcome Study; Pathologist; Pathology; Patients; Performance; Phenotype; Prediction of Response to Therapy; Reading; Reporting; Reproducibility; Retrieval; Supervision; Systemic Lupus Erythematosus; Testing; Tissues; Training; Uncertainty; accurate diagnosis; base; convolutional neural network; deep learning; diagnosis standard; falls; improved; indexing; innovation; kidney biopsy; neural network; novel; predict clinical outcome; time interval; tool; treatment response; user-friendly; web portal

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.

高达60%的成人和80%的儿童患有系统性红斑狼疮（SLE）