Development and Validation of a Deep Learning system to estimate Interstitial Fibrosis from a kidney ultrasonography image

开发和验证从肾脏超声图像估计间质纤维化的深度学习系统

基本信息

批准号：
10781840
负责人：
Ambarish Athavale
金额：
$ 35.8万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-22 至 2028-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10781840
关键词：
Age Albuminuria Algorithms Artificial Intelligence Atrophic Biopsy Body Size Chronic Kidney Failure Clinical Data Development Disease Disease Progression Elderly Etiology Evaluation Fibrosis Future Gender Goals Hemorrhage Image Individual Kidney Kidney Diseases Kidney Failure Length Methods Mission Modeling Monitor Outcome Pathologist Patients Performance Persons Plug-in Population Prognosis Reading Renal function Reproducibility Research Severities Severity of illness Slide System Techniques Testing Therapeutic immunosuppression Time Tubular formation Ultrasonography United States National Institutes of Health Validation Work aged blind clinical biomarkers clinical practice clinical predictors clinically relevant cohort deep learning deep learning model hazard histopathological examination improved interstitial kidney biopsy kidney imaging nephrogenesis novel therapeutics prognostic prognostication programs radiologist routine imaging tool treatment response ultrasound user-friendly virtual

项目摘要

PROJECT SUMMARY Interstitial fibrosis is a common finding on kidney biopsy, and strongly predicts future decline in kidney function irrespective of the underlying etiology of kidney disease. Unfortunately, interstitial fibrosis is poorly captured by the current clinical biomarkers of kidney function (eGFR and albuminuria). Thus, interstitial fibrosis is common, holds substantial prognostic importance, and yet clinicians are blind to its presence or severity except in rare instances when kidney biopsies are performed. Concurrently, new drugs are being tested to limit kidney interstitial fibrosis, but there are no non-invasive methods to assess changes in fibrosis over time. Interstitial fibrosis is currently estimated from histopathological examination of a kidney biopsy, which are rarely done. A non-invasive test to estimate interstitial fibrosis is not currently available. Our exciting preliminary data demonstrated that use of routine ultrasonography (USG) of the kidney, interpreted by deep learning/artificial intelligence can non-invasively assess the presence and severity of interstitial fibrosis. The overarching goal of this study is to further develop, and internally and externally validate a deep learning-based algorithm to estimate interstitial fibrosis from USG images of the kidney relative to the kidney biopsy gold standard. We hypothesize that, embedded within a kidney USG image are interstitial fibrosis corelates that can be extracted by deep learning and quantitatively analyzed to estimate interstitial fibrosis with high precision, and will improve prediction of longitudinal decline in kidney function. If so, given the widespread availability of kidney USG world-wide, this non-invasive estimate of interstitial fibrosis would have immediate clinical implications with improved prognostication, and ability to serially monitor interstitial fibrosis in response to therapy. The proposed program of research will address three specific aims: Aim 1. To further develop and internally validate a deep learning- based system for interstitial fibrosis quantification from kidney USG image. In Aim 2, we will externally validate the performance of the deep learning model using an independent cohort of USG images and kidney biopsies, and evaluate performance across strata of age, gender, and body size. Finally, in Aim 3, we will determine if the USG deep learning-based interstitial fibrosis score is associated with kidney disease progression with similar strengths relative to kidney biopsy assessment of interstitial fibrosis. Upon completion of this program of research, we envision development of an app. / plug-in for ultrasound reading modules that would facilitate widespread dissemination of the deep-learning tool, such that USG-based fibrosis scoring is widely available to treating clinicians.

项目概要间质纤维化是肾活检的常见发现，强烈预示着未来肾功能的下降无论肾脏疾病的根本病因如何。不幸的是，间质纤维化很难被捕获当前肾功能的临床生物标志物（eGFR 和蛋白尿）。因此，间质纤维化很常见，具有重大的预后重要性，但临床医生对其存在或严重程度视而不见，除非在罕见的情况下进行肾脏活检的情况。同时，正在测试限制肾脏损伤的新药间质纤维化，但没有非侵入性方法来评估纤维化随时间的变化。插页式目前，纤维化是通过肾活检的组织病理学检查来估计的，但很少进行这种检查。一个目前尚无评估间质纤维化的非侵入性测试。我们令人兴奋的初步数据证明使用常规肾脏超声检查 (USG)，并通过深度学习/人工解释智力可以无创地评估间质纤维化的存在和严重程度。总体目标是这项研究是为了进一步开发并在内部和外部验证一种基于深度学习的算法来估计肾脏 USG 图像相对于肾活检金标准的间质纤维化。我们假设嵌入肾脏 USG 图像中的是间质纤维化相关物，可以通过深层提取学习和定量分析以高精度估计间质纤维化，并将改进预测肾功能纵向下降。如果是这样，考虑到肾脏超声检查在世界范围内的广泛使用，这间质纤维化的非侵入性评估将具有直接的临床意义预测以及连续监测间质纤维化对治疗的反应的能力。拟议的计划研究将解决三个具体目标：目标 1. 进一步开发和内部验证深度学习- 基于肾脏 USG 图像的间质纤维化定量系统。在目标 2 中，我们将进行外部验证使用独立的 USG 图像和肾脏活检队列的深度学习模型的性能，并评估不同年龄、性别和体型的表现。最后，在目标 3 中，我们将确定是否基于 USG 深度学习的间质纤维化评分与肾脏疾病进展相关，具有相似的相关性相对于肾活检评估间质纤维化的优势。完成本计划后研究后，我们设想开发一个应用程序。 / 超声波读取模块插件，这将有助于深度学习工具的广泛传播，使得基于 USG 的纤维化评分广泛应用于治疗临床医生。