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

• 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
• 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

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.

项目摘要 间质纤维化是肾脏活检的普遍发现，并强烈预测肾功能的未来下降 不论肾脏疾病的潜在病因。不幸的是，间质性纤维化的捕获不佳 当前的肾功能临床生物标志物（EGFR和蛋白尿）。因此，间质性纤维化很常见， 具有实质性的预后重要性，但是临床医生对其存在或严重程度视而不见 进行肾脏活检的实例。同时，正在测试新药以限制肾脏 间质纤维化，但没有非侵入性的方法来评估纤维化随着时间的变化。间隙 目前，通过对肾脏活检的组织病理学检查估计，纤维化很少进行。一个 目前尚不可用进行估计间质纤维化的非侵入性测试。我们令人兴奋的初步数据 证明了肾脏常规超声检查（USG），通过深度学习/人造解释 智力可以非侵入性评估间质纤维化的存在和严重程度。总体目标 这项研究是为了进一步发展，内部和外部验证一种基于深度学习的算法以估计 从肾脏的USG图像到肾脏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核层，可以通过深处提取 学习和定量分析以高精度估算间质纤维化，并将改善预测 肾功能下降的纵向下降。如果是这样，鉴于全球肾脏USG的广泛可用性 间质性纤维化的非侵入性估计值将立即具有临床意义，并有所改善 预后，以及响应治疗的串行监测间质纤维化的能力。拟议的计划 研究将针对三个具体目标：目标1。进一步发展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AIM 2中，我们将在外部验证 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， 并评估年龄，性别和身体大小的阶层的表现。最后，在AIM 3中，我们将确定是否 USG深度学习的间质纤维化评分与肾脏疾病进展有关 相对于肾脏活检评估间隙纤维化的优势。完成此计划后 研究，我们设想开发应用程序。 /用于超声阅读模块的插件，可以促进 深度学习工具的广泛传播，因此，基于USG的纤维化评分可广泛使用 治疗临床医生。