Center for Machine Learning in Urology-Scientific Project

泌尿科机器学习中心科学项目

• 批准号: 10260579
• 负责人: Yong Fan
• 金额: $ 20.59万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 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 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10260579
• 关键词: Adult; Age; Alzheimer' s Disease; Anatomy; Benign; Body mass index; Brain; Characteristics; Child; Clinical; Clinical Research; Clinical Trials; Collaborations; Diagnosis; Diagnostic Imaging; Emergency department visit; Engineering; Event; Functional disorder; Future; Goals; Gold; Human; Hydronephrosis; Image; Individual; Kidney; Kidney Calculi; Lead; Location; Longevity; Machine Learning; Magnetic Resonance Imaging; Manuals; Measurement; Measures; Medical; Methods; Nephrolithiasis; Operative Surgical Procedures; Outcome; Pain; Patients; Pattern; Pediatric Hospitals; Pennsylvania; Performance; Philadelphia; Prediction of Response to Therapy; Predictive Value; Probability; Research; Research Project Grants; Risk; Shapes; Skin; Structure; Time; Ultrasonography; Universities; Urinary Calculi; Urinary tract; Urologic Diseases; Urology; Variant; Visit; X-Ray Computed Tomography; automated algorithm; automated analysis; clinical care; clinical epidemiology; clinical predictors; deep learning; deep learning algorithm; density; disorder risk; imaging study; improved; individualized medicine; insight; machine learning algorithm; mild cognitive impairment; outcome prediction; point of care; prevent; risk stratification; tool; tumor; unnecessary treatment

PROJECT SUMMARY Kidney stones are characterized by the episodic occurrence of debilitating stone events, which lead to painful passage, emergent visits, and surgery. Proper selection of medical and surgical treatments depends on accurate assessment of stone characteristics, including size and location. Current methods for quantifying these characteristics depend on manual measurement by humans, which introduces unnecessary variation, is laborious, and makes analyzing the large number of imaging studies performed for clinical trials very difficult. Existing automated measurements are proprietary, only segment (partition) the stone from the surrounding structures without considering other clinically important features such as hydronephrosis, and are slow. A critical barrier to effectively implementing individualized therapies that decrease the burden of nephrolithiasis is the lack of automated analyses of diagnostic imaging that could accurately measure stone and kidney characteristics, and predict, in real time, an individual’s risk of stone events, such as spontaneous stone passage. In this Research Project, the Children’s Hospital of Philadelphia (CHOP) and the University of Pennsylvania (Penn) Center for Machine Learning in Urology (CMLU) forges a collaboration among experts in machine learning of diagnostic imaging, clinical epidemiology, and benign urologic disease. We build upon our recent discoveries that machine learning (particularly deep learning) of diagnostic images accurately, reliably, and rapidly predicts disease risk strata and outcomes. This project uses machine learning of CT to automate measurement of conventional characteristics of stones (e.g. size, location, and shape) and renal anatomy (e.g. hydronephrosis, ureteral dilation). We then apply this method to predict spontaneous passage of ureteral stones for individuals across the lifespan. In doing so, the proposed studies will develop clinically useful open-access prediction tools that will transform the standard of quantifying urinary stones and, in a fully automated way, accurately, reliably, and rapidly identify patients with ureteral stones most likely to benefit from early surgical intervention. In Aim 1, we will use deep learning to automatically segment and measure conventional features of urinary stones (e.g. size, density) and adjacent renal and ureteral anatomy (e.g. degree of hydronephrosis) in CT images of 2,000 children and adults evaluated at CHOP and Penn, respectively. In Aim 2, we will use deep learning to extract informative features from CT images that predict ureteral stone passage for 723 unique children and adults. The features include conventional features, engineered features, and deep-learning features that may neither be appreciated by nor be able to be measured by humans. These results would transform clinical care and research and provide insights into those who would be most likely to benefit from early elective surgery to remove stones to prevent future pain and emergent visits.

项目摘要 肾结石的特征在于使人衰弱的结石事件的偶发性发生，这导致疼痛的 通过，紧急访问，和手术。正确选择医疗和手术治疗取决于准确的 评估结石特征，包括大小和位置。目前量化这些的方法 特性依赖于人工测量，这引入了不必要的变化， 费力，并且使得分析为临床试验进行的大量成像研究非常困难。 现有的自动化测量是专有的，只能将石头从周围分割（分区） 在不考虑其他临床重要特征如肾积水的情况下，一个关键 有效实施降低肾结石负担的个体化治疗的障碍是缺乏 诊断成像的自动分析可以准确测量结石和肾脏的特征， 并在真实的时间内预测个体的结石事件风险，例如自发结石通过。 在这个研究项目中，费城儿童医院（CHOP）和宾夕法尼亚大学 （宾夕法尼亚大学）泌尿学机器学习中心（CMLU）在机器学习领域的专家之间建立了合作 学习影像学诊断、临床流行病学和良性泌尿系统疾病。我们建立在我们最近的 发现机器学习（特别是深度学习）诊断图像准确，可靠， 快速预测疾病风险分层和结果。该项目使用CT的机器学习来自动化 测量结石的常规特征（例如，大小、位置和形状）和肾脏解剖结构（例如， 肾积水、输尿管扩张）。然后，我们应用这种方法来预测输尿管结石的自发通过 在整个生命周期中的作用。在这样做的过程中，拟议的研究将开发临床有用的开放获取 预测工具，将改变量化尿结石的标准，并以完全自动化的方式， 准确、可靠、快速地识别最有可能受益于早期手术的输尿管结石患者 干预在目标1中，我们将使用深度学习来自动分割和测量常规特征 泌尿系结石（例如大小、密度）和邻近肾脏和输尿管解剖结构（例如肾积水程度） 分别在CHOP和Penn评估的2，000名儿童和成人的CT图像。在目标2中，我们将使用深度 学习从CT图像中提取信息特征，预测723例独特的输尿管结石通过 儿童和成人。功能包括常规功能、工程功能和深度学习功能 人类可能既不理解也无法测量。这些结果将改变 临床护理和研究，并提供洞察那些最有可能受益于早期选择性 手术以清除结石，以防止未来的疼痛和紧急访问。