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 肾结石的特征是剧集发生使人衰弱的石头事件，这导致了痛苦 通过，临时访问和手术。正确选择医疗和手术治疗取决于准确 评估石材特征，包括大小和位置。当前量化这些方法 特征取决于人类的手动测量，引入了不必要的变化，是 费力，并对临床试验进行的大量成像研究非常困难。 现有的自动测量是专有的，只有细分市场（分区）周围的石头 结构没有考虑其他临床上重要的特征，例如肾积水，并且很慢。批判 有效实施减少肾石岩燃烧的个性化疗法的障碍是缺乏 可以准确测量石材和肾脏特征的诊断成像的自动分析 并实时预测个人发生石材事件的风险，例如赞助石通道。 在这个研究项目中，费城儿童医院（CHOP）和宾夕法尼亚大学 （Penn）泌尿外科机器学习中心（CMLU）在机器专家之间建立了合作 学习诊断成像，临床流行病学和良性泌尿科疾病。我们以最新为基础 发现机器学习（部分深度学习）准确，可靠地发现了诊断图像 迅速预测疾病风险层和结果。该项目使用CT的机器学习来自动化 测量石头（例如大小，位置和形状）和肾解剖学（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石的自发通道 对于整个生命周期的个人。为此，拟议的研究将开发临床上有用的开放式访问 预测工具将改变量化尿石的标准，并以完全自动化的方式， 准确，可靠，迅速鉴定有输尿管结石的患者，最有可能受益于早期手术 干涉。在AIM 1中，我们将使用深度学习自动细分并测量常规功能 在 分别在Chop和Penn评估的2,000名儿童和成人的CT图像。在AIM 2中，我们将使用深度 学习从CT图像中提取信息的功能，这些特征可预测723独特的输尿管石通道 儿童和成人。这些功能包括常规功能，工程功能和深度学习功能 这可能不会被人类衡量，也不能被人衡量。这些结果将转变 临床护理和研究，并为那些最有可能从早期选修中受益的人提供见解 手术以去除石头，以防止将来的疼痛和紧急访问。