权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Automated retinopathy of prematurity classification using machine learning

使用机器学习对早产儿视网膜病变进行自动分类

基本信息

批准号：
8723225
负责人：
MICHAEL F. CHIANG
金额：
$ 19.89万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-09-01 至 2016-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8723225
关键词：
Address Algorithms Blindness Blood Vessels Caring Characteristics Childhood Classification Clinical Clinical Data Complement Computers Data Data Set Databases Decision Making Development Diagnosis Diagnostic Disease Disease Progression Documentation Early Diagnosis Evaluation Expert Opinion Funding Goals Human Image Image Analysis Infant Machine Learning Measures Medical Medical Imaging Methodology Methods Modeling Monitor Neonatal Intensive Care Units Online Systems Ophthalmologist Ophthalmology Performance Pilot Projects Premature Infant Process Public Health Reading Research Infrastructure Research Personnel Resources Retinal Retinal Diseases Retinopathy of Prematurity Structure System Techniques Telemedicine Testing Time Training Travel Tubular formation United States United States National Institutes of Health Variant base bioimaging biomedical informatics clinical care clinical decision-making clinically significant computer science design disease classification disorder risk experience image processing improved public health relevance research study retina blood vessel structure two-dimensional

项目摘要

DESCRIPTION (provided by applicant): The goal of this project is to develop a web-based, semi-automated system for identifying severe retinopathy of prematurity (ROP) with "plus disease," using an existing data set of retinal images collected from previous NIH-funded research studies. ROP is treatable if diagnosed early, yet continues to be a leading cause of childhood blindness throughout the world. Diagnosis and documentation of ophthalmoscopic findings in ROP are subjective and qualitative, and studies have found that there is often significant diagnostic variation, even when experts are shown the exact same clinical data. Computer-based image analysis and the application of machine learning techniques to feature extraction and image classification have potential to address many of these limitations. Recent advances in image processing have had led to sophisticated techniques for tracing vessel-like structures. Additionally, machine-learning techniques will enable us to leverage these existing annotated image databases to improve the performance of our algorithms for vessel segmentation and disease classification. Our overall hypothesis is that retinal vascular features may be quantified and used to assist clinicians in the diagnosis of ROP. These hypotheses will be tested using two Specific Aims: (1) Develop and evaluate semi-automated algorithms to segment retinal vessels and generate a set of retinal vessel-based features. (2) Develop computer-based decision support algorithms that best correlate with expert opinions. Overall, this project will build upon infrastructure developed from previous studies, create potential for improving the accuracy and consistency of clinical ROP diagnosis, provide a demonstration of computer-based decision support from image analysis during real-world medical care, and stimulate future research toward understanding the vascular features associated with severe ROP. This project will be performed by a multi-disciplinary team of investigators with expertise in ophthalmology, biomedical informatics, computer science, machine learning, and image processing.

描述（由申请人提供）：本项目的目标是开发一个基于网络的半自动系统，用于识别具有“附加疾病”的严重早产儿视网膜病变（ROP），使用从以前NIH资助的研究中收集的视网膜图像的现有数据集。如果早期诊断，ROP是可以治疗的，但仍然是世界各地儿童失明的主要原因。ROP中检眼镜检查结果的诊断和记录是主观和定性的，研究发现，即使向专家展示完全相同的临床数据，也往往存在显著的诊断差异。基于计算机的图像分析和机器学习技术在特征提取和图像分类中的应用有可能解决许多这些限制。图像处理的最新进展已经导致了用于追踪血管样结构的复杂技术。此外，机器学习技术将使我们能够利用这些现有的注释图像数据库来提高我们的血管分割和疾病分类算法的性能。我们的总体假设是，视网膜血管特征可以量化，并用于帮助临床医生诊断ROP。这些假设将使用两个特定目标进行测试：（1）开发和评估半自动算法以分割视网膜血管并生成一组基于视网膜血管的特征。(2)开发基于计算机的决策支持算法，最好与专家意见相关。总的来说，该项目将建立在从以前的研究开发的基础设施上，为提高临床ROP诊断的准确性和一致性创造潜力，在现实世界的医疗护理期间提供基于计算机的决策支持的演示，并刺激未来的研究，以了解与严重ROP相关的血管特征。该项目将由具有眼科学、生物医学信息学、计算机科学、机器学习和图像处理专业知识的多学科研究人员团队执行。