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）：该项目的目的是开发一种基于网络的半自动化系统，用于使用从先前的NIH资助研究研究中收集的现有视网膜图像识别具有“加疾病”的严重的早产视网膜病（ROP）。如果早期被诊断出，ROP是可以治疗的，但仍然是全世界儿童失明的主要原因。 ROP中眼镜发现结果的诊断和文献是主观和定性的，研究发现，即使专家显示了完全相同的临床数据，也经常存在明显的诊断差异。基于计算机的图像分析以及机器学习技术以提取和图像分类的应用有可能解决许多此类局限性。图像处理的最新进展导致了追踪类似容器的结构的复杂技术。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算法的性能进行血管分割和疾病分类。我们的总体假设是，可以量化视网膜血管特征，并用于协助临床医生诊断ROP。这些假设将使用两个特定目的进行检验：（1）开发和评估半自动化算法以分割视网膜血管并产生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，这些算法与专家意见最有联系。总体而言，该项目将基于从先前的研究开发的基础设施基础上，创造了提高临床ROP诊断的准确性和一致性的潜力，提供了现实世界中医疗期间图像分析的基于计算机的决策支持，并刺激未来的研究以理解与严重ROP相关的血管特征。该项目将由具有眼科，生物医学信息学，计算机科学，机器学习和图像处理方面的专业知识的研究人员组成的多学科团队进行。