Automated retinopathy of prematurity classification using machine learning

项目总结/摘要 该项目的目标是开发一个基于网络的半自动系统，用于识别严重的视网膜病变， 早产儿（ROP）与“加疾病”，使用现有的数据集的视网膜图像收集自以前的NIH- 资助的研究。如果早期诊断，ROP是可以治疗的，但仍然是儿童的主要原因 全世界的失明。ROP的眼底镜检查结果的诊断和记录是 主观和定性的，研究发现，往往有显着的诊断差异，即使当 专家们看到了完全相同的临床数据。计算机图像分析及其应用 用于特征提取和图像分类的机器学习技术具有解决许多 这些限制。图像处理方面的最新进展已经导致了复杂的追踪技术 血管样结构。此外，机器学习技术将使我们能够利用这些现有的 注释的图像数据库，以提高我们的血管分割和疾病的算法的性能 分类.我们的总体假设是，视网膜血管特征可以量化并用于提供帮助 临床医生对ROP的诊断。这些假设将使用两个具体目标进行检验：（1）发展和 评估分割视网膜血管的半自动算法，并生成一组基于视网膜血管的 功能. (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，该项目将建立在以前研究开发的基础设施上， 提高了临床ROP诊断的准确性和一致性，为计算机辅助ROP诊断提供了示范 在现实世界的医疗保健过程中，从图像分析中获得决策支持，并刺激未来的研究， 了解与严重ROP相关的血管特征。该项目将由多个- 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。

Project Summary/Abstract 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.