YEARS

2013-2016

AUTHORS

Jayashree Kalpathy-Cramer, Michael Chiang

TITLE

Automated retinopathy of prematurity classification using machine learning

ABSTRACT

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.

FUNDED PUBLICATIONS

  • Dealing with inter-expert variability in retinopathy of prematurity: A machine learning approach.
  • Manifold Learning by Preserving Distance Orders.
  • Plus disease diagnosis in retinopathy of prematurity: vascular tortuosity as a function of distance from optic disk.
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    22 TRIPLES      17 PREDICATES      23 URIs      9 LITERALS

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    1 grants:72d44ae122c702c8b2f49decbf1a57ee sg:abstract 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.
    2 sg:endYear 2016
    3 sg:fundingAmount 482448.0
    4 sg:fundingCurrency USD
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    15 sg:language English
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    17 sg:scigraphId 72d44ae122c702c8b2f49decbf1a57ee
    18 sg:startYear 2013
    19 sg:title Automated retinopathy of prematurity classification using machine learning
    20 sg:webpage http://projectreporter.nih.gov/project_info_description.cfm?aid=8723225
    21 rdf:type sg:Grant
    22 rdfs:label Grant: Automated retinopathy of prematurity classification using machine learning
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