YEARS

2002-2007

AUTHORS

Michael H Goldbaum

TITLE

Medical Advice from Glaucoma Informatics (MAGI)

ABSTRACT

DESCRIPTION (provided by applicant): The project, Medical Advice from Glaucoma Informatics (MAGI), seeks to improve glaucoma diagnosis and management with state-of-the-art machine learning classifiers. These classifiers will automate the interpretation of standard automated perimetry (SAP), newer visual field tests, and structural tests for glaucoma in the general population and in stratified glaucoma populations. Phase 1 will complete the feasibility testing already underway. Phase 2 will apply the refined methods to a wider set of glaucoma testing problems.The management of glaucoma depends on a series of classifications. The glaucoma provider classifies tests as normal or indicative of glaucoma. The clinician then determines whether an eye has glaucoma or has had progression. Assembling these classifications, the provider makes decisions about management. Automated test interpreters, either as part of the testing machine or as a computer-based resource, can aid glaucoma providers with real-time interpretations. The research we propose takes advantage of our extensive data sets and builds on the ongoing research in our laboratories.Statistical classifiers, Bayesian nets, machine learning classifiers, and expert systems represent different types of classifiers with diverse properties. Machine learning classifiers can perform exceptionally well at identifying classes, even when the data are complex and have dependencies. We will test and select the optimal machine learning classifier for diagnosis. We will further improve classifier performance and determine feature utility by optimizing the feature set visual field tests are time consuming and stressful. We will streamline the tests by removing unimportant test points.Even with decades of experience, there is uncertainty with regard to the evaluation of the SAP. There is less accumulated knowledge about non-standard tests, such as short-wavelength automated perimetry, nerve fiber layer thickness, or optic nerve head topography. Machine classifiers may learn how to interpret nonstandard tests better. We will go beyond STATPAC's capabilities with classifiers that have learned to interpret SAP, nonstandard visual field tests, structural glaucoma tests, and STATPAC plots in the general population and in patients stratified by race, family history, and other information available at the time of the test.Conversion of suspects to glaucoma and progression of glaucoma cannot yet be predicted from tests. We will develop classifiers for these predictions. Classifiers will be designed to diagnose early glaucoma, detect early progression, and identify glaucomatous eyes at risk of progression.Unsupervised learning provides cluster analysis that can determine distinct groups with members in some way similar from the test data. In an effort to discover new and use useful information with unsupervised learning, we will mine our data in visual function and structural tests for glaucoma and in specific combinations of population groups.

FUNDED PUBLICATIONS

  • Unsupervised machine learning with independent component analysis to identify areas of progression in glaucomatous visual fields.
  • Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects.
  • Combining functional and structural tests improves the diagnostic accuracy of relevance vector machine classifiers.
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    22 TRIPLES      17 PREDICATES      23 URIs      9 LITERALS

    Subject Predicate Object
    1 grants:e2514b5593a6a56ac3a158fae30b7170 sg:abstract DESCRIPTION (provided by applicant): The project, Medical Advice from Glaucoma Informatics (MAGI), seeks to improve glaucoma diagnosis and management with state-of-the-art machine learning classifiers. These classifiers will automate the interpretation of standard automated perimetry (SAP), newer visual field tests, and structural tests for glaucoma in the general population and in stratified glaucoma populations. Phase 1 will complete the feasibility testing already underway. Phase 2 will apply the refined methods to a wider set of glaucoma testing problems.The management of glaucoma depends on a series of classifications. The glaucoma provider classifies tests as normal or indicative of glaucoma. The clinician then determines whether an eye has glaucoma or has had progression. Assembling these classifications, the provider makes decisions about management. Automated test interpreters, either as part of the testing machine or as a computer-based resource, can aid glaucoma providers with real-time interpretations. The research we propose takes advantage of our extensive data sets and builds on the ongoing research in our laboratories.Statistical classifiers, Bayesian nets, machine learning classifiers, and expert systems represent different types of classifiers with diverse properties. Machine learning classifiers can perform exceptionally well at identifying classes, even when the data are complex and have dependencies. We will test and select the optimal machine learning classifier for diagnosis. We will further improve classifier performance and determine feature utility by optimizing the feature set visual field tests are time consuming and stressful. We will streamline the tests by removing unimportant test points.Even with decades of experience, there is uncertainty with regard to the evaluation of the SAP. There is less accumulated knowledge about non-standard tests, such as short-wavelength automated perimetry, nerve fiber layer thickness, or optic nerve head topography. Machine classifiers may learn how to interpret nonstandard tests better. We will go beyond STATPAC's capabilities with classifiers that have learned to interpret SAP, nonstandard visual field tests, structural glaucoma tests, and STATPAC plots in the general population and in patients stratified by race, family history, and other information available at the time of the test.Conversion of suspects to glaucoma and progression of glaucoma cannot yet be predicted from tests. We will develop classifiers for these predictions. Classifiers will be designed to diagnose early glaucoma, detect early progression, and identify glaucomatous eyes at risk of progression.Unsupervised learning provides cluster analysis that can determine distinct groups with members in some way similar from the test data. In an effort to discover new and use useful information with unsupervised learning, we will mine our data in visual function and structural tests for glaucoma and in specific combinations of population groups.
    2 sg:endYear 2007
    3 sg:fundingAmount 1787460.0
    4 sg:fundingCurrency USD
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    10 sg:hasFundedPublication articles:4c50e7dfa7813a5f6b56defc58aa063a
    11 articles:519e4d0ec98016660effadd08e77835b
    12 articles:dc488288e2c885fe40c3676327820d35
    13 sg:hasFundingOrganization grid-institutes:grid.280030.9
    14 sg:hasRecipientOrganization grid-institutes:grid.266100.3
    15 sg:language English
    16 sg:license http://scigraph.springernature.com/explorer/license/
    17 sg:scigraphId e2514b5593a6a56ac3a158fae30b7170
    18 sg:startYear 2002
    19 sg:title Medical Advice from Glaucoma Informatics (MAGI)
    20 sg:webpage http://projectreporter.nih.gov/project_info_description.cfm?aid=6937074
    21 rdf:type sg:Grant
    22 rdfs:label Grant: Medical Advice from Glaucoma Informatics (MAGI)
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