Automated image-based biomarker computation tools for diabetic retinopathy
Automated image-based biomarker computation tools for diabetic retinopathy
批准号:
8252674
负责人:
Kaushal Solanki
金额:
$26.09万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2012
资助国家:
美国
项目状态:
已结题
起止时间:
2012-09-01 至 2013-11-30
关键词:
AddressAdoptionAlgorithmsAppearanceAreaBiologicalBiological MarkersBlindnessCaliforniaCaringClassificationClinicClinicalClinical ResearchColorComputer Vision SystemsComputer softwareConsultationsCountyDescriptorDetectionDevelopmentDiabetic RetinopathyEarly identificationEyeFaceFundusFutureGoalsHumanImageImage AnalysisImageryIndividualIndustryInstitutesInterventionJointsLesionLongitudinal StudiesLos AngelesMachine LearningManualsMarketingMeasurementMeasuresMedical centerMethodsMicroaneurysmMonitorOphthalmic examination and evaluationOptic DiskOptometryPatientsPattern RecognitionPhaseProcessPublishingReadingResearchRetinalRetinal DiseasesScreening procedureSmall Business Technology Transfer ResearchSoftware EngineeringSoftware ToolsTimeUniversitiesVisionVisitbaseclinical applicationclinical careclinical practiceclinically significantcohortdesigndiabetic patientdrug discoveryexperienceimage processingimage registrationmacular edemamemberpreventprofessorsuccesstooluser-friendly
中文摘要
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英文摘要
DESCRIPTION (provided by applicant): In this STTR project, we present EyeMark, a set of tools for automated computation of biomarkers for diabetic retinopathy using retinal image photographs. Specifically, we will develop tools for computation of microaneurysm (MA) appearance and disappearance rates (jointly known as turnover rates) for use as a biomarker in monitoring progression of diabetic retinopathy (DR). The availability of a reliable image-based biomarker will have high positive influence on various aspects of DR care, including screening, monitoring progression, drug discovery and clinical research. There is ample published evidence that MA turnover rates are a good predictor of likelihood of progression to more severe retinopathy, establishing MA turnover as an excellent biomarker for diabetic retinopathy. Measuring this quantity involves two steps: careful alignment of current and baseline images, and marking of individual MAs. This process is very time consuming and prone to error, if done by entirely by human graders. The primary goal of this project is to overcome the above limitations by automating both the steps involved in MA turnover measurement: accurate image registration, and MA detection. We will develop end-too-end desktop software for automated computation of MA turnover and also provide intuitive visualization tools for clinicians to more effectively monitor diabetic retinopathy progression.
PUBLIC HEALTH RELEVANCE: The proposed tool will greatly enhance the clinical care available to diabetic retinopathy patients by providing an automated tool for computation of a biomarker in a non-invasive manner. This will enable identification of patients who are more likely to progress to severe retinopathy, thus helping prevent vision loss in such patients by timely intervention. Early identification is especially important in face of long backlog of diabetic patients waiting for an eye examination, and the fact that 90% of vision loss can be saved by early identification. The availability of an effective biomarker will also positively influence the drug discovery process by facilitating early and reliable determination of biological efficacy of potential new therapies.
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项目类别:
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资助金额:$49.08万
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资助金额:$19.95万
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依托单位:
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批准号:9104250
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资助金额:$48.34万
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资助金额:$39.45万
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依托单位:
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项目类别:
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资助金额:$30.0万
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财政年份:2012
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负责人:Kaushal Solanki
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依托单位:
海外基金