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The Image- and Data Quality Ontology for scientificdata management
Thomas Schrader, Yao Zhou, Sonja Niepage, Claudia Hahn,Sabine Hanss
University of Applied Sciences Brandenburg, Department of Pathology Charité, Departmentof Medical Informatics Charité
June 8, 2012
Outline
1 Background
2 Material & Methods
3 Results
4 Discussion
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Background
Outline
1 Background
2 Material & Methods
3 Results
4 Discussion
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Background
Secundary Use of Clinical Data
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Background
Discusion about data & image quality
Usage of data in various use cases - study recruitment, patternanalysis, exploratory data analysisImages in different contexts - diagnostic process, second opinion,education, image analysis, follow upDifferent views of clinicians, scientists and techniciansDifferent responsibilities in data life cycle
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Material & Methods
Outline
1 Background
2 Material & Methods
3 Results
4 Discussion
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Material & Methods
Development Ontology
Delphi-Method - Discusion and determination of quality criteriaAnalysis of quality criteria - calculation of numeric qualityindicatorsModeling the ontology via Protegé
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Material & Methods
Metadata Analysis
3461 DICOM-image from 3 Radiology Departments822 DICOM-images from experiments with a phantom2639 anonymized,randomly selected images
5 different Computer TomographsSOMATON Sensation (Siemens)SOMATON Definition AS+ (Siemens)MX-16 (Philips)Brilliance (Philips)Brightspeed (GE)
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Results
Outline
1 Background
2 Material & Methods
3 Results
4 Discussion
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Results
Data Life Cycle
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Results
Roles in Data Life Cycle
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Results
Facets of data quality
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Results
Data types
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Results
Methods of Quality Analsis of Images
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Results
Quality Dimensions
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Results
Quality Dimension: Free of Error
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Results
Quality Dimension: Completeness.Resolution
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Results
Quality Dimension: Completeness
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Results
Completeness.Metadata: used metadata
Figure: Number of shared metadata
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Results
Completeness.Metadata - shared metadata
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Discussion
Outline
1 Background
2 Material & Methods
3 Results
4 Discussion
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Discussion
Thinking about Quality
Quality is not equal quality - essential differentiation of qualitydimensionQuality & use caseWho is responsible for what? - data life cycle & rolesSpecification of standardization aim - technolocial aspects vs.content vs. quality dimension
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Discussion
Framework for Analysis of Data Quality
Y. Zhou, S. Hanß, M. Cornils, C. Hahn, S. Niepage, T. Schrader. A SOA-based Data Quality Assessment Framework in a Medical
Science Center. ICIQ Potsdam, 2009
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Discussion
Take Home Message
OpEN.SC Ontology presents quality criteria and their relationsData Life CycleRolesUse casesRessources & devices
TransparencyImplementability
Specific requirementsAvailability of an algorithmsAllocation of quality indicators
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Discussion
Thank you very much for your attention
Any questions?Contact
Mail: [email protected]: ++49+17696906763Tel.: +49 (0)3381-355 423
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