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New strategies for automated differential diagnosis of degenerative brain disorders.
- Source :
-
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2007; Vol. 2007, pp. 3421-5. - Publication Year :
- 2007
-
Abstract
- New strategies are considered for automated, single-subject differential diagnosis of independent degenerative brain disorders characterized by similar clinical symptoms using functional imaging. The methodology of these strategies is described and its application in parkinsonian movement disorders is illustrated for PET data. Using an automated diagnostic Topographic Profile Rating (TPR) technique based on the Scaled Subprofile Model (SSM-PCA), single-subject score values for different conditions are compared with reference values to predict diagnosis. The discriminatory parameters of reference score sets associated with significant SSM principal components referred to as group invariant subprofiles (GIS networks) are examined. It is shown that the extraction of exclusive sub-networks that stem from contrasting image features between conditions can be an effective tool for optimization that does not require expert knowledge.
- Subjects :
- Diagnosis, Differential
Humans
Reproducibility of Results
Sensitivity and Specificity
Algorithms
Artificial Intelligence
Image Enhancement methods
Image Interpretation, Computer-Assisted methods
Neurodegenerative Diseases diagnostic imaging
Pattern Recognition, Automated methods
Positron-Emission Tomography methods
Subjects
Details
- Language :
- English
- ISSN :
- 2375-7477
- Volume :
- 2007
- Database :
- MEDLINE
- Journal :
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
- Publication Type :
- Academic Journal
- Accession number :
- 18002732
- Full Text :
- https://doi.org/10.1109/IEMBS.2007.4353066