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Computer-assisted system for diagnosis of Alzheimer disease using data base- independent estimation and fluorodeoxyglucose- positron-emission tomography and 3D-stereotactic surface projection


Uemura, T., Ishii, K., Miyamoto, N., Yoshikawa, T.


AJNR. American Journal Of Neuroradiology, Volume: 32, No.: 3, Pages.: 556-559

Year of Publication



Background and Purpose: Recently, voxel-based statistical parametric images have been developed as additional diagnostic tools for AD. However these methods require the generation of a data base of healthy brain images. The purpose of this study was to produce and evaluate an automatic method using a data base-independent estimation system for the diagnosis of mild AD.; Materials and Methods: We retrospectively selected 66 subjects, including 33 patients with early AD and 33 age-matched healthy volunteers. Individual brain metabolic images were obtained by using FDG-PET. These were transformed by using 3D-SSP. We then produced CADDIES, which compares the parietal and sensorimotor metabolic counts by using t tests. If parietal metabolism was significantly lower than the sensorimotor metabolism, the subject was automatically diagnosed as having AD. The FDG-PET images were also analyzed by using a previous automatic diagnosis system (CAAD) that is dependent on the construction of a “normal data base” of healthy brain images. Diagnostic performance was compared between the 2 methods.; Results: The CADDIES demonstrated a sensitivity of 88%, specificity of 79%, and accuracy of 85%, while the CAAD system demonstrated a sensitivity of 70%, specificity of 94%, and accuracy of 82%. The area under the ROC curve of CADDIES was 0.964. The areas under ROC curves of the CAAD method in the parietal and posterior cingulate gyri were 0.843 and 0.939, respectively.; Conclusions: The CADDIES method demonstrated a diagnostic accuracy similar to that of the CAAD system. Our results indicate that this method can be applied to the detection of patients with early AD in routine clinical examinations, with the benefit of not requiring the generation of a normal data base.;

Bibtex Citation

@article{Uemura_2011, doi = {10.3174/ajnr.a2342}, url = {}, year = 2011, month = {feb}, publisher = {American Society of Neuroradiology ({ASNR})}, volume = {32}, number = {3}, pages = {556--559}, author = {T. Uemura and K. Ishii and N. Miyamoto and T. Yoshikawa}, title = {Computer-Assisted System for Diagnosis of Alzheimer Disease Using Data Base- Independent Estimation and Fluorodeoxyglucose- Positron-Emission Tomography and 3D-Stereotactic Surface Projection}, journal = {American Journal of Neuroradiology} }


aged, algorithms, alzheimer disease, artificial intelligence, brain, cluster analysis, computer graphics, computer simulation, databases factual, diagnostic, diagnostic use, fluorodeoxyglucose f18, humans, image enhancement, imaging threedimensional, information storage and retrieval, male, methods, models biological, models statistical, numerical analysis computerassisted, pattern recognition automated, performance, radionuclide imaging, radiopharmaceuticals, reproducibility of results, sensitivity and specificity, signal processing computerassisted, subtraction technique

Countries of Study


Types of Dementia

Alzheimer’s Disease

Types of Study


Type of Outcomes


Type of Interventions

Diagnostic Target Identification

Diagnostic Targets

Neuroimaging (e.g. MRI, PET, CAT etc.)