Neural-Network-Based Classification of Cognitively Normal, Demented, Alzheimer Disease and Vascular Dementia from Single Photon Emission with Computed Tomography Image Data from Brain

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 92; no. 12; pp. 5530 - 5534
• Main Authors: Rui J. P. de Figueiredo, Shankle, W. Rodman, Maccato, Andrea, Dick, Malcolm B., Mundkur, Prashanth, Mena, Ismael, Cotman, Carl W.
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences of the United States of America 06-06-1995; National Acad Sciences; National Academy of Sciences
• Subjects: Aged; Aged, 80 and over; Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Alzheimers disease; Brain; Brain - diagnostic imaging; Coefficients; Dementia; Dementia, Vascular - diagnostic imaging; Female; Graphics; Humans; Male; Mathematical vectors; Medical research; Middle Aged; Neural Networks (Computer); Neurology; Older adults; Older people; Organotechnetium Compounds; Oximes; Photons; Scientific imaging; Single photon emission computed tomography; Technetium Tc 99m Exametazime; Tomography, Emission-Computed, Single-Photon; Vascular dementia
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.92.12.5530

Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.