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New Computer-Aided Diagnosis of Dementia Using Positron Emission Tomography: Brain Regional Sensitivity-Mapping Method
Author(s) -
Akihiro Kakimoto,
Yuichi Kamekawa,
Shigeru Ito,
Etsuji Yoshikawa,
Hiroyuki Okada,
Sadahiko Nishizawa,
Satoshi Minoshima,
Yasuomi Ouchi
Publication year - 2011
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0025033
Subject(s) - dementia , positron emission tomography , nuclear medicine , cognitive impairment , medicine , sensitivity (control systems) , alzheimer's disease , artificial intelligence , pattern recognition (psychology) , disease , computer science , engineering , electronic engineering
Purpose We devised a new computer-aided diagnosis method to segregate dementia using one estimated index (Total Z score) derived from the Brodmann area (BA) sensitivity map on the stereotaxic brain atlas. The purpose of this study is to investigate its accuracy to differentiate patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) from normal adults (NL). Methods We studied 101 adults (NL: 40, AD: 37, MCI: 24) who underwent 18 FDG positron emission tomography (PET) measurement. We divided NL and AD groups into two categories: a training group with (Category A) and a test group without (Category B) clinical information. In Category A, we estimated sensitivity by comparing the standard uptake value per BA (SUVR) between NL and AD groups. Then, we calculated a summated index (Total Z score) by utilizing the sensitivity-distribution maps and each BA z-score to segregate AD patterns. To confirm the validity of this method, we examined the accuracy in Category B. Finally, we applied this method to MCI patients. Results In Category A, we found that the sensitivity and specificity of differentiation between NL and AD were all 100%. In Category B, those were 100% and 95%, respectively. Furthermore, we found this method attained 88% to differentiate AD-converters from non-converters in MCI group. Conclusions The present automated computer-aided evaluation method based on a single estimated index provided good accuracy for differential diagnosis of AD and MCI. This good differentiation power suggests its usefulness not only for dementia diagnosis but also in a longitudinal study.

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