Molecular Imaging and Magnetic Resonance Imaging in Early Diagnosis of Alzheimer's Disease

2008 ◽  
Vol 21 (6) ◽  
pp. 755-771
Author(s):  
O. Schillaci ◽  
L. Travascio ◽  
C. Bruni ◽  
G. Bazzocchi ◽  
A. Testa ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Imaging ◽  
Magnetic Resonance ◽  
Early Diagnosis ◽  
Neurodegenerative Disorder ◽  
The Elderly ◽  
Resonance Imaging ◽  
New Techniques ◽  
Molecular Imaging Agents

Alzheimer's disease (AD), a progressive neurodegenerative disorder, is the most common cause of dementia in the elderly. Magnetic resonance (MR) or computed tomography (CT) imaging is recommended for routine evaluation of dementias. The development of molecular imaging agents and the new techniques of MR for AD are critically important for early diagnosis, neuropathogenesis studies and assessing treatment efficacy in AD. Neuroimaging using nuclear medicine techniques such as SPECT, PET and MR spectroscopy has the potential to characterize the biomarkers for Alzheimer's disease. The present review summarizes the results of radionuclide imaging and MR imaging in AD.

scholarly journals A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects

2020 ◽  
Vol 9 (7) ◽  
pp. 2146
Author(s):  
Gopi Battineni ◽  
Nalini Chintalapudi ◽  
Francesco Amenta ◽  
Enea Traini
Keyword(s):  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Magnetic Resonance ◽  
Neurodegenerative Disorder ◽  
Support Vector ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

Increasing evidence suggests the utility of magnetic resonance imaging (MRI) as an important technique for the diagnosis of Alzheimer’s disease (AD) and for predicting the onset of this neurodegenerative disorder. In this study, we present a sophisticated machine learning (ML) model of great accuracy to diagnose the early stages of AD. A total of 373 MRI tests belonging to 150 subjects (age ≥ 60) were examined and analyzed in parallel with fourteen distinct features related to standard AD diagnosis. Four ML models, such as naive Bayes (NB), artificial neural networks (ANN), K-nearest neighbor (KNN), and support-vector machines (SVM), and the receiver operating characteristic (ROC) curve metric were used to validate the model performance. Each model evaluation was done in three independent experiments. In the first experiment, a manual feature selection was used for model training, and ANN generated the highest accuracy in terms of ROC (0.812). In the second experiment, automatic feature selection was conducted by wrapping methods, and the NB achieved the highest ROC of 0.942. The last experiment consisted of an ensemble or hybrid modeling developed to combine the four models. This approach resulted in an improved accuracy ROC of 0.991. We conclude that the involvement of ensemble modeling, coupled with selective features, can predict with better accuracy the development of AD at an early stage.

Prevalence of Sinusitis Detected by Magnetic Resonance Imaging in Subjects with Dementia or Alzheimer’s Disease

2015 ◽  
Vol 12 (10) ◽  
pp. 1006-1011 ◽  
Author(s):  
Minori Yasue ◽  
Saiko Sugiura ◽  
Yasue Uchida ◽  
Hironao Otake ◽  
Masaaki Teranishi ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Resonance Imaging
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