Role of Artificial Intelligence Techniques (Automatic Classifiers) in Molecular Imaging Modalities in Neurodegenerative Diseases

Author(s): Silvia Cascianelli, Michele Scialpi, Serena Amici, Nevio Forini, Matteo Minestrini, Mario Luca Fravolini, Helmut Sinzinger, Orazio Schillaci, Barbara Palumbo.

Journal Name: Current Alzheimer Research

Volume 14 , Issue 2 , 2017

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Abstract:

Artificial Intelligence (AI) is a very active Computer Science research field aiming to develop systems that mimic human intelligence and is helpful in many human activities, including Medicine. In this review we presented some examples of the exploiting of AI techniques, in particular automatic classifiers such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Classification Tree (ClT) and ensemble methods like Random Forest (RF), able to analyze findings obtained by positron emission tomography (PET) or single-photon emission tomography (SPECT) scans of patients with Neurodegenerative Diseases, in particular Alzheimer’s Disease. We also focused our attention on techniques applied in order to preprocess data and reduce their dimensionality via feature selection or projection in a more representative domain (Principal Component Analysis – PCA – or Partial Least Squares – PLS – are examples of such methods); this is a crucial step while dealing with medical data, since it is necessary to compress patient information and retain only the most useful in order to discriminate subjects into normal and pathological classes. Main literature papers on the application of these techniques to classify patients with neurodegenerative disease extracting data from molecular imaging modalities are reported, showing that the increasing development of computer aided diagnosis systems is very promising to contribute to the diagnostic process.

Keywords: Alzheimer’s Disease, computer aided diagnosis, dementia, machine learning, molecular imaging, Parkinson’s Disease, PET, SPECT.

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Article Details

VOLUME: 14
ISSUE: 2
Year: 2017
Page: [198 - 207]
Pages: 10
DOI: 10.2174/1567205013666160620122926
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