###### Abstract  
The dementia spectrum is a broad range of disorders with complex diagnosis, pathophysiology, and a limited set of treatment options, where the most common variety is Alzheimer's disease (AD). Positron emission tomography (PET) has become a valuable tool for the detection of AD; however, following the results of post-mortem studies, AD diagnosis has modest sensitivity and specificity at best. It remains common practice that readings of these images are performed by a physician's subjective impressions of the spatial pattern of tracer uptake, and so quantitative methods based on established biomarkers have had little penetration into clinical practice. The present study is a review of the data-driven methods available for molecular neuroimaging studies (fluorodeoxyglucose-/amyloid-/tau-PET), with emphasis on the use of machine/deep learning as quantitative tools complementing the specialist in detecting AD. This work is divided into two broad parts. The first covers the epidemiology and pathology of AD, followed by a review of the role of PET imaging and tracers for AD detection. The second presents quantitative methods used in the literature for detecting AD, including the general linear model and statistical parametric mapping, 3D stereotactic surface projection, principal component analysis, scaled subprofile modeling, support vector machines, and neural networks.  
deep learning; machine learning; neuroimaging; PET; Alzheimer's disease; statistical parametric mapping; scaled subprofile modeling; stereotactic surface projection; support vector machine; neural network +
Footnote †: journal: Medical Imaging  
impairment (MCI) further complicates the diagnostic task because approximately 20% of all MCI patients progress to develop some form of dementia with 3-5 years of diagnosis (pMCI), while the balance will remain stable and see a plateau in their cognitive decline (sMCI) [5,6].  
Distinct alterations in the basal rates of glucose metabolism have been found to precede and coincide with the clinical manifestation of dementia-related cognitive symptoms [5, 6, 7, 8]. Patients suspected of having AD may undergo brain positron emission tomography (PET) imaging with a variety of tracers; however, these images are commonly evaluated by a physician's subjective impression of tracer uptake instead of being subject to quantitative analysis. Data-driven neuroimaging has great potential to alleviate the issue of diagnostic inaccuracy and increase confidence in clinical decision-making by providing objective quantitative metrics to complement the specialist physician, but thus far it has seen limited application in clinical practice [9, 10, 11].  
This article presents a detailed review of the use of data-driven methods to detect AD neuropathology from several varieties of PET neuroimaging studies, with an emphasis on the application of machine/deep learning methods to complement clinical evaluation. We first present an overview of AD's epidemiology, pathology, and the most widely recognized biomarkers. Next, we survey several PET tracers that are useful for the detection of AD in the context of AD as a progressive proteinopathic disorder. Finally, we detail the quantitative methods useful for detecting AD from PET studies and survey the representative literature for each of these methods.