## 4 Quantitative Methods for the Detection of AD  
In current clinical practice, both the patient's medical history and the physician's neuropsychological examination are the primary means of establishing a diagnosis of dementia; however, clinical diagnosis comes very late in AD progression [1]. [PERSON] et al., discovered that the diagnosis of AD by specialist physicians in living patients is at best modestly sensitive (70.9-87.3%) and generally nonspecific (44.3-70.8%) over a wide range of clinical diagnostic criteria. Diagnostic accuracy was found to be particularly poor in the cases of mild early AD progression [2]. Even post-mortem autopsy-based diagnosis can vary across laboratories due to significant variations in interobserver agreement, which shows a depreciation in the gold standard of autopsy for neuropathological evaluation [2; 3; 4]. Patients with MCI possess biomarker pathology that partially overlaps that of healthy controls and AD patients, which further complicates the diagnosis of AD by introducing the problem of prognosticating cognitive decline in this potentially prodormal stage [5; 6; 7; 8]. DSDs also typically have a broad overlap in their clinical presentation at earlier stages, which further complicates the differential diagnosis. When neuroimaging studies are ordered, it is typically for the differential diagnosis of DSDs. These are also routinely evaluated by a physician's subjective impression of tracer uptake throughout the brain rather than objective data-driven analysis. In view of the modest accuracy of the clinical evaluation, this is insufficient to fully exploit the available potential of disease-modifying therapies, especially since the recent and controversial approval of Aduhelm(r) (tradename: aducanumab) [16; 17]. Glucose metabolism and in vivo quantification of proteinopathies have proven themselves in the literature to be well-established ways of detecting AD pathology and in prognosticating cognitive decline with a variety of data-driven methodologies. For the most accurate estimation of cerebral metabolic rates, the gold standard of quantitative PET is dynamic acquisition (timeseries of 3D volumes) analyzed using kinetic modelling. Since there is no legitimate reference tissue (i.e., brain regions devoid of tracer uptake), arterial blood samples are also required to produce a plasma input function. Such methods are exceptionally complicated and time-consuming in the context of clinical applicability. They also introduce additional burden and cost to the facility and the subject due to the requirement for longer PET studies (dynamic multi-frame acquisitions) and arterial line insertion. Blood sampling is also known for having methodological difficulties such as scanner-to-well-counter calibration and bias/noise in high performance liquid chromatography counting of the parent radioisotope fraction. Kinetic studies also suffer from the possibility of multiple confounds such as motion-induced errors in attenuation correction, partial volume effects from the limited spatial resolution of PET devices, age-related effects on neuroreceptor ligand binding, intrasubject variation introducing issues with coregistration, the normalization of PET-MR images, in vivo radiotracer metabolism, and the effects of genetics on neuroreceptor ligand binding. Simplified semi-quantitative models are therefore preferable, such as the use of standard uptake values (SUV) or use the ratio of this value to a standardized reference region such as the pons as a surrogate for elaborate kinetic modelling (SUVr). SUV/SUVr methods also have the advantage of being useful with static (single frame) PET studies, which are quicker and less costly to acquire compared to dynamic PET studies [73; 74; 75]. Of particular interest to the current article is the use of machine/deep learning methods that consider whole image data in conjunction with genetic, demographic, and/or clinical data without resorting to kinetic modelling.