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Amyloid imaging has become a clinically important diagnostic tool of Alzheimer’s disease
and related disorders. In many recent studies using amyloid PET, the images were classified
as normal or abnormal based on their standard uptake value ratios(SUVRs). However,
the image processing pipelines to obtain SUVR differ among research centers and are
not readily accessible in clinical practice. Therefore, reading amyloid PET images
in the clinical setting largely depends on visual assessment. In this preliminary
study, we evaluated whether a supervised machine learning algorithm can replicate
the classifications made by human raters blinded to the clinical diagnosis.
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