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REPLICATING VISUAL ASSESSMENTS OF 18F-FLORBETABEN PET USING MACHINE LEARNING TECHNIQUE

      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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