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논문 기본 정보

자료유형
학술저널
저자정보
Simfukwe Chanda (Department of Neurology Chung-Ang University College of Medicine Seoul Korea.) Lee Reeree (Department of Nuclear Medicine Chung-Ang University College of Medicine Seoul Korea.) Youn Young Chul (Department of Neurology Chung-Ang University College of Medicine Seoul Korea.)
저널정보
대한치매학회 Dementia and Neurocognitive Disorders(대한치매학회지) Dementia and Neurocognitive Disorders(대한치매학회지) 제22권 제2호
발행연도
2023.4
수록면
61 - 68 (8page)
DOI
10.12779/dnd.2023.22.2.61

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Background and Purpose: Analyzing brain amyloid positron emission tomography (PET) images to access the occurrence of β-amyloid (Aβ) deposition in Alzheimer’s patients requires much time and effort from physicians, while the variation of each interpreter may differ. For these reasons, a machine learning model was developed using a convolutional neural network (CNN) as an objective decision to classify the Aβ positive and Aβ negative status from brain amyloid PET images. Methods: A total of 7,344 PET images of 144 subjects were used in this study. The 18F-florbetaben PET was administered to all participants, and the criteria for differentiating Aβ positive and Aβ negative state was based on brain amyloid plaque load score (BAPL) that depended on the visual assessment of PET images by the physicians. We applied the CNN algorithm trained in batches of 51 PET images per subject directory from 2 classes: Aβ positive and Aβ negative states, based on the BAPL scores. Results: The binary classification of the model average performance matrices was evaluated after 40 epochs of three trials based on test datasets. The model accuracy for classifying Aβ positivity and Aβ negativity was (95.00±0.02) in the test dataset. The sensitivity and specificity were (96.00±0.02) and (94.00±0.02), respectively, with an area under the curve of (87.00±0.03). Conclusions: Based on this study, the designed CNN model has the potential to be used clinically to screen amyloid PET images.

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