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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2018.12
- 수록면
- 418 - 425 (8page)
이용수
초록· 키워드
오류제보하기
Amyloid brain positron emission tomography (PET) images are visually and subjectively analyzed by the physician with a lot of time and effort to determine the β-Amyloid (Aβ) deposition. We designed a convolutional neural network (CNN) model that predicts the Aβ-positive and Aβ-negative status. We performed 18F-florbetaben (FBB) brain PET on controls and patients (n=176) with mild cognitive impairment and Alzheimer"s Disease (AD). We classified brain PET images visually as per the on the brain amyloid plaque load score. We designed the visual geometry group (VGG16) model for the visual assessment of slice-based samples. To evaluate only the gray matter and not the white matter, gray matter masking (GMM) was applied to the slice-based standard samples. All the performance metrics were higher with GMM than without GMM (accuracy 92.39 vs. 89.60, sensitivity 87.93 vs. 85.76, and specificity 98.94 vs. 95.32). For the patientbased standard, all the performance metrics were almost the same (accuracy 89.78 vs. 89.21), lower (sensitivity 93.97 vs. 99.14), and higher (specificity 81.67 vs. 70.00). The area under curve with the VGG16 model that observed the gray matter region only was slightly higher than the model that observed the whole brain for both slice-based and patient-based decision processes. Amyloid brain PET images can be appropriately analyzed using the CNN model for predicting the Aβ-positive and Aβ-negative status.
목차
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES
참고문헌 (15)
참고문헌 신청
[학술지(정기간행물)] - Barthel H / 2011 / Cerebral amyloid-β PET with florbetaben (18F) in patients with Alzheimer's disease and healthy controls: a multicenter phase 2 diagnostic study / The Lancet Neurology 10 : 424 ~ 435
[학술지(정기간행물)] - Bergstra J / 2012 / Random search for hyper-parameter optimization / Journal of Machine Learning Research 13 : 281 ~ 305
[학술지(정기간행물)] - Choi WH / 2016 / Automated quantification of amyloid positron emission tomography: a comparison of pmod and mimneuro / Annals of Nuclear Medicine 30 : 682 ~ 689
[학술지(정기간행물)] - Gulshan V / 2016 / Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs / JAMA 316 : 2402 ~ 2410
[학술지(정기간행물)] - Tamil Iniyan Gunasekaran / 2015 / MicroRNAs as Novel Biomarkers for the Diagnosis of Alzheimer's Disease and Modern Advancements in the Treatment / 대한의생명과학회지 21 (1) : 1 ~ 8
[학술지(정기간행물)] - Bergstra J / 2012 / Random search for hyper-parameter optimization / Journal of Machine Learning Research 13 : 281 ~ 305
[학술지(정기간행물)] - Choi WH / 2016 / Automated quantification of amyloid positron emission tomography: a comparison of pmod and mimneuro / Annals of Nuclear Medicine 30 : 682 ~ 689
[학술지(정기간행물)] - Gulshan V / 2016 / Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs / JAMA 316 : 2402 ~ 2410
[학술지(정기간행물)] - Tamil Iniyan Gunasekaran / 2015 / MicroRNAs as Novel Biomarkers for the Diagnosis of Alzheimer's Disease and Modern Advancements in the Treatment / 대한의생명과학회지 21 (1) : 1 ~ 8
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UCI(KEPA) : I410-ECN-0101-2019-510-000302727