ArticleEuropean journal of nuclear medicine and molecular imaging2020
The clinical feasibility of deep learning-based classification of amyloid PET images in visually equivocal cases.
Article in European journal of nuclear medicine and molecular imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Impact of adjunctive quantitative analysis on visual interpretation of amyloid PET: a multiple tracer, multicentre study.European radiology · 2026Article
- Stratifying amyloid burden in early Alzheimer's disease using cascaded attention-guided vision transformer using [¹⁸F]Florbetapir PET.European radiology · 2026Article
- Multimodal MRI accurately identifies amyloid status in unbalanced cohorts in Alzheimer's disease continuum.Network neuroscience (Cambridge, Mass.) · 2025Article
- A mini review of transforming dementia care in China with data-driven insights: overcoming diagnostic and time-delayed barriers.Frontiers in aging neuroscience · 2025Review
- Interrater agreement and variability in visual reading of [18F] flutemetamol PET images.Annals of nuclear medicine · 2025Article
- Quantitative evaluation of striatal uptake ratios using an adaptive template registration method forAnnals of nuclear medicine · 2024Article
- A Comparative Analysis of Two Automated Quantification Methods for Regional Cerebral Amyloid Retention: PET-Only and PET-and-MRI-Based Methods.International journal of molecular sciences · 2024Article
- AmyloidPETNet: Classification of Amyloid Positivity in Brain PET Imaging Using End-to-End Deep Learning.Radiology · 2024Article
- Comparison of consistency in centiloid scale among different analytical methods in amyloid PET: the CapAIBL, VIZCalc, and Amyquant methods.Annals of nuclear medicine · 2024Article
- Comparison of deep learning architectures for predicting amyloid positivity in Alzheimer's disease, mild cognitive impairment, and healthy aging, from T1-weighted brain structural MRI.Frontiers in neuroscience · 2024Article
- Deep learning application for the classification of Alzheimer's disease usingScientific reports · 2023Article
- Neuropsychiatric Symptoms and Commonly Used Biomarkers of Alzheimer's Disease: A Literature Review from a Machine Learning Perspective.Journal of Alzheimer's disease : JAD · 2023Review
- Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation.European journal of nuclear medicine and molecular imaging · 2022Review
- Comparison of Three Automated Approaches for Classification of Amyloid-PET Images.Neuroinformatics · 2022Article
- Direct inference of Patlak parametric images in whole-body PET/CT imaging using convolutional neural networks.European journal of nuclear medicine and molecular imaging · 2022Article
- Automated semi-quantitative amyloid PET analysis technique without MR images for Alzheimer's disease.Annals of nuclear medicine · 2022Article
- The impact of subthreshold levels of amyloid deposition on conversion to dementia in patients with amyloid-negative amnestic mild cognitive impairment.Alzheimer's research & therapy · 2022Article
- [Journal of clinical neurology (Seoul, Korea) · 2022Article
- 60 Years of Achievements by KSNM in Neuroimaging Research.Nuclear medicine and molecular imaging · 2022Review
- Diagnostic performance of deep learning models for detecting bone metastasis on whole-body bone scan in prostate cancer.European journal of nuclear medicine and molecular imaging · 2022Article
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Abstract
purposeAlthough most deep learning (DL) studies have reported excellent classification accuracy, these studies usually target typical Alzheimer's disease (AD) and normal cognition (NC) for which conventional visual assessment performs well. A clinically relevant issue is the selection of high-risk subjects who need active surveillance among equivocal cases. We validated the clinical feasibility of DL compared with visual rating or quantitative measurement for assessing the diagnosis and prognosis of subjects with equivocal amyloid scans.
methods
resultsThe visual reading was positive in 175 cases, equivocal in 54 cases, and negative in 201 cases. The composite SUVR cutoff value was 1.32 (AUC 0.99). The subject-level performance of DL using the test set was 100%. Among the 54 equivocal cases, 37 cases were classified as positive (Eq(deep+)) by DL, 40 cases were classified by a second-round visual assessment, and 40 cases were classified by quantification. The DL- and quantification-based classifications showed good agreement (83%, κ = 0.59). The composite SUVRs differed between Eq(deep+) (1.47 [0.13]) and Eq(deep-) (1.29 [0.10]; P < 0.001). DL, but not the visual rating, showed a significant difference in the Mini-Mental Status Examination score change during the follow-up between Eq(deep+) (- 4.21 [0.57]) and Eq(deep-) (- 1.74 [0.76]; P = 0.023) (mean duration, 1.76 years).
conclusionsIn visually equivocal scans, DL was more related to quantification than to visual assessment, and the negative cases selected by DL showed no decline in cognitive outcome. DL is useful for clinical diagnosis and prognosis assessment in subjects with visually equivocal amyloid scans.
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