ArticleEuropean journal of nuclear medicine and molecular imaging2021
Visual interpretation of [
Article in European journal of nuclear medicine and molecular imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 29 citations in OpenAlex.
- Impact of adjunctive quantitative analysis on visual interpretation of amyloid PET: a multiple tracer, multicentre study.European radiology · 2026Article
- Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models.Communications chemistry · 2025Article
- Voxel-based evaluation for [Quantitative imaging in medicine and surgery · 2024Article
- Impact of shortening time on diagnosis ofEJNMMI research · 2024Article
- New objective simple evaluation methods of amyloid PET/CT using whole-brain histogram and Top20%-Map.Annals of nuclear medicine · 2024Article
- AmyloidPETNet: Classification of Amyloid Positivity in Brain PET Imaging Using End-to-End Deep Learning.Radiology · 2024Article
- Deep Learning-Driven Estimation of Centiloid Scales from Amyloid PET Images withBrain sciences · 2024Article
- Validation of quantitative assessment of florbetaben PET scans as an adjunct to the visual assessment across 15 software methods.European journal of nuclear medicine and molecular imaging · 2023Article
- International Nuclear Medicine Consensus on the Clinical Use of Amyloid Positron Emission Tomography in Alzheimer's Disease.Phenomics (Cham, Switzerland) · 2023Article
- Estimation of brain amyloid accumulation using deep learning in clinical [EJNMMI physics · 2023Article
- Fast and Accurate Amyloid Brain PET Quantification Without MRI Using Deep Neural Networks.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2023Article
- Automated semi-quantitative amyloid PET analysis technique without MR images for Alzheimer's disease.Annals of nuclear medicine · 2022Article
- 60 Years of Achievements by KSNM in Neuroimaging Research.Nuclear medicine and molecular imaging · 2022Review
- A Brief History of Nuclear Medicine Physics, Instrumentation, and Data Sciences in Korea.Nuclear medicine and molecular imaging · 2021Review
- Influence of Physical Activity Levels and Functional Capacity on Brain β-Amyloid Deposition in Older Women.Frontiers in aging neuroscience · 2021Article
- Deep learning improves utility of tau PET in the study of Alzheimer's disease.Alzheimer's & dementia (Amsterdam, Netherlands) · 2021Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 3 countries.
Funding
Abstract
purposeAmyloid PET which has been widely used for noninvasive assessment of cortical amyloid burden is visually interpreted in the clinical setting. As a fast and easy-to-use visual interpretation support system, we analyze whether the deep learning-based end-to-end estimation of amyloid burden improves inter-reader agreement as well as the confidence of the visual reading.
methodsA total of 121 clinical routines [
resultsInter-reader agreements for the visual reading based on a 3-point scale (BAPL score) calculated by Fleiss kappa coefficients were 0.46 and 0.76 for the visual reading without and with the deep learning system, respectively. For the two reading sessions, the confidence score of visual reading was improved at the visual reading session with the output (1.27 ± 0.078 for visual reading-only session vs. 1.66 ± 0.63 for a visual reading session with the deep learning system).
conclusionOur results highlight the impact of deep learning-based one-step amyloid burden estimation system on inter-reader agreement and confidence of reading when applied to clinical routine amyloid PET reading.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.