ArticleEuropean journal of nuclear medicine and molecular imaging2021
Classification of negative and positive
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 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 25 citations in OpenAlex.
- Stratifying amyloid burden in early Alzheimer's disease using cascaded attention-guided vision transformer using [¹⁸F]Florbetapir PET.European radiology · 2026Article
- Recent Advances in the Application of Artificial Intelligence in Alzheimer's Disease.Current Alzheimer research · 2025Review
- AmyloidPETNet: Classification of Amyloid Positivity in Brain PET Imaging Using End-to-End Deep Learning.Radiology · 2024Article
- Automated detection of Alzheimer's disease: a multi-modal approach with 3D MRI and amyloid PET.Scientific reports · 2024Article
- Estimation of brain amyloid accumulation using deep learning in clinical [EJNMMI physics · 2023Article
- Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review.Frontiers in medicine · 2023Review
- Spatial normalization and quantification approaches of PET imaging for neurological disorders.European journal of nuclear medicine and molecular imaging · 2022Review
- 3D Convolutional Neural Network-Based Denoising of Low-Count Whole-BodyDiagnostics (Basel, Switzerland) · 2022Article
- Application of artificial intelligence in brain molecular imaging.Annals of nuclear medicine · 2022Review
- Deep Learning Analysis UsingFrontiers in oncology · 2022Article
- Optimization of Automatic Synthesis and Separation of [Frontiers in chemistry · 2022Article
- Recent contributions to the field of subjective cognitive decline in aging: A literature review.Alzheimer's & dementia (Amsterdam, Netherlands)Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 1 institution in 1 country.
Funding
Abstract
purposeVisual reading of
methods
resultsThe sagittal 2D-CNN classified the SCIENCe scans with the highest average accuracy of 99% ± 2 (SD), sensitivity of 97% ± 7 and specificity of 100%. The ADNI scans were classified with a 95% accuracy, 100% sensitivity and 92.3% specificity.
conclusionThe 2D-CNN algorithm can classify Aß negative and positive
Indexed as
Identifiers
What OpenQuestion holds
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.