ArticleJournal of the Korean Society of Radiology2023
Prediction of Amyloid β-Positivity with both MRI Parameters and Cognitive Function Using Machine Learning.
Article in Journal of the Korean Society of Radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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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
5 citing papers in PubMed.
- Predicting Brain Amyloid PET Positivity Using the Amyloid Beta Composite (ABC) Index in Patients With Cognitive Impairment.Dementia and neurocognitive disorders · 2026Article
- A Robust Residual Three-dimensional Convolutional Neural Networks Model for Prediction of Amyloid-β Positivity by Using FDG-PET.Clinical nuclear medicine · 2025Article
- Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phaseScientific reports · 2025Article
- Advancing Alzheimer's Diagnosis with AI-Enhanced MRI: A Review of Challenges and Implications.Current neuropharmacology · 2025Review
- Predicting Brain Amyloid Status Using the National Institute of Health Toolbox (NIHTB) for Assessment of Neurological and Behavioral Function.The journal of prevention of Alzheimer's disease · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To investigate the MRI markers for the prediction of amyloid β (Aβ)-positivity in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and to evaluate the differences in MRI markers between Aβ-positive (Aβ [+]) and -negative groups using the machine learning (ML) method. Materials and Methods: This study included 139 patients with MCI and AD who underwent amyloid PET-CT and brain MRI. Patients were divided into Aβ (+) ( Results: The Fazekas scale of WMH ( Conclusion: The application of ML using the MMSE, third ventricle, and hippocampal volume is helpful in predicting Aβ-positivity with a good accuracy.
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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 Socratic graph.