ArticleDementia and neurocognitive disorders2022
Prediction of East Asian Brain Age using Machine Learning Algorithms Trained With Community-based Healthy Brain MRI.
Article in Dementia and neurocognitive disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 8 citations in OpenAlex.
- Predicting Brain Age and Gender from Brain Volume Data Using Variational Quantum Circuits.Brain sciences · 2024Article
- Magnetic resonance image-based brain age as a discriminator of dementia conversion in patients with amyloid-negative amnestic mild cognitive impairment.Scientific reports · 2023Article
- Assessing Machine Learning Models for Predicting Age with Intracranial Vessel Tortuosity and Thickness Information.Brain sciences · 2023Article
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Authors and funding
2 authors at 1 institution in 1 country.
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Abstract
Background and Purpose: Magnetic resonance imaging (MRI) helps with brain development analysis and disease diagnosis. Brain volumes measured from different ages using MRI provides useful information in clinical evaluation and research. Therefore, we trained machine learning models that predict the brain age gap of healthy subjects in the East Asian population using T1 brain MRI volume images. Methods: In total, 154 T1-weighted MRIs of healthy subjects (55-83 years of age) were collected from an East Asian community. The information of age, gender, and education level was collected for each participant. The MRIs of the participants were preprocessed using FreeSurfer(https://surfer.nmr.mgh.harvard.edu/) to collect the brain volume data. We trained the models using different supervised machine learning regression algorithms from the scikit-learn (https://scikit-learn.org/) library. Results: The trained models comprised 19 features that had been reduced from 55 brain volume labels. The algorithm BayesianRidge (BR) achieved a mean absolute error (MAE) and r squared (R Conclusions: The MAE and R
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