Optimising brain age estimation through transfer learning: A suite of pre-trained foundation models for improved performance and generalisability in a clinical setting
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Authors
Wood, David A.
Townend, Matthew
Guilhem, Emily
Kafiabadi, Sina
Hammam, Ahmed
Wei, Yiran
Al Busaidi, Ayisha
Mazumder, Asif
Sasieni, Peter
Barker, Gareth J.
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Issue Date
2024
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Article
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Abstract
Estimated age from brain MRI data has emerged as a promising biomarker of neurological health. However, the absence of large, diverse, and clinically representative training datasets, along with the complexity of managing heterogeneous MRI data, presents significant barriers to the development of accurate and generalisable models appropriate for clinical use. Here, we present a deep learning framework trained on routine clinical data (N up to 18,890, age range 18-96 years). We trained five separate models for accurate brain age prediction (all with mean absolute error ≤4.0 years, R
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Journal
Human Brain Mapping
Volume
45
Issue
4