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Accurate brain-age models for routine clinical MRI examinations

Research output: Contribution to journalArticlepeer-review

David A Wood, Sina Kafiabadi, Ayisha Al Busaidi, Emily Guilhem, Antanas Montvila, Jeremy Lynch, Matthew Townend, Siddharth Agarwal, Asif Mazumder, Gareth J Barker, Sebastian Ourselin, James H Cole, Thomas C Booth

Original languageEnglish
Article number118871
Pages (from-to)118871
JournalNeuroImage
Volume249
Early online date13 Jan 2022
DOIs
Accepted/In press3 Jan 2022
E-pub ahead of print13 Jan 2022
Published1 Apr 2022

Bibliographical note

Funding Information: This work was supported by the Royal College of Radiologists , King's College Hospital Research and Innovation , King's Health Partners Challenge Fund , NVIDIA (through the unrestricted use of a GPU obtained in a competition), and the Wellcome/Engineering and Physical Sciences Research Council Centre for Medical Engineering ( WT 203148/Z/16/Z ). Publisher Copyright: © 2022 The Author(s)

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Abstract

Convolutional neural networks (CNN) can accurately predict chronological age in healthy individuals from structural MRI brain scans. Potentially, these models could be applied during routine clinical examinations to detect deviations from healthy ageing, including early-stage neurodegeneration. This could have important implications for patient care, drug development, and optimising MRI data collection. However, existing brain-age models are typically optimised for scans which are not part of routine examinations (e.g., volumetric T1-weighted scans), generalise poorly (e.g., to data from different scanner vendors and hospitals etc.), or rely on computationally expensive pre-processing steps which limit real-time clinical utility. Here, we sought to develop a brain-age framework suitable for use during routine clinical head MRI examinations. Using a deep learning-based neuroradiology report classifier, we generated a dataset of 23,302 'radiologically normal for age' head MRI examinations from two large UK hospitals for model training and testing (age range = 18-95 years), and demonstrate fast (< 5 seconds), accurate (mean absolute error [MAE] < 4 years) age prediction from clinical-grade, minimally processed axial T2-weighted and axial diffusion-weighted scans, with generalisability between hospitals and scanner vendors (Δ MAE < 1 year). The clinical relevance of these brain-age predictions was tested using 228 patients whose MRIs were reported independently by neuroradiologists as showing atrophy 'excessive for age'. These patients had systematically higher brain-predicted age than chronological age (mean predicted age difference = +5.89 years, 'radiologically normal for age' mean predicted age difference = +0.05 years, p < 0.0001). Our brain-age framework demonstrates feasibility for use as a screening tool during routine hospital examinations to automatically detect older-appearing brains in real-time, with relevance for clinical decision-making and optimising patient pathways.

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