TY - JOUR
T1 - Accurate brain-age models for routine clinical MRI examinations
AU - Wood, David A
AU - Kafiabadi, Sina
AU - Busaidi, Ayisha Al
AU - Guilhem, Emily
AU - Montvila, Antanas
AU - Lynch, Jeremy
AU - Townend, Matthew
AU - Agarwal, Siddharth
AU - Mazumder, Asif
AU - Barker, Gareth J
AU - Ourselin, Sebastian
AU - Cole, James H
AU - Booth, Thomas C
N1 - 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)
PY - 2022/4/1
Y1 - 2022/4/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85122612086&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2022.118871
DO - 10.1016/j.neuroimage.2022.118871
M3 - Article
C2 - 34995797
SN - 1053-8119
VL - 249
SP - 118871
JO - NeuroImage
JF - NeuroImage
M1 - 118871
ER -