Investigating Brain Age Deviation in Preterm Infants: A Deep Learning Approach

Saha, Sunirmal; Pagnozzi, Alex M.; George, Joanne M.; Colditz, Paul B.; Boyd, Roslyn N.; Rose, Stephen; Fripp, Jürgen; Pannek, Kerstin

doi:10.1007/978-3-030-00807-9_9

Cited by 1 publication

(2 citation statements)

References 11 publications

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Section: High Reliability and Accuracy Of 3d Cnn For Brain Age Predicmentioning

confidence: 99%

“…Although there is no error caused by manual intervention in predicting brain age using CNN-based model, there may be systematic bias (49,50). As reported in (49), CNN-based model will overestimate the younger and underestimate the older, decreasing the reliability of prediction results. To evaluate the reliability of the predicted results in this paper, the Bland-Altman plots characterizing the relationships between the mean and the difference of the predicted and actual value were given, showing in Figure 7.…”

Section: High Reliability and Accuracy Of 3d Cnn For Brain Age Predicmentioning

confidence: 99%

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Brain Age Prediction of Children Using Routine Brain MR Images via Deep Learning

et al. 2020

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Predicting brain age of children accurately and quantitatively can give help in brain development analysis and brain disease diagnosis. Traditional methods to estimate brain age based on 3D magnetic resonance (MR), T1 weighted imaging (T1WI), and diffusion tensor imaging (DTI) need complex preprocessing and extra scanning time, decreasing clinical practice, especially in children. This research aims at proposing an end-to-end AI system based on deep learning to predict the brain age based on routine brain MR imaging. We spent over 5 years enrolling 220 stacked 2D routine clinical brain MR T1-weighted images of healthy children aged 0 to 5 years old and randomly divided those images into training data including 176 subjects and test data including 44 subjects. Data augmentation technology, which includes scaling, image rotation, translation, and gamma correction, was employed to extend the training data. A 10-layer 3D convolutional neural network (CNN) was designed for predicting the brain age of children and it achieved reliable and accurate results on test data with a mean absolute deviation (MAE) of 67.6 days, a root mean squared error (RMSE) of 96.1 days, a mean relative error (MRE) of 8.2%, a correlation coefficient (R) of 0.985, and a coefficient of determination (R 2) of 0.971. Specially, the performance on predicting the age of children under 2 years old with a MAE of 28.9 days, a RMSE of 37.0 days, a MRE of 7.8%, a R of 0.983, and a R 2 of 0.967 is much better than that over 2 with a MAE of 110.0 days, a RMSE of 133.5 days, a MRE of 8.2%, a R of 0.883, and a R 2 of 0.780.

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Section: High Reliability and Accuracy Of 3d Cnn For Brain Age Predicmentioning

confidence: 99%

Section: High Reliability and Accuracy Of 3d Cnn For Brain Age Predicmentioning

confidence: 99%

Brain Age Prediction of Children Using Routine Brain MR Images via Deep Learning

et al. 2020

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show abstract

Investigating Brain Age Deviation in Preterm Infants: A Deep Learning Approach

Cited by 1 publication

References 11 publications

Brain Age Prediction of Children Using Routine Brain MR Images via Deep Learning

Brain Age Prediction of Children Using Routine Brain MR Images via Deep Learning

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