Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) model
Background and aims: Preterm birth imposes a high risk for developing neuromotor delay. Earlier prediction of adverse outcome in preterm infants is crucial for referral to earlier intervention. This study aimed to predict abnormal motor outcome at 2 years from early brain diffusion magnetic resonanc...
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Elsevier
2020-07-01
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Series: | NeuroImage |
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Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811920302949 |
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doaj-746ec08494bc439fbf01ce01e08c8755 |
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Article |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Susmita Saha Alex Pagnozzi Pierrick Bourgeat Joanne M. George DanaKai Bradford Paul B. Colditz Roslyn N. Boyd Stephen E. Rose Jurgen Fripp Kerstin Pannek |
spellingShingle |
Susmita Saha Alex Pagnozzi Pierrick Bourgeat Joanne M. George DanaKai Bradford Paul B. Colditz Roslyn N. Boyd Stephen E. Rose Jurgen Fripp Kerstin Pannek Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) model NeuroImage Preterm infants Neurodevelopment Motor outcome Neuro-sensory motor development assessment Deep learning Convolutional neural network |
author_facet |
Susmita Saha Alex Pagnozzi Pierrick Bourgeat Joanne M. George DanaKai Bradford Paul B. Colditz Roslyn N. Boyd Stephen E. Rose Jurgen Fripp Kerstin Pannek |
author_sort |
Susmita Saha |
title |
Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) model |
title_short |
Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) model |
title_full |
Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) model |
title_fullStr |
Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) model |
title_full_unstemmed |
Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) model |
title_sort |
predicting motor outcome in preterm infants from very early brain diffusion mri using a deep learning convolutional neural network (cnn) model |
publisher |
Elsevier |
series |
NeuroImage |
issn |
1095-9572 |
publishDate |
2020-07-01 |
description |
Background and aims: Preterm birth imposes a high risk for developing neuromotor delay. Earlier prediction of adverse outcome in preterm infants is crucial for referral to earlier intervention. This study aimed to predict abnormal motor outcome at 2 years from early brain diffusion magnetic resonance imaging (MRI) acquired between 29 and 35 weeks postmenstrual age (PMA) using a deep learning convolutional neural network (CNN) model. Methods: Seventy-seven very preterm infants (born <31 weeks gestational age (GA)) in a prospective longitudinal cohort underwent diffusion MR imaging (3T Siemens Trio; 64 directions, b = 2000 s/mm2). Motor outcome at 2 years corrected age (CA) was measured by Neuro-Sensory Motor Developmental Assessment (NSMDA). Scores were dichotomised into normal (functional score: 0, normal; n = 48) and abnormal scores (functional score: 1–5, mild-profound; n = 29). MRIs were pre-processed to reduce artefacts, upsampled to 1.25 mm isotropic resolution and maps of fractional anisotropy (FA) were estimated. Patches extracted from each image were used as inputs to train a CNN, wherein each image patch predicted either normal or abnormal outcome. In a postprocessing step, an image was classified as predicting abnormal outcome if at least 27% (determined by a grid search to maximise the model performance) of its patches predicted abnormal outcome. Otherwise, it was considered as normal. Ten-fold cross-validation was used to estimate performance. Finally, heatmaps of model predictions for patches in abnormal scans were generated to explore the locations associated with abnormal outcome. Results: For the identification of infants with abnormal motor outcome based on the FA data from early MRI, we achieved mean sensitivity 70% (standard deviation SD 19%), mean specificity 74% (SD 39%), mean AUC (area under the receiver operating characteristic curve) 72% (SD 14%), mean F1 score of 68% (SD 13%) and mean accuracy 73% (SD 19%) on an unseen test data set. Patch-based prediction heatmaps showed that the patches around the motor cortex and somatosensory regions were most frequently identified by the model with high precision (74%) as a location associated with abnormal outcome. Part of the cerebellum, and occipital and frontal lobes were also highly associated with abnormal NSMDA/motor outcome. Discussion/conclusion: This study established the potential of an early brain MRI-based deep learning CNN model to identify preterm infants at risk of a later motor impairment and to identify brain regions predictive of adverse outcome. Results suggest that predictions can be made from FA maps of diffusion MRIs well before term equivalent age (TEA) without any prior knowledge of which MRI features to extract and associated feature extraction steps. This method, therefore, is suitable for any case of brain condition/abnormality. Future studies should be conducted on a larger cohort to re-validate the robustness and effectiveness of these models. |
topic |
Preterm infants Neurodevelopment Motor outcome Neuro-sensory motor development assessment Deep learning Convolutional neural network |
url |
http://www.sciencedirect.com/science/article/pii/S1053811920302949 |
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doaj-746ec08494bc439fbf01ce01e08c87552020-11-25T02:59:34ZengElsevierNeuroImage1095-95722020-07-01215116807Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) modelSusmita Saha0Alex Pagnozzi1Pierrick Bourgeat2Joanne M. George3DanaKai Bradford4Paul B. Colditz5Roslyn N. Boyd6Stephen E. Rose7Jurgen Fripp8Kerstin Pannek9Australian e-Health Research Centre, CSIRO, Brisbane, Australia; Corresponding author.Australian e-Health Research Centre, CSIRO, Brisbane, AustraliaAustralian e-Health Research Centre, CSIRO, Brisbane, AustraliaQueensland Cerebral Palsy and Rehabilitation Research Centre, Centre for Children’s Health Research, Faculty of Medicine, The University of Queensland, Brisbane, AustraliaAustralian e-Health Research Centre, CSIRO, Brisbane, AustraliaCentre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane, AustraliaQueensland Cerebral Palsy and Rehabilitation Research Centre, Centre for Children’s Health Research, Faculty of Medicine, The University of Queensland, Brisbane, AustraliaAustralian e-Health Research Centre, CSIRO, Brisbane, AustraliaAustralian e-Health Research Centre, CSIRO, Brisbane, AustraliaAustralian e-Health Research Centre, CSIRO, Brisbane, AustraliaBackground and aims: Preterm birth imposes a high risk for developing neuromotor delay. Earlier prediction of adverse outcome in preterm infants is crucial for referral to earlier intervention. This study aimed to predict abnormal motor outcome at 2 years from early brain diffusion magnetic resonance imaging (MRI) acquired between 29 and 35 weeks postmenstrual age (PMA) using a deep learning convolutional neural network (CNN) model. Methods: Seventy-seven very preterm infants (born <31 weeks gestational age (GA)) in a prospective longitudinal cohort underwent diffusion MR imaging (3T Siemens Trio; 64 directions, b = 2000 s/mm2). Motor outcome at 2 years corrected age (CA) was measured by Neuro-Sensory Motor Developmental Assessment (NSMDA). Scores were dichotomised into normal (functional score: 0, normal; n = 48) and abnormal scores (functional score: 1–5, mild-profound; n = 29). MRIs were pre-processed to reduce artefacts, upsampled to 1.25 mm isotropic resolution and maps of fractional anisotropy (FA) were estimated. Patches extracted from each image were used as inputs to train a CNN, wherein each image patch predicted either normal or abnormal outcome. In a postprocessing step, an image was classified as predicting abnormal outcome if at least 27% (determined by a grid search to maximise the model performance) of its patches predicted abnormal outcome. Otherwise, it was considered as normal. Ten-fold cross-validation was used to estimate performance. Finally, heatmaps of model predictions for patches in abnormal scans were generated to explore the locations associated with abnormal outcome. Results: For the identification of infants with abnormal motor outcome based on the FA data from early MRI, we achieved mean sensitivity 70% (standard deviation SD 19%), mean specificity 74% (SD 39%), mean AUC (area under the receiver operating characteristic curve) 72% (SD 14%), mean F1 score of 68% (SD 13%) and mean accuracy 73% (SD 19%) on an unseen test data set. Patch-based prediction heatmaps showed that the patches around the motor cortex and somatosensory regions were most frequently identified by the model with high precision (74%) as a location associated with abnormal outcome. Part of the cerebellum, and occipital and frontal lobes were also highly associated with abnormal NSMDA/motor outcome. Discussion/conclusion: This study established the potential of an early brain MRI-based deep learning CNN model to identify preterm infants at risk of a later motor impairment and to identify brain regions predictive of adverse outcome. Results suggest that predictions can be made from FA maps of diffusion MRIs well before term equivalent age (TEA) without any prior knowledge of which MRI features to extract and associated feature extraction steps. This method, therefore, is suitable for any case of brain condition/abnormality. Future studies should be conducted on a larger cohort to re-validate the robustness and effectiveness of these models.http://www.sciencedirect.com/science/article/pii/S1053811920302949Preterm infantsNeurodevelopmentMotor outcomeNeuro-sensory motor development assessmentDeep learningConvolutional neural network |