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Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) model
2020
NeuroImage
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convolutional neural network (CNN) model. ...
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 ...
Acknowledgements We would like to acknowledge the families of preterm infants who participated in this study and all the associated medical, nursing and administrative staff. ...
doi:10.1016/j.neuroimage.2020.116807
pmid:32278897
fatcat:nf4knnadzzcsdliw7a7wr6pro4
Early Prediction of Cognitive Deficit in Very Preterm Infants Using Brain Structural Connectome With Transfer Learning Enhanced Deep Convolutional Neural Networks
2020
Frontiers in Neuroscience
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As such, we developed a transfer learning enhanced convolutional neural network (TL-CNN) model for early prediction of cognitive assessment at 2 years of age in very preterm infants using brain structural ...
The results suggest that deep learning models may facilitate early prediction of later neurodevelopmental outcomes in very preterm infants at term-equivalent age. ...
In this study, we proposed a TL-enhanced deep CNN (TL-CNN) model for early prediction of cognitive deficit at 2 years of age in very preterm infants using brain structural connectome derived from at term ...
doi:10.3389/fnins.2020.00858
pmid:33041749
pmcid:PMC7530168
fatcat:ufmstu6kqvcmrimzgzvmyfocfa
A multi-task, multi-stage deep transfer learning model for early prediction of neurodevelopment in very preterm infants
2020
Scientific Reports
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early interventions to improve clinical outcomes in very preterm infants. ...
(cognitive, language and motor) outcomes at 2 years corrected age in very preterm infants. ...
Acknowledgements This study was supported by the National Institutes of Health Grants R21-HD094085, R01-NS094200 and R01-NS096037 and a Trustee grant from Cincinnati Children's Hospital Medical Center. ...
doi:10.1038/s41598-020-71914-x
pmid:32934282
fatcat:mqllg23b5nbjbdneyl43gazi6e
BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment
2017
NeuroImage
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Abstract We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict clinical neurodevelopmental outcomes from brain networks. ...
We apply the BrainNetCNN framework to predict cognitive and motor developmental outcome scores from structural brain networks of infants born preterm. ...
S.P.M. is currently the Bloorview Children's Hospital Chair in Pediatric Neuroscience. R.E.G. is supported by a Senior Scientist Award from the Child & Family Research Institute. ...
doi:10.1016/j.neuroimage.2016.09.046
pmid:27693612
fatcat:7orxb5grqzdh5mi3k6337kdoi4
Deep Multimodal Learning From MRI and Clinical Data for Early Prediction of Neurodevelopmental Deficits in Very Preterm Infants
2021
Frontiers in Neuroscience
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Thus, analyzing quantitative multimodal MRI features affords unique opportunities to study early postnatal brain development and neurodevelopmental outcome prediction in VPIs. ...
These survivors, especially, very preterm infants (VPIs), born ≤ 32 weeks gestational age, are at high risk for neurodevelopmental impairments. ...
In another study, Saha et al. (2020) achieved a mean accuracy of 73% on predicting motor outcome in preterm infants by applying a CNN model on DTI data. ...
doi:10.3389/fnins.2021.753033
pmid:34675773
pmcid:PMC8525883
fatcat:w2iwwvfyubfrbnymdronjxrbxy
Brain age predicted using graph convolutional neural network explains developmental trajectory in preterm neonates
[article]
2021
bioRxiv
pre-print
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Here, we investigated the ability of the graph convolutional network (GCN) to predict brain age for preterm neonates by accounting for morphometrics measured on the cortical surface and the surface mesh ...
Our findings demonstrate that GCN-based age prediction of preterm neonates (n=170; mean absolute error [MAE]: 1.06 weeks) outperformed conventional machine learning algorithms and deep learning methods ...
For instance, in aging brains, the PBA has been investigated using standard deep learning approaches (Ning et al., 2020) where an image volume is inputted into a convolutional neural network (CNN) and ...
doi:10.1101/2021.05.15.444320
fatcat:p2hhvgtyp5fejgqnagphfukvzm
Prospects of deep learning for medical imaging
2018
Precision and Future Medicine
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First, an overview of how traditional machine learning evolved to deep learning is provided. Second, a survey of the application of deep learning in medical imaging research is given. ...
Recently, a highly flexible machine learning approach known as deep learning has emerged as a disruptive technology to enhance the performance of existing machine learning techniques and to solve previously ...
predict a cognitive and motor developmental outcome score
by using structural brain networks of diffusion tensor imag-
ing. ...
doi:10.23838/pfm.2018.00030
fatcat:2bclzigfijadzcdoqhhzniqwdy
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
[article]
2021
arXiv
pre-print
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With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. ...
As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interactive nodes connected by edges whose weights can be ...
DCRNN model: Diffusion convolutional recurrent neural networks (DCRNN) [56] introduce the diffusion graph convolutional layer to capture spatial dependencies, and uses a sequence-to-sequence architecture ...
arXiv:2105.13137v1
fatcat:gm7d2ziagba7bj3g34u4t3k43y
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
2021
Sensors
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With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. ...
As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be ...
DCRNN model: Diffusion convolutional recurrent neural networks (DCRNNs) [51] introduce the diffusion graph convolutional layer to capture spatial dependencies, and uses a sequence-to-sequence architecture ...
doi:10.3390/s21144758
fatcat:jytyt4u2pjgvhnhcto3vcvd3a4
Brain microstructure and morphology of very preterm-born infants at term equivalent age: associations with motor and cognitive outcomes at 1 and 2 years
2020
NeuroImage
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Very preterm-born infants are at risk of adverse neurodevelopmental outcomes. ...
Brain magnetic resonance imaging (MRI) at term equivalent age (TEA) can probe tissue microstructure and morphology, and demonstrates potential in the early prediction of outcomes. ...
Rose, Jurgen Fripp, and Kerstin Pannek. 2020. 31 "Predicting Motor Outcome in Preterm Infants from Very Early Brain Diffusion MRI 32 Using a Deep Learning Convolutional Neural Network (CNN) Model." ...
doi:10.1016/j.neuroimage.2020.117163
pmid:32663645
fatcat:wco2lcwnjre7fmb4l6qwssoqra
Machine Learning on Human Connectome Data from MRI
[article]
2016
arXiv
pre-print
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Functional MRI (fMRI) and diffusion MRI (dMRI) are non-invasive imaging modalities that allow in-vivo analysis of a patient's brain network (known as a connectome). ...
Recently, researchers have been exploring the application of machine learning models to connectome data in order to predict clinical outcomes and analyze the importance of subnetworks in the brain. ...
190] and delayed motor and cognitive development in preterm infants [24, 25, 92] since early intervention of these conditions can improve patient outcomes [14, 190] . ...
arXiv:1611.08699v1
fatcat:opmtmr3eejbjjm4swfmg54g4q4
Fusing autonomy and sociality via embodied emergence and development of behaviour and cognition from fetal period
2019
Philosophical Transactions of the Royal Society of London. Biological Sciences
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This work is important because it models very early autonomous development in realistic detailed human embodiment. ...
We present a hypothetical early developmental scenario that fills in the very beginning part of the comprehensive scenarios proposed in developmental robotics. ...
Other collaborators and (former) students who contributed to our work reviewed in this paper are gratefully acknowledged. ...
doi:10.1098/rstb.2018.0031
pmid:30852992
pmcid:PMC6452254
fatcat:ebmqbqydf5gypntxakyy2yhgxu
Diffusion Kurtosis Imaging of the neonatal Spinal Cord: design and application of the first processing pipeline implemented in Spinal Cord Toolbox
[article]
2021
medRxiv
pre-print
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packages widely adopted in brain imaging domain. ...
Due to its extreme sensitivity to non-gaussian diffusion, DKI proves particularly suitable for detecting complex, subtle, fast microstructural changes occurring in this area at this early and critical ...
It is composed of a cascade of two convolutional neural networks (CNN), specifically designed to deal with spinal cord morphometry: the first detects the cord centerline and reduces the space around the ...
doi:10.1101/2021.03.12.21253413
fatcat:yaytyxwblnfvzht5shsqjaxcey
28th Annual Computational Neuroscience Meeting: CNS*2019
2019
BMC Neuroscience
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Activity from both the LP and PD neurons of the stomatogastric ganglion of a crab was recorded using intracellular electrodes and sent to a computer through a DAQ device. ...
A hexapod robotic platform was built using printable parts from the BQ DIWO PrintBot Crab, whose schematics are open-source. ...
Deep Convolutional Neural Networks (CNNs) excel at object recognition and classification, with accuracy levels that now exceed humans [1] . ...
doi:10.1186/s12868-019-0538-0
fatcat:3pt5qvsh45awzbpwhqwbzrg4su
SPR 2020
2020
Pediatric Radiology
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A convolutional neural network with the U-Net architecture was used to train the segmentation model. ...
Paper #: 014
Free-Breathing Highly Accelerated 2D Cine Cardiac MRI Using Deep Convolutional Neural Network (DCNN) Reconstruction: Clinical Validation Evan J. ...
Methods & Materials: Cases of IHA seen on brain MRIs at a single tertiary paediatric centre on 1.5T and 3T performed from September 2001 and September 2019 were compiled from a search of MRI reports on ...
doi:10.1007/s00247-020-04679-0
pmid:32435980
fatcat:y6da6d4blvaxlkus6w6zahpwlu
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