AIMC Topic: Infant, Premature

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BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment.

NeuroImage
We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local convolutions done in traditional image-based CNNs, our BrainNetCNN is compo...

Prediction of brain maturity in infants using machine-learning algorithms.

NeuroImage
Recent resting-state functional MRI investigations have demonstrated that much of the large-scale functional network architecture supporting motor, sensory and cognitive functions in older pediatric and adult populations is present in term- and prema...

Automatic Segmentation of MR Brain Images With a Convolutional Neural Network.

IEEE transactions on medical imaging
Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes u...

Machine-learning to characterise neonatal functional connectivity in the preterm brain.

NeuroImage
Brain development is adversely affected by preterm birth. Magnetic resonance image analysis has revealed a complex fusion of structural alterations across all tissue compartments that are apparent by term-equivalent age, persistent into adolescence a...

Evaluating Prevalence of Preterm Postnatal Growth Faltering Using Fenton 2013 and INTERGROWTH-21st Growth Charts with Logistic and Machine Learning Models.

Nutrients
Postnatal growth faltering (PGF) significantly affects premature neonates, leading to compromised neurodevelopment and an increased risk of long-term health complications. This retrospective study at a level III NICU of a tertiary hospital analyzed...

Improving Neonatal Care with AI: Class Weight Optimization for Respiratory Distress Syndrome Prediction in Very Low Birth Weight Infants.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In this study, we developed an AI model to predict Respiratory Distress Syndrome (RDS) in premature infants, aiming to reduce unnecessary treatment with artificial pulmonary surfactant. We analyzed data from 13,120 infants in 76 hospitals, considerin...

Application of Artificial Intelligence in Infant Movement Classification: A Reliability and Validity Study in Infants Who Were Full-Term and Preterm.

Physical therapy
OBJECTIVE: Preterm infants are at high risk of neuromotor disorders. Recent advances in digital technology and machine learning algorithms have enabled the tracking and recognition of anatomical key points of the human body. It remains unclear whethe...

Image Reconstruction Using Deep Learning for Near-Infrared Optical Tomography: Generalization Assessment.

Advances in experimental medicine and biology
Time is one of the most critical factors in preventing brain lesions due to hypoxic ischemia in preterm infants. Since early detection of low oxygenation is vital and the time window for therapy is narrow, near-infrared optical tomography (NIROT) mus...