Keywords: Neonatal, Neonatal, Neonatal, Point-of-care MRI, Machine Learning/Artificial Intelligence, Segmentation
Motivation: To propose a new approach for managing hydrocephalus in neonates based on neonatal MRI and AI-based brain ventricle volume quantitation, brain parenchyma volume and ventricle-to-brain volumes ratio.
Goal(s): To evaluate the robustness of quantitative measurements of ventricle and brain volumes using AI-based algorithms, on scans acquired on a 1 Tesla permanent magnet neonatal MRI.
Approach: The performance of three AI-based segmentation algorithms was evaluated using linear correlations and Intersection over Union (IoU) score between ground truth and predictions.
Results: Results show high linear correlations between ground truth and algorithm predictions, validating the use of these volumetric measurements to monitor hydrocephalus longitudinally.
Impact: An AI based method for segmenting neonatal MRI images may provide volumetric quantitation and enable fast and accurate decision making on surgical intervention in preterm infants with hydrocephalus.
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