About 32−42% of very preterm infants develop minor motor impairments around the world. Unfortunately, large MRI datasets with clinical outcome annotations/labels are typically unavailable, especially in neonates. To address this challenge of limited training data, we developed a semi-supervised graph convolutional network model to utilize both labeled and unlabeled data during model training to predict motor impairments at 2 years corrected age using brain structural connectome derived from diffusion MRI obtained at term-equivalent age in very preterm infants. The proposed model was able to identify infants with motor impairments with an accuracy of 68.1% and an AUC of 0.67.
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