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Abstract #2259

A semi-supervised graph convolutional network for early prediction of motor impairments in very preterm infants using brain connectome

Hailong Li1, Ming Chen1,2, Jinghua Wang3, Nehal A. Parikh4,5, and Lili He1,5
1Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, 2Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, United States, 3Deep MRI Imaging Inc., Lewes, DE, United States, 4The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, 5Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States

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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