Abstract #4628

A deep transfer learning model for early prediction of cognitive deficits using brain structural connectome data in very preterm infants

Ming Chen^1,2, Hailong Li¹, Jinghua Wang³, Weihong Yuan^3,4, Adebayo Brainmah⁴, Mekibib Altaye⁵, Nehal Parikh^1,6, and Lili He^1,4,6

¹The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, ²Department of Electronic Engineering and Computing Science, University of Cincinnati, Cincinnati, OH, United States, ³Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States, ⁴Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, ⁵Department of Biostatistics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, ⁶Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States

The high risk of cognitive deficits is a major concern for parents and clinicians caring for premature babies. Early and accurate identification of children at risk is urgently needed for early treatment decision. We propose a deep transfer learning model to predict cognitive deficits at 2 years corrected age using brain structural connectome data obtained at term. The proposed model was able to identify infants at high-risk of later cognitive deficit with an accuracy of 78.5% and an AUC of 0.75. The predicted cognitive scores were significantly correlated with corresponding Bayley-III cognitive scores, with a Pearson’s correlation coefficient of 0.48.

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