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Abstract #4629

Early prediction of neurodevelopmental deficits in very preterm infants using a multi-task deep transfer learning model

Lili He1,2,3, Hailong Li1,3, Jinghua Wang4, Ming Chen1,5, Jonathan R. Dillman4,6, and Nehal Parikh1,2
1The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, 2Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States, 3Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, 4Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States, 5Department of Electronic Engineering and Computing Science, University of Cincinnati, Cincinnati, OH, United States, 6Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States

We proposed a deep transfer learning model using the fusion of clinical and brain functional connectome data obtained at term for early neurodevelopmental deficits prediction at two years corrected age in very preterm infants. The proposed model was first trained in an unsupervised fashion using 884 subjects from publicly available ABIDE repository, then fine-tuned and cross-validated on 33 very preterm infants. Our model achieved an AUC of 0.77, 0.63 and 0.74 on the risk stratification of cognitive, language and motor deficits, respectively. Our findings demonstrated the feasibility of using deep transfer learning on connectome data for abnormal neurodevelopment prediction.

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