Abstract #1695

Early prediction of cognitive deficits in very preterm infants using graph convolutional networks with brain structural connectome

Hailong Li¹, Ming Chen^1,2, Jinghua Wang³, Nehal A. Parikh^4,5, and Lili He^1,5

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

Up to 40% of very preterm infants (≤32 weeks’ gestational age) are identified with cognitive deficits at 2 years of age. Reproducible approaches that serve as neonatal prognostic tools are urgently needed for early treatment decision. We developed a graph convolutional network model to learn the latent topological features of brain structural connectome obtained at term-equivalent age for predicting cognitive deficits at 2 years corrected age in very preterm infants. The proposed model was able to identify infants at high-risk of cognitive deficits with a balanced accuracy of 78.5% and an area under the receiver operating characteristic curve of 0.78.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.