A self-training deep neural network for early prediction of cognitive deficits in very preterm infants using brain functional connectome data.

ABSTRACT:

Background

Deep learning has been employed using brain functional connectome data for evaluating the risk of cognitive deficits in very preterm infants. Although promising, training these deep learning models typically requires a large amount of labeled data, and labeled medical data are often very difficult and expensive to obtain.

Objective

This study aimed to develop a self-training deep neural network (DNN) model for early prediction of cognitive deficits at 2 years of corrected age in very preterm infants (gestational age ≤32 weeks) using both labeled and unlabeled brain functional connectome data.

Materials and methods

We collected brain functional connectome data from 343 very preterm infants at a mean (standard deviation) postmenstrual age of 42.7 (2.5) weeks,

SUBMITTER: Ali R 

PROVIDER: S-EPMC9574648 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature