ArticlePediatric radiology2022
A self-training deep neural network for early prediction of cognitive deficits in very preterm infants using brain functional connectome data.
Article in Pediatric radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 26 citations in OpenAlex.
- Opportunities and Challenges of Using Artificial Intelligence in Predicting Clinical Outcomes and Length of Stay in Neonatal Intensive Care Units: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- AI in Childbirth and Emerging Alternatives to Traditional Fertility Treatments.Reproductive sciences (Thousand Oaks, Calif.) · 2026Review
- Application of machine learning in early childhood development research: a scoping review.BMJ open · 2025Article
- DFC-Igloo: A dynamic functional connectome learning framework for identifying neurodevelopmental biomarkers in very preterm infants.Computer methods and programs in biomedicine · 2024Article
- Article
- Focused review on artificial intelligence for disease detection in infants.Frontiers in digital health · 2024Review
- A Semi-Supervised Graph Convolutional Network for Early Prediction of Motor Abnormalities in Very Preterm Infants.Diagnostics (Basel, Switzerland) · 2023Article
- Refined Automatic Brain Tumor Classification Using Hybrid Convolutional Neural Networks for MRI Scans.Diagnostics (Basel, Switzerland) · 2023Article
- Supervised Learning Algorithm Based on Spike Train Inner Product for Deep Spiking Neural Networks.Brain sciences · 2023Article
- A Similarity Measure-Based Approach Using RS-fMRI Data for Autism Spectrum Disorder Diagnosis.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundDeep 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.
objectiveThis 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
methodsWe collected brain functional connectome data from 343 very preterm infants at a mean (standard deviation) postmenstrual age of 42.7 (2.5) weeks, among whom 103 children had a cognitive assessment at 2 years (i.e. labeled data), and the remaining 240 children had not received 2-year assessments at the time this study was conducted (i.e. unlabeled data). To develop a self-training DNN model, we built an initial student model using labeled brain functional connectome data. Then, we applied the trained model as a teacher model to generate pseudo-labels for unlabeled brain functional connectome data. Next, we combined labeled and pseudo-labeled data to train a new student model. We iterated this procedure to obtain the best student model for the early prediction task in very preterm infants.
resultsIn our cross-validation experiments, the proposed self-training DNN model achieved an accuracy of 71.0%, a specificity of 71.5%, a sensitivity of 70.4% and an area under the curve of 0.75, significantly outperforming transfer learning models through pre-training approaches.
conclusionWe report the first self-training prognostic study in very preterm infants, efficiently utilizing a small amount of labeled data with a larger share of unlabeled data to aid the model training. The proposed technique is expected to facilitate deep learning with insufficient training data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.