Trial reportScientific reports2020
A multi-task, multi-stage deep transfer learning model for early prediction of neurodevelopment in very preterm infants.
Trial report in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03345069 (Early Prediction of Cognitive and Motor Deficits Using Advanced MRI in Very Preterm Infants), which is not on this map. Cited by 38 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Early Prediction of Cognitive and Motor Deficits Using Advanced MRI in Very Preterm Infants
Who cites it
38 citing papers in PubMed, 3 syntheses or guidelines pooled it, 67 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
- Machine learning for understanding and predicting neurodevelopmental outcomes in premature infants: a systematic review.Pediatric research · 2023Pooled it
- Neurodevelopmental Outcome in Very Low Birth Weight Preterm Infants: An Exploratory Multivariable Analysis Including Sonographic Brain Volume Trajectories-Data from the NeoNEVS Project.Children (Basel, Switzerland) · 2026Article
- Barriers and facilitators to neurodevelopmental follow-up among at-risk newborns across Washington State.Research square · 2026Article
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- Artificial intelligence in diagnosis of pediatric neurodevelopmental disorders: a scoping review.World journal of pediatrics : WJP · 2026Article
- Diffuse white matter abnormality is independently predictive of neurodevelopmental outcomes in preterm infants.Archives of disease in childhood. Fetal and neonatal edition · 2026Article
- Artificial intelligence in thyroid ultrasound: clinical applications and perspectives.Frontiers in endocrinology · 2026Review
- The Impact of Artificial Intelligence on Women's Healthcare: A Systematic Review.Qatar medical journal · 2026Review
- Machine learning models for neurocognitive outcome prediction in preterm born infants.Pediatric research · 2025Article
- Machine learning for outcome prediction in preterm infants: opportunities and challenges.Pediatric research · 2025Article
- [Artificial intelligence in preventive medicine for children and adolescents-applications and acceptance].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025Review
- Machine learning prediction of feeding intolerance in preterm infants: a pre-feeding risk stratification model.Frontiers in pediatrics · 2025Article
- DFC-Igloo: A dynamic functional connectome learning framework for identifying neurodevelopmental biomarkers in very preterm infants.Computer methods and programs in biomedicine · 2024Article
- Predicting tissue distribution and tumor delivery of nanoparticles in mice using machine learning models.Journal of controlled release : official journal of the Controlled Release Society · 2024Article
- Deep multimodal saliency parcellation of cerebellar pathways: Linking microstructure and individual function through explainable multitask learning.Human brain mapping · 2024Article
- Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making.Paediatric drugs · 2024Review
- Computational Approaches for Connecting Maternal Stress to Preterm Birth.Clinics in perinatology · 2024Review
- Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Survivors following very premature birth (i.e., ≤ 32 weeks gestational age) remain at high risk for neurodevelopmental impairments. Recent advances in deep learning techniques have made it possible to aid the early diagnosis and prognosis of neurodevelopmental deficits. Deep learning models typically require training on large datasets, and unfortunately, large neuroimaging datasets with clinical outcome annotations are typically limited, especially in neonates. Transfer learning represents an important step to solve the fundamental problem of insufficient training data in deep learning. In this work, we developed a multi-task, multi-stage deep transfer learning framework using the fusion of brain connectome and clinical data for early joint prediction of multiple abnormal neurodevelopmental (cognitive, language and motor) outcomes at 2 years corrected age in very preterm infants. The proposed framework maximizes the value of both available annotated and non-annotated data in model training by performing both supervised and unsupervised learning. We first pre-trained a deep neural network prototype in a supervised fashion using 884 older children and adult subjects, and then re-trained this prototype using 291 neonatal subjects without supervision. Finally, we fine-tuned and validated the pre-trained model using 33 preterm infants. Our proposed model identified very preterm infants at high-risk for cognitive, language, and motor deficits at 2 years corrected age with an area under the receiver operating characteristic curve of 0.86, 0.66 and 0.84, respectively. Employing such a deep learning model, once externally validated, may facilitate risk stratification at term-equivalent age for early identification of long-term neurodevelopmental deficits and targeted early interventions to improve clinical outcomes in very preterm infants.
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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.