AI-Driven Neonatal MRI Interpretation: A Systematic Review of Diagnostic Efficiency, Prognostic Value, and Implementation Barriers for Hypoxic-Ischemic Encephalopathy
Bibliographic record
Abstract
Artificial intelligence (AI), especially deep learning techniques, is revolutionizing neonatal neuroimaging by significantly improving the detection and prognostic evaluation of hypoxic-ischemic encephalopathy (HIE), a major contributor to neonatal morbidity and mortality. This systematic review integrates findings from five high-quality, peer-reviewed studies published between 2015 and 2025, identified through comprehensive searches of PubMed, Embase, Scopus, and the Cochrane Library. The review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and applied the Newcastle-Ottawa Scale (NOS), Risk of Bias 2 (RoB 2), and Assessment of Multiple Systematic Reviews 2 (AMSTAR 2) tools to ensure methodological rigor and minimize bias. AI algorithms, especially convolutional neural networks (CNNs), have shown high effectiveness in identifying brain injuries associated with HIE, with sensitivity ranging from 83% to 95% and specificity between 86% and 93%. These models frequently outperform conventional radiological assessments in diagnostic accuracy. These models also reduced interpretation time by up to 47%, streamlining critical care workflows. Prognostic AI tools showed 77-87% accuracy in predicting long-term neurodevelopmental outcomes, aiding in early clinical interventions and family guidance. Despite these promising results, limitations such as small sample sizes (n = 100-200), heterogeneous MRI protocols, and high computational demands hinder broader clinical application. Standardized imaging, multi-center collaboration, and explainable AI models are crucial for clinical scalability. Moreover, successful integration of AI into neonatal intensive care units (NICUs) requires rigorous validation, ethical oversight, and clinician training to ensure safety, transparency, and trust. Collaborative efforts between neonatologists, radiologists, data scientists, and policymakers will be essential to align AI innovations with patient-centered care. As this technology matures, it holds significant potential to improve diagnostic precision, optimize clinical outcomes, and reduce disparities in neonatal neurological care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".