ArticleBMC pediatrics2025
Diagnosing acute bilirubin encephalopathy in neonates using MRI-based deep learning model.
Article in BMC pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Predicting neurodevelopmental outcomes in neonatal hyperbilirubinemia: a multidimensional nomogram integrating biomarkers and neurobehavioral scores.Frontiers in pediatrics · 2026Article
- A LASSO-based nomogram for predicting acute bilirubin encephalopathy in newborns with severe hyperbilirubinemia.Frontiers in pediatrics · 2026Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundNeonatal acute bilirubin encephalopathy (ABE) severely endangers the neonatal health. However, early clinical symptoms of ABE are nonspecific, often leading to missed diagnoses. The current study endeavors to establish a computer-assisted integrated model for clinical assessment and diagnosis of ABE.
methodsDiagnostic data from the ABE group and the hyperbilirubinemia without concurrent ABE (non-ABE) group were retrospectively analyzed. Patients were divided into a pre-training cohort, a training cohort, and two test cohorts. The training cohort and test cohort 1 were used to train and test a deep learning (DL) model integrating multimodal, self-supervised, and multi-instance learning. Test cohort 2 was used to compare the DL model with the radiologists.
resultsA total of 1048 magnetic resonance images from 262 patients were analyzed. The accuracy of the DL model and the area under the curve were 86.3% and 91.2% and 91.1% and 89.3% in test cohorts 1 and 2, respectively.
conclusionsThis study integrated clinical and radiological data into DL models to accurately diagnose ABE, close to the proficiency level of senior radiologists. It provides a convenient, low-cost evaluation model for patient management decisions and physician diagnoses.
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