ArticleJournal of translational medicine2019
Mining multi-site clinical data to develop machine learning MRI biomarkers: application to neonatal hypoxic ischemic encephalopathy.
Article in Journal of translational medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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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.
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Who cites it
19 citing papers in PubMed.
- Artificial Intelligence in Pediatric Imaging: A Primer for Pediatric Clinicians.Indian journal of pediatrics · 2026Review
- Artificial intelligence for pediatric neuroimaging.Pediatric radiology · 2026Review
- BONBID-HIE 2023: Lesion Segmentation Challenge in BOston Neonatal Brain Injury Data for Hypoxic Ischemic Encephalopathy.IEEE transactions on medical imaging · 2026Article
- Article
- PARADISE: Personalized and regional adaptation for HIE disease identification and segmentation.Medical image analysis · 2025Article
- Current progress and future prospects of machine learning in the diagnosis of neonatal encephalopathy: a narrative review.Translational pediatrics · 2025Review
- BOston Neonatal Brain Injury Data for Hypoxic Ischemic Encephalopathy (BONBID-HIE): I. MRI and Lesion Labeling.Scientific data · 2025Article
- Machine-learning based prediction of future outcome using multimodal MRI during early childhood.Seminars in fetal & neonatal medicine · 2024Review
- Artificial Intelligence Outcome Prediction in Neonates with Encephalopathy (AI-OPiNE).Radiology. Artificial intelligence · 2024Article
- MRI predictors of long-term outcomes of neonatal hypoxic ischaemic encephalopathy: a primer for radiologists.The British journal of radiology · 2024Review
- Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities.Journal of perinatology : official journal of the California Perinatal Association · 2024Review
- Radiomics based of deep medullary veins on susceptibility-weighted imaging in infants: predicting the severity of brain injury of neonates with perinatal asphyxia.European journal of medical research · 2023Article
- The role of MRI-based texture analysis to predict the severity of brain injury in neonates with perinatal asphyxia.The British journal of radiology · 2022Article
- Article
- Learning the impact of acute and chronic diseases on forecasting neonatal encephalopathy.Computer methods and programs in biomedicine · 2021Article
- Multi-channel attention-fusion neural network for brain age estimation: Accuracy, generality, and interpretation with 16,705 healthy MRIs across lifespan.Medical image analysis · 2021Article
- Artificial intelligence in paediatric radiology: Future opportunities.The British journal of radiology · 2021Review
- Current Perspectives of Artificial Intelligence in Pediatric Neuroradiology: An Overview.Frontiers in radiology · 2021Review
- [Artificial intelligence in image evaluation and diagnosis].Monatsschrift Kinderheilkunde : Organ der Deutschen Gesellschaft fur Kinderheilkunde · 2021Review
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Authors and funding
13 authors.
Funding
Abstract
backgroundSecondary and retrospective use of hospital-hosted clinical data provides a time- and cost-efficient alternative to prospective clinical trials for biomarker development. This study aims to create a retrospective clinical dataset of Magnetic Resonance Images (MRI) and clinical records of neonatal hypoxic ischemic encephalopathy (HIE), from which clinically-relevant analytic algorithms can be developed for MRI-based HIE lesion detection and outcome prediction.
methodsThis retrospective study will use clinical registries and big data informatics tools to build a multi-site dataset that contains structural and diffusion MRI, clinical information including hospital course, short-term outcomes (during infancy), and long-term outcomes (~ 2 years of age) for at least 300 patients from multiple hospitals. DISCUSSION: Within machine learning frameworks, we will test whether the quantified deviation from our recently-developed normative brain atlases can detect abnormal regions and predict outcomes for individual patients as accurately as, or even more accurately, than human experts. Trial Registration Not applicable. This study protocol mines existing clinical data thus does not meet the ICMJE definition of a clinical trial that requires registration.
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