Pilot corpus: NIH-funded biomedical datasets. Any DOI can be scored on demand — network effects sharpen as coverage grows.

Interpretable Severity Scoring of Pelvic Trauma Through Automated Fracture Detection and Bayesian Inference

IEEE Transactions on Medical Imaging(2024)10.1109/tmi.2024.3428836Source: DataRank Database

Interpretable Severity Scoring of Pelvic Trauma Through Automated Fracture Detection and Bayesian Inference is a research paper published in IEEE Transactions on Medical Imaging (2024). On theSindex it has a DataRank of 0. It has been cited 7 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access7 citations

Download PDF

Cite:

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (4 grants)Impact (FWCI 0.97)OA: hybridTopics (48)IDs (PubMed, PMC, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

Run a calibrated FAIR evaluation for this paper →

We only score data papers we can read in full — never from an abstract alone.

Authors (5)

David Dreizin ORCID,Catalina Gómez ORCID,Anna Zapaishchykova ORCID,Mathias Unberath ORCID,Haomin Chen ORCID

Live enrichment

PubMed:39037876 PMC:PMC11783822 OpenAlex:https://openalex.org/W4400877966

National Institutes of Health (NIH) through the NIH National Institute of General Medical Sciences

Grant: R01 GM148987

NIH National Institute of Biomedical Imaging and Bioengineering

Grant: K08 EB027141

National Institutes of Health

Grant: 5R01GM148987-03

Human-centered CT-based CADx Tools for Traumatic Torso Hemorrhage

National Institutes of Health

Grant: 5K08EB027141-02

Machine learning-based segmentation and risk modeling for real-time prediction of major arterial bleeding after pelvic fractures

FWCI

0.97

Citation Percentile

0.7%

Citation Trend

2025

2026

hybridLicense: cc-by

journalhttps://doi.org/10.1109/tmi.2024.3428836

publisherhttps://doi.org/10.1109/tmi.2024.3428836

publisherhttp://xplorestaging.ieee.org/ielx8/42/10820125/10605832.pdf?arnumber=10605832

repositoryhttps://pubmed.ncbi.nlm.nih.gov/39037876

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/11783822

Fields of Study

Medical Imaging and AnalysisPelvic and Acetabular InjuriesHydraulic Fracturing and Reservoir AnalysisComputer ScienceMedicineEngineering0202 electrical engineering, electronic engineering, information engineering02 engineering and technologyHumansPelvic BonesBayes TheoremFractures, BoneTomography, X-Ray ComputedAlgorithmsRadiographic Image Interpretation, Computer-AssistedMaleFemaleWhole Body Imaging

MeSH Terms

AlgorithmsBayes TheoremFemaleHumansMalePelvic BonesRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedFractures, BoneWhole Body Imaging

Keywords

Pelvic fractureRadiologyMedicineArtificial intelligenceTrauma centerBlunt traumaPelvisComputer scienceSurgeryMaleFractures, BoneHumansRadiographic Image Interpretation, Computer-AssistedBayes TheoremFemaleWhole Body ImagingPelvic BonesTomography, X-Ray ComputedArticleAlgorithms

Sustainable Development Goals

SDG 0: Gender equality