🏆 Finalist — NIH Data Sharing Index (“S-Index”) Challenge
Pilot corpus: NIH-funded biomedical datasets. Any DOI can be scored on demand — network effects sharpen as coverage grows.

Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images

bioRxiv (Cold Spring Harbor Laboratory)(2020)10.1101/2020.02.26.967208Source: DataRank Database

Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2020). On theSindex it has a DataRank of 0.425. It has been cited 16 times.

N/A
0.425DataRank · unranked

Scored on demand from live citation data

Open Access16 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 (5 grants)OA: greenTopics (25)IDs (PubMed, 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.

      DataRank Breakdown

      Base Score 100%Citation Network 0%

      Base Score Contribution

      0.425

      From this paper's citation signal

      Citation Network Contribution

      0

      Citation network not refreshed for this result

      This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.

      Learn more about DataRank methodology →
      Live enrichment

      National Science Foundation

      Grant: 1524535

      III: Small: Data Analysis in the Cloud with Guaranteed and Explainable Performance

      National Institutes of Health

      Grant: 5R35GM128638-03

      Opening the Black Box of Machine Learning Models

      National Institutes of Health

      Grant: 5R35GM128638-05

      Opening the Black Box of Machine Learning Models

      National Institutes of Health

      Grant: 1K23EY029246-01

      Epidemiology and clinical outcomes of diabetic macular edema

      National Science Foundation

      Grant: 1535565

      AitF: FULL: Query Processing with Optimal Communication Cost

      Fields of Study

      Retinal Imaging and AnalysisGlaucoma and retinal disordersRetinal Diseases and Treatments03 medical and health sciences0302 clinical medicine

      Keywords

      GlaucomaOptical coherence tomographyArtificial intelligenceComputer scienceData setOptic discModalFundus (uterus)Intraocular pressureMedical diagnosisSegmentationPopulationMachine learningPattern recognition (psychology)OphthalmologyMedicineRadiologyOptic DiskHumansTomography, Optical Coherence

      Sustainable Development Goals

      SDG 3: 3. Good health