Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions is a research paper published in Scientific Reports (2021). On theSindex it has a DataRank of 0. It has been cited 40 times.
Scored on demand from live citation data
Linked data & code
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
National Institutes of Health
Grant: R03HD094577
National Institutes of Health
Grant: R01HD079076
National Institutes of Health
Grant: K24DA029262
NINDS NIH HHS
Grant: K23 NS104211
NINDS NIH HHS
Grant: L30 NS108301
National Institutes of Health
Grant: 5R01HD079076-04
Neuromuscular Mechanisms Underlying Poor Recovery from Whiplash Injuries
National Institutes of Health
Grant: 5K24DA029262-09
Neuroimaging and Mentoring in Translational Pain Research
National Institutes of Health
Grant: 7R03HD094577-03
MRI and machine learning to improve early prognosis and clinical management after spinal cord injury
National Institutes of Health
Grant: 5K23NS104211-03
Neuroimaging-Based Brain and Spinal Cord Biomarkers for Cervical Radiculopathy
FWCI
2.05
Citation Percentile
0.9%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals