Improved Diagnosis of Systemic Sclerosis Using Nailfold Capillary Flow
- Submitting institution
-
The University of Manchester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 76480957
- Type
- E - Conference contribution
- DOI
-
10.1007/978-3-319-46726-9_40
- Title of conference / published proceedings
- Medical image computing and computer-assisted intervention -- MICCAI 2016 : 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings.
- First page
- 344
- Volume
- 9902
- Issue
- -
- ISSN
- 0302-9743
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
5
- Research group(s)
-
B - Info, Imag & DS
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Demonstrated that automated analysis of nailfold images was at least as good as human experts at detecting vessel abnormalities due to systemic sclerosis. This provided the evidence to support a successful NIHR grant application (i4i_HEI_II-LB-1117-20006, GBP565,000) to develop a low-cost imaging and interpretation system for early diagnosis of systemic sclerosis at the point of care. The project involves Salford Royal NHS Foundation Trust and Inspectis AB as partners.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
