Journal article
Diagnosing dry eye with dynamic-area high-speed videokeratoscopy
Journal of Biomedical Optics, Vol.16(7), pp.1-9
2011
PMID: 21806273
Abstract
Dry eye syndrome is one of the most commonly reported eye health conditions. Dynamic-area highspeed videokeratoscopy (DA-HSV) represents a promising alternative to the most invasive clinical methods for the assessment of the tear film surface quality (TFSQ), particularly as Placido-disk videokeratoscopy is both relatively inexpensive and widely used for corneal topography assessment. Hence, improving this technique to diagnose dry eye is of clinical significance and the aim of this work. First, a novel ray-tracing model is proposed that simulates the formation of a Placido image. This model shows the relationship between tear film topography changes and the obtained Placido image and serves as a benchmark for the assessment of indicators of the ring's regularity. Further, a novel block-feature TFSQ indicator is proposed for detecting dry eye from a series of DA-HSV measurements. The results of the new indicator evaluated on data from a retrospective clinical study, which contains 22 normal and 12 dry eyes, have shown a substantial improvement of the proposed technique to discriminate dry eye from normal tear film subjects. The best discrimination was obtained under suppressed blinking conditions. In conclusion, this work highlights the potential of the DA-HSV as a clinical tool to diagnose dry eye syndrome.
Details
- Title
- Diagnosing dry eye with dynamic-area high-speed videokeratoscopy
- Authors
- David Alonso-Caneiro (Author) - Queensland University of TechnologyJason Turuwhenua (Author) - University of AucklandD. Robert Iskander (Author) - Wrocław University of Science and TechnologyMichael J. Collins (Author) - Queensland University of Technology
- Publication details
- Journal of Biomedical Optics, Vol.16(7), pp.1-9
- Publisher
- S P I E - International Society for Optical Engineering
- Date published
- 2011
- DOI
- 10.1117/1.3598837
- ISSN
- 1560-2281
- PMID
- 21806273
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 99972393802621
- Output Type
- Journal article
Metrics
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web Of Science research areas
- Biochemical Research Methods
- Optics
- Radiology, Nuclear Medicine & Medical Imaging
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Source: InCites