DKDA: Development of a Keratoconus Detection Algorithm by Deep Learning Analysis and Its Validation on Eyestar Images
Study Details
Study Description
Brief Summary
Monocentric clinical study to develop an imaging analysis algorithm for the Eyestar 900 to identify keratoconus corneas and improve biometry for intraocular lens calculations
Condition or Disease | Intervention/Treatment | Phase |
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Detailed Description
Keratoconus is a progressive corneal ectatic disorder, characterised by thinning, protrusion and irregularity. Corneal imaging is crucial in keratoconus detection and progression analysis. Detection of keratoconus in early stages is important and has therapeutic consequence, whether to plan a surgical intervention or calculating an intraocular lens, before cataract surgery, as standard lens calculation techniques may lead to wrong results in patients with a keratoconus.
The Eyestar 900 is a swept-source OCT biometer and has the potential to be used for early keratoconus identification and progression analysis.
Study Design
Arms and Interventions
Arm | Intervention/Treatment |
---|---|
Patients with keratoconus corneas Corneal tomography on patients with keratoconus diagnosis |
Device: Corneal tomography with Eyestar 900
Non-invasive corneal tomography to develop an imaging analysis algorithm for keratoconus corneas
Device: Corneal tomography with Pentacam
Non-invasive corneal tomography to develop an imaging analysis algorithm for keratoconus corneas
Device: Biometry with IOL-Master
Non-invasive biometry for presurgical intraocular lens calculation
|
participants with healthy corneas Corneal tomography on healthy participants |
Device: Corneal tomography with Eyestar 900
Non-invasive corneal tomography to develop an imaging analysis algorithm for keratoconus corneas
Device: Corneal tomography with Pentacam
Non-invasive corneal tomography to develop an imaging analysis algorithm for keratoconus corneas
Device: Biometry with IOL-Master
Non-invasive biometry for presurgical intraocular lens calculation
|
retrospective part fully anonymised Picture data of existing 4500 patients |
Other: retrospective analysis, no intervention
retrospective analysis of 4500 existing, fully anonymised picture data
|
Outcome Measures
Primary Outcome Measures
- Keratoconus identification [2.5 years]
Classification accuracy of the keratoconus identification algorithm for the Eyestar device in comparison to the gold standard (Belin-Ambrosio Enhanced Extasia Deviation Index) BAD_D in Pentacam images.
Secondary Outcome Measures
- Feasibility in clinical practice [2.5 years]
Evaluation of the feasibility (percentage of valid measurements without errors and/or problems in image aquisition) of cornea measurements in keratoconus and healthy eyes.
Eligibility Criteria
Criteria
Inclusion Criteria:
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Patients with all stages of keratoconus
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Patients with healthy corneas
Exclusion Criteria:
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Keratoconus patients with hydrops, status following hydrops
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Patients with degenerative corneal diseases
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Patients after corneal surgery
Contacts and Locations
Locations
Site | City | State | Country | Postal Code | |
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1 | Universitätsklinik für Augenheilkunde, Inselspital | Bern | Switzerland | 3010 |
Sponsors and Collaborators
- University Hospital Inselspital, Berne
Investigators
- Principal Investigator: Früh Beatrice, Prof.Dr. med., Universitätsklinik für Augenheilkunde, Inselspital Bern
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- DKDA-E900-2020