Development of a Keratoconus Detection Algorithm by Deep Learning Analysis and Its Validation on Eyestar Images

Cataract Clinical Trial

— DKDA  

Official title:

Development of a Keratoconus Detection Algorithm by Deep Learning Analysis and Its Validation on Eyestar Images

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT04763785
• Other study ID #: DKDA-E900-2020
• Status: Active, not recruiting
• Start date: May 11, 2021
• Est. completion date: December 1, 2023

Study information

• Verified date: June 2021
• Source: University Hospital Inselspital, Berne
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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

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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 4800
• Est. completion date: December 1, 2023
• Est. primary completion date: June 1, 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

1. Patients with all stages of keratoconus

2. Patients with healthy corneas

Exclusion Criteria:

1. Keratoconus patients with hydrops, status following hydrops

2. Patients with degenerative corneal diseases

3. Patients after corneal surgery

Related Conditions & MeSH terms

• Cataract
• Corneal Ectasia
• Eye Diseases
• Keratoconus

Intervention

Device:
• Corneal tomography with Eyestar 900
Non-invasive corneal tomography to develop an imaging analysis algorithm for keratoconus corneas
• Corneal tomography with Pentacam
Non-invasive corneal tomography to develop an imaging analysis algorithm for keratoconus corneas
• Biometry with IOL-Master
Non-invasive biometry for presurgical intraocular lens calculation

Other:
• retrospective analysis, no intervention
retrospective analysis of 4500 existing, fully anonymised picture data

Locations

Country	Name	City	State
Switzerland	Universitätsklinik für Augenheilkunde, Inselspital	Bern

Sponsors (1)

Lead Sponsor	Collaborator
University Hospital Inselspital, Berne

Country where clinical trial is conducted

Switzerland, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Keratoconus identification	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.	2.5 years
Secondary	Feasibility in clinical practice	Evaluation of the feasibility (percentage of valid measurements without errors and/or problems in image aquisition) of cornea measurements in keratoconus and healthy eyes.	2.5 years