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

NCT04763785 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 4800

Last updated 2021-09-30

No results posted yet for this study

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

Conditions

  • Keratoconus
  • Eye Diseases
  • Cataract
  • Corneal Ectasia

Interventions

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

OTHER

retrospective analysis, no intervention

retrospective analysis of 4500 existing, fully anonymised picture data

Sponsors & Collaborators

  • Insel Gruppe AG, University Hospital Bern

    lead OTHER

Principal Investigators

  • Früh Beatrice, Prof.Dr. med. · Universitätsklinik für Augenheilkunde, Inselspital Bern

Eligibility

Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2021-05-11
Primary Completion
2023-06-01
Completion
2023-12-01

Countries

  • Switzerland

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04763785 on ClinicalTrials.gov