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

Study Overview
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Brief Summary
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Monocentric clinical study to develop an imaging analysis algorithm for the Eyestar 900 to identify keratoconus corneas and improve biometry for intraocular lens calculations

Detailed Description
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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.

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

Conditions
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Keratoconus, Cataract, Eye Diseases, Corneal Ectasia

Intervention / Treatment
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* Device: Corneal tomography with Eyestar 900
* Device: Corneal tomography with Pentacam
* Device: Biometry with IOL-Master
* Other: retrospective analysis, no intervention


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Patients with all stages of keratoconus Patients with healthy corneas Exclusion Criteria: Keratoconus patients with hydrops, status following hydrops Patients with degenerative corneal diseases Patients after corneal surgery
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Patients with keratoconus corneas<br>Corneal tomography on patients with keratoconus diagnosis | Device: Corneal tomography with Eyestar 900<br>* Non-invasive corneal tomography to develop an imaging analysis algorithm for keratoconus corneas<br>Device: Corneal tomography with Pentacam<br>* Non-invasive corneal tomography to develop an imaging analysis algorithm for keratoconus corneas<br>Device: Biometry with IOL-Master<br>* Non-invasive biometry for presurgical intraocular lens calculation<br>|
| participants with healthy corneas<br>Corneal tomography on healthy participants | Device: Corneal tomography with Eyestar 900<br>* Non-invasive corneal tomography to develop an imaging analysis algorithm for keratoconus corneas<br>Device: Corneal tomography with Pentacam<br>* Non-invasive corneal tomography to develop an imaging analysis algorithm for keratoconus corneas<br>Device: Biometry with IOL-Master<br>* Non-invasive biometry for presurgical intraocular lens calculation<br>|
| retrospective part<br>fully anonymised Picture data of existing 4500 patients | Other: retrospective analysis, no intervention<br>* retrospective analysis of 4500 existing, fully anonymised picture data<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Keratoconus, Eye Diseases, Cataract, Corneal Imaging, Corneal Topography, Corneal Tomography, Corneal Ectasia