# Efficiency of an Algorithm Derived From Corneal Tomography Parameters to Distinguish Highly Susceptible Corneas to Ectasia From Healthy

> **NCT04313387** · — · COMPLETED · sponsor: **Gildasio Castello de Almeida Junior** · enrollment: 588 (actual)

## Conditions studied

- Keratoconus, Artificial Intelligence, Support Vector Machine

## Interventions

- **DIAGNOSTIC_TEST:** Corneal tomography multivariate index derived from a support vector machine (CTMVI).

## Key facts

- **NCT ID:** NCT04313387
- **Lead sponsor:** Gildasio Castello de Almeida Junior
- **Sponsor class:** OTHER
- **Phase:** —
- **Study type:** OBSERVATIONAL
- **Status:** COMPLETED
- **Start date:** 2012-01-01
- **Primary completion:** 2018-01-01
- **Final completion:** 2018-01-01
- **Target enrollment:** 588 (ACTUAL)
- **Last updated:** 2020-03-18

## Collaborators

- [object Object]

## Primary source

ClinicalTrials.gov registry: https://clinicaltrials.gov/study/NCT04313387

## Citation

> US National Library of Medicine, ClinicalTrials.gov registration NCT04313387, "Efficiency of an Algorithm Derived From Corneal Tomography Parameters to Distinguish Highly Susceptible Corneas to Ectasia From Healthy". Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/clinical/NCT04313387. Licensed CC0.

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