Development of a Keratoconus Detection Algorithm by Deep Learning Analysis and Its Validation on Eyestar Images
试验速览
- 阶段
- 不适用
- 入组人数
- 4,800
- 试验地点
- 1
- 主要终点
- Keratoconus identification
研究概览
简要总结
Monocentric clinical study to develop an imaging analysis algorithm for the Eyestar 900 to identify keratoconus corneas and improve biometry for intraocular lens calculations
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Patients with all stages of keratoconus
- •Patients with healthy corneas
排除标准
- •Keratoconus patients with hydrops, status following hydrops
- •Patients with degenerative corneal diseases
- •Patients after corneal surgery
结局指标
主要结局
Keratoconus identification
时间窗: 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.
次要结局
- Feasibility in clinical practice(2.5 years)