Predictive Performance of a Generative Model for Corneal Tomography After Implantable Collamer Lens Implantation
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 818
- 试验地点
- 1
- 主要终点
- AUROC of convolutional neural network in predicting vault after ICL surgery
研究概览
简要总结
To evaluate the efficacy of a corneal tomography Imaging model in predicting postoperative vault based on preoperative corneal topography in Implantable Collamer Lens (ICL) surgery.
详细描述
Accurate vault prediction is crucial for Implantable Collamer Lens (ICL) surgery safety and efficacy. Current methods using preoperative biometrics and regression formulas show limited accuracy due to parameter variability and incomplete utilization of corneal topography data. To address this, we developed a deep learning model that predicts postoperative vault while generating anterior chamber morphology images from preoperative data, enabling personalized surgical planning.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 18 Years 至 45 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •(1) stable myopia (≤0.50D/year change for 2 years), (2) ACD ≥2.80mm, (3) intact corneal endothelium (≥2000 cells/mm²), and (4) no confounding ocular/systemic conditions.
排除标准
- •(1) glaucoma-spectrum disorders or retinal vasculopathies, (2) prior corneal/intraocular surgery, (3) compromised corneal endothelium, (4) uncontrolled systemic diseases, and (5) pregnancy/lactation.
结局指标
主要结局
AUROC of convolutional neural network in predicting vault after ICL surgery
时间窗: Day 7
The area under the receiver operating characteristic of convolutional neural network in predicting vault after ICL surgery
次要结局
- Sensitivity and specificity of convolutional neural network in predicting Vault after ICL implantation(Day 7)
研究者
Fu Gui
Associate research fellow; Attending physician
Second Affiliated Hospital of Nanchang University