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.
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.
Study Type
OBSERVATIONAL
Enrollment
818
The ICL procedures collected would be assessed by the corneal tomography generation model. The performance of the model would be assessed, including accuracy,AUC, sensitivity and specificity.
The Second Affiliated Hospital of Nanchang University
Nanchang, Jiangxi, China
RECRUITINGAUROC of convolutional neural network in predicting vault after ICL surgery
The area under the receiver operating characteristic of convolutional neural network in predicting vault after ICL surgery
Time frame: Day 7
Sensitivity and specificity of convolutional neural network in predicting Vault after ICL implantation
Sensitivity and specificity of convolutional neural network in predicting Vault after ICL implantation
Time frame: Day 7
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