Predictive Performance of a Generative Model for Corneal Tomography After ICL Implantation
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-45
SponsorSecond Affiliated Hospital of Nanchang University
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
None
Disqualifiers
None
Trial population
Patients from clinics in different eye centers across China. Each subject must with complete surgical video recording and medical records.
Trial design
Design model
Other
Time perspective
Cross-sectional
Treatments tested in this trial
Corneal tomography generation model after ICL surgery
Diagnostic testThe 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.
Treatment groups
818 Participants
are divided into 1 treatment groupGroup A: Eyes with ICL surgeries1 intervention
Trial outcomes
Primary outcomes
1
AUROC 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 7Secondary outcomes
1
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 7Sponsors and contacts
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Source ClinicalTrials.gov