AI diagnostic algorithm: The ICL procedures collected would be assessed by the algorithm. The performance of the algorithm would be assessed, including accuracy, AUC, sensitivity and specificity.
Study summary
To evaluate the diagnostic efficacy of deep learning network model in implantable collamer lens selection and prediction in a multicenter cross-sectional study
Eligibility
Sex
ALL
Min age
18 Years
Max age
45 Years
Healthy volunteers
Accepted
Inclusion Criteria:
1. Aged 18-45 years ;
2. Myopia, with or without astigmatism, annual diopter change ≤ 0.50 D for 2 consecutive years ;
3. Anterior chamber depth ≥ 2.80 mm ;
4. Corneal endothelial cell count ≥ 2000 / mm2, stable cell morphology ;
5. There were no other ocular diseases that significantly affected vision and / or systemic organic lesions that affected surgical recovery.
Exclusion Criteria:
1. There were no other ocular diseases that significantly affected vision and / or systemic organic lesions that affected surgical recovery;
2. Have a history of corneal refractive surgery or intraocular surgery ;
3. Corneal endothelial cell count is low ;
4. Those with systemic diseases ;
5. Lactating or pregnant women.
Primary outcome measure(s)
AUROC of convolutional neural network in predicting vault after ICL surgery — Day 7 The area under the receiver operating characteristic of convolutional neural network in predicting vault after ICL surgery
AUROC of convolutional neural network in predicting anterior chamber angle after ICL implantation — Day 7 The area under the receiver operating characteristic of convolutional neural network in predicting anterior chamber angle after ICL implantation
Trial sites (1)
Facility
City
Region
Status
The Second Affiliated Hospital of Nanchang University
Nanchang
Jiangxi
Recruiting
More Second Affiliated Hospital of Nanchang University trials in China
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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