To evaluate the diagnostic efficiency of the neural network in predicting complications of Small Incision Lenticule Extraction in a multi-center cross-sectional study.
A condition in which the spherical equivalent refractive error of an eye is ≤-0.50 D when ocular accommodation is relaxed;
Age ≥18 years;
Spherical equivalent (SE) ≥-10.0D;
Corrected distance visual acuity (CDVA) ≥16/20;
The presence or history of eye conditions other than myopia and astigmatism, such as keratoconus or external eye injury;
A history of eye surgery;
The presence or history of systemic diseases.
Patients from clinics in different eye centers across China. Each subject must with complete surgical video recording and medical records.
Other
Cross-sectional
AI diagnostic algorithm
Diagnostic testThe SMILE procedures collected would be assessed by the algorithm. The performance of the algorithm would be assessed, including accuracy, AUC, sensitivity and specificity.
AUROC of convolutional neural network in predicting OBL area
The area under the receiver operating characteristic of convolutional neural network in predicting opaque bubble layer area during the SMILE surgeries
AUROC of convolutional neural network in predicting progressive suction loss
The area under the receiver operating characteristic of convolutional neural network in predicting progressive suction loss during the SMILE surgeries
AUROC of convolutional neural network in predicting effective optical zone
The area under the receiver operating characteristic of convolutional neural network in predicting effective optical zone after the SMILE surgeries
AUROC of convolutional neural network in predicting postoperative refractive error
The area under the receiver operating characteristic of convolutional neural network in predicting refractive error after the SMILE surgeries
Sensitivity and specificity of convolutional neural network in predicting OBL area
Sensitivity and specificity of convolutional neural network in predicting opaque bubble layer area during the SMILE surgeries
Sensitivity and specificity of convolutional neural network in predicting progressive suction loss
Sensitivity and specificity of convolutional neural network in predicting progressive suction loss during the SMILE surgeries
Sensitivity and specificity of convolutional neural network in predicting effective optical zone
ensitivity and specificity of convolutional neural network in predicting effective optical zone after the SMILE surgeries
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Second Affiliated Hospital of Nanchang University
Lead sponsor
Hangzhou Huaxia Eye Hospital
Collaborator
Nanchang Bright Eye Hospital
Collaborator
