To evaluate the diagnostic efficiency of the neural network in predicting complications of Small Incision Lenticule Extraction in a multi-center cross-sectional study.
The primary cause of global visual impairment currently is refractive error, and Small Incision Lenticule Extraction (SMILE) using femtosecond laser for corneal stromal lenticule extraction can alter the refractive power. However, complications such as opaque bubble layer (OBL), negative pressure detachment, and black spots may arise during the SMILE laser scanning process due to individual differences in corneal characteristics, significantly affecting the normal course of surgery and postoperative recovery. Experienced docters can often predict intraoperative complications based on scan images, patient cooperation, and other factors, but the learning curve is relatively long. At present, artificial intelligence has achieved the accuracy comparable to human physicians in the interpretation of medical imaging of many different diseases.Previously, we have trained a deep convolutional neural network for predicting intraoperative complications in SMILE procedures. The current multi-center study is designed to evaluate the efficacy of the convolutional neural network based algorithm in predicting intraoperative complications and to assess its utility in the real world.
Study Type
OBSERVATIONAL
Enrollment
1,250
The SMILE procedures collected would be assessed by the algorithm. The performance of the algorithm 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 OBL area
The area under the receiver operating characteristic of convolutional neural network in predicting opaque bubble layer area during the SMILE surgeries
Time frame: Day 0
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
Time frame: Day 0
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
Time frame: Day 7
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
Time frame: Day 0
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
Time frame: Day 0
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
Time frame: Day 7, Day 30, Day 90
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