Deep Convolutional Neural Network, Intraoperative Complications, Postoperative Outcomes, Small-incision Lenticule Extraction (SMILE) Surgery
Conditions
Keywords
femtosecond laser small-incision lenticule extraction, Intraoperative Complications, Deep Convolutional Neural Network, Postoperative Outcomes
Brief summary
To evaluate the diagnostic efficiency of the neural network in predicting complications of Small Incision Lenticule Extraction in a multi-center cross-sectional study.
Detailed description
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.
Interventions
DIAGNOSTIC_TESTAI diagnostic algorithm
The SMILE procedures collected would be assessed by the algorithm. The performance of the algorithm would be assessed, including accuracy, AUC, sensitivity and specificity.
Sponsors
Second Affiliated Hospital of Nanchang University
Hangzhou Huaxia Eye Hospital
Nanchang Bright Eye Hospital
Study design
Observational model
OTHER
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to 45 Years
Healthy volunteers
Yes
Inclusion criteria
* 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; * Stable myopia for at least 2 years; * No contact lenses wearing for at least 2 weeks.
Exclusion criteria
* 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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| AUROC of convolutional neural network in predicting OBL area | Day 0 | 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 | Day 0 | 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 | Day 7 | 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 | Day 7 | The area under the receiver operating characteristic of convolutional neural network in predicting refractive error after the SMILE surgeries |
| AUROC of convolutional neural network in predicting postoperative central corneal thickness | Day 7 | The area under the receiver operating characteristic of convolutional neural network in predicting central corneal thickness after the SMILE surgeries |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity and specificity of convolutional neural network in predicting OBL area | Day 0 | 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 | Day 0 | 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 | Day 7, Day 30, Day 90 | ensitivity and specificity of convolutional neural network in predicting effective optical zone after the SMILE surgeries |
Countries
China
Contacts
CONTACTJian Xiong, docter
CONTACTFu Gui, docter
Outcome results
None listed