Diagnostic Efficacy of Convolutional Neural Network Based Algorithm in Predicting Intraoperative Complications and Postoperative Outcomes in Small Incision Lenticule Extraction
Trial Snapshot
- Phase
- Not Applicable
- Status
- Recruiting
- Sponsor
- Enrollment
- 1,250
- Locations
- 1
- Primary Endpoint
- AUROC of convolutional neural network in predicting progressive suction loss
Study Overview
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.
Study Design
- Study Type
- Observational
- Observational Model
- Other
- Time Perspective
- Cross Sectional
Eligibility Criteria
- Ages
- 18 Years to 45 Years (Adult)
- Sex
- All
- Accepts 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.
Arms & Interventions
Eyes with SMILE surgeries
Eyes with SMILE surgeries which were performed by surgeons with experiences.
Intervention: AI diagnostic algorithm (Diagnostic Test)
Outcomes
Primary Outcomes
AUROC of convolutional neural network in predicting progressive suction loss
Time Frame: 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
Time Frame: 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 OBL area
Time Frame: 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 postoperative refractive error
Time Frame: 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
Time Frame: Day 7
The area under the receiver operating characteristic of convolutional neural network in predicting central corneal thickness after the SMILE surgeries
Secondary Outcomes
- Sensitivity and specificity of convolutional neural network in predicting OBL area(Day 0)
- Sensitivity and specificity of convolutional neural network in predicting progressive suction loss(Day 0)
- Sensitivity and specificity of convolutional neural network in predicting effective optical zone(Day 7, Day 30, Day 90)
Investigators
Jian Xiong
Associate research fellow; Attending physician
Second Affiliated Hospital of Nanchang University