Diagnostic Efficacy of CNN in Predicting Intraoperative Complications and Postoperative Outcomes in SMILE

Recruiting

• Conditions: Deep Convolutional Neural Network; Postoperative Outcomes; Small-incision Lenticule Extraction (SMILE) Surgery; Intraoperative Complications
• Interventions: Diagnostic Test: AI diagnostic algorithm

• Registration Number: NCT06204926
• Lead Sponsor: Second Affiliated Hospital of Nanchang University

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 Timeline

• Study Start: 2021-06-15
• Study First Submitted: 2024-01-03
• Study First Submitted QC: 2024-01-03
• Study First Posted: 2024-01-12
• Status Verified: 2025-01
• Last Update Submitted: 2025-01-13
• Last Update Posted: 2025-01-15
• Primary Completion: 2025-12
• Study Completion: 2025-12

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1250

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Eyes with SMILE surgeries	AI diagnostic algorithm	Eyes with SMILE surgeries which were performed by surgeons with experiences.

Primary Outcome Measures

Name	Time	Method
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 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

Secondary Outcome Measures

Name	Time	Method
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 effective optical zone	Day 7	ensitivity and specificity of convolutional neural network in predicting effective optical zone after 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

Trial Locations

Locations (1)

The Second Affiliated Hospital of Nanchang University; 🇨🇳 Nanchang, Jiangxi, China