Diagnostic Efficacy of CNN in Predicting Intraoperative Complications in SMILE

Intraoperative Complications Clinical Trial

Official title:

Diagnostic Efficacy of Convolutional Neural Network Based Algorithm in Predicting Intraoperative Complications in Small-incision Lenticule Extraction

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06204926
• Other study ID #: ?2023? No.(96)
• Status: Recruiting
• Start date: June 15, 2021
• Est. completion date: December 2024

Study information

• Verified date: March 2024
• Source: Second Affiliated Hospital of Nanchang University
• Contact: Jian Xiong, docter
• Phone: 18170906556
• Email: 894040417[@]qq.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1250
• Est. completion date: December 2024
• Est. primary completion date: December 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years to 45 Years

Eligibility

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.

Related Conditions & MeSH terms

• Intraoperative Complications
• Surgical Wound

Intervention

Diagnostic Test:
• AI 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.

Locations

Country	Name	City	State
China	The Second Affiliated Hospital of Nanchang University	Nanchang	Jiangxi

Sponsors (3)

Lead Sponsor	Collaborator
Second Affiliated Hospital of Nanchang University	Hangzhou Huaxia Eye Hospital, Nanchang Bright Eye Hospital

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	Day 0
Primary	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	Day 0
Secondary	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	Day 0
Secondary	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	Day 0