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

NCT06204926 | Recruiting

• 1 other identifier: [2023] No.(96)
• Study Type: observational
• Target: 1,250
• Locations: 1 country
• Sites: 1

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.

Conditions

Deep Convolutional Neural Network; Small-incision Lenticule Extraction (SMILE) Surgery; Intraoperative Complications; Postoperative Outcomes

Keywords

femtosecond laser small-incision lenticule extraction; Intraoperative Complications; Deep Convolutional Neural Network; Postoperative Outcomes

Outcome Measures

Primary Outcomes (5)

• 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

• 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

• 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

  Day 7

• AUROC of convolutional neural network in predicting postoperative refractive error

  The area under the receiver operating characteristic of convolutional neural network in predicting refractive error after the SMILE surgeries

  Day 7

• AUROC of convolutional neural network in predicting postoperative central corneal thickness

  The area under the receiver operating characteristic of convolutional neural network in predicting central corneal thickness after the SMILE surgeries

  Day 7

Secondary Outcomes (3)

• 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

Study Arms (1)

Eyes with SMILE surgeries

Eyes with SMILE surgeries which were performed by surgeons with experiences.

Diagnostic Test: AI diagnostic algorithm

Interventions

AI diagnostic algorithm DIAGNOSTIC_TEST

The SMILE procedures collected would be assessed by the algorithm. The performance of the algorithm would be assessed, including accuracy, AUC, sensitivity and specificity.

Eyes with SMILE surgeries

Eligibility Criteria

• Age: 18 Years - 45 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

Patients from clinics in different eye centers across China. Each subject must with complete surgical video recording and medical records.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Second Affiliated Hospital of Nanchang University: lead
• Hangzhou Huaxia Eye Hospital: collaborator
• Nanchang Bright Eye Hospital: collaborator

Study Sites (1)

The Second Affiliated Hospital of Nanchang University

Nanchang, Jiangxi, 330000, China

RECRUITING

MeSH Terms

Conditions

Smiling; Intraoperative Complications

Condition Hierarchy (Ancestors)

Facial Expression; Nonverbal Communication; Communication; Behavior; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Jian Xiong, docter

CONTACT

18170906556; 894040417@qq.com

Fu Gui, docter

CONTACT

13879101919; 564436578@qq.com

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate research fellow; Attending physician

Study Record Dates

• First Submitted: January 3, 2024
• First Posted: January 12, 2024
• Study Start: June 15, 2021
• Primary Completion (Estimated): December 1, 2027
• Study Completion (Estimated): December 1, 2027
• Last Updated: April 24, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)