Clinical trial NCT06204926: Diagnostic Efficacy of CNN in Predicting Intraoperative Complications and Postoperative Outcomes in SMILE
Official title: Diagnostic Efficacy of Convolutional Neural Network Based Algorithm in Predicting Intraoperative Complications and Postoperative Outcomes in Small Incision Lenticule Extraction
Study type: OBSERVATIONAL
Overall status: RECRUITING
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
Lead sponsor: Second Affiliated Hospital of Nanchang University
Sponsor class: OTHER
Collaborators: name: Hangzhou Huaxia Eye Hospital
name: Nanchang Bright Eye Hospital
First posted: 2024-01-12
Enrollment: 1250
Sex: ALL
Minimum age (years): 18
Maximum age (years): 45
Standard ages: ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: CROSS_SECTIONAL
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.

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.

Eligibility criteria: 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.

Primary outcomes:
measure: AUROC of convolutional neural network in predicting OBL area
timeFrame: Day 0
measure: AUROC of convolutional neural network in predicting progressive suction loss
timeFrame: Day 0
measure: AUROC of convolutional neural network in predicting effective optical zone
timeFrame: Day 7
Secondary outcomes:
measure: Sensitivity and specificity of convolutional neural network in predicting OBL area
timeFrame: Day 0
measure: Sensitivity and specificity of convolutional neural network in predicting progressive suction loss
timeFrame: Day 0
measure: Sensitivity and specificity of convolutional neural network in predicting effective optical zone
timeFrame: Day 7
Other outcomes:
Locations:
city: Nanchang
country: China
facility: The Second Affiliated Hospital of Nanchang University
state: Jiangxi
Study officials:
References: