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Recruiting NCT06204926

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

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

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: AI diagnostic algorithm.
Who it may be relevant to
Registry conditions: Deep Convolutional Neural Network, Small-incision Lenticule Extraction (SMILE) Surgery, Intraoperative Complications, Postoperative Outcomes. Basic parameters: 18 years — 45 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

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Official title

Diagnostic Efficacy of Convolutional Neural Network Based Algorithm in Predicting Intraoperative Complications and Postoperative Outcomes in Small Incision Lenticule Extraction

Overview

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.

Interventions

  • 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.

Primary outcome measures

  • AUROC of convolutional neural network in predicting OBL area [Time frame: Day 0]
  • AUROC of convolutional neural network in predicting progressive suction loss [Time frame: Day 0]
  • AUROC of convolutional neural network in predicting effective optical zone [Time frame: Day 7]
  • AUROC of convolutional neural network in predicting postoperative refractive error [Time frame: Day 7]
  • AUROC of convolutional neural network in predicting postoperative central corneal thickness [Time frame: Day 7]
Secondary outcome measures (3)
  • Sensitivity and specificity of convolutional neural network in predicting OBL area [Time frame: Day 0]
  • Sensitivity and specificity of convolutional neural network in predicting progressive suction loss [Time frame: Day 0]
  • Sensitivity and specificity of convolutional neural network in predicting effective optical zone [Time frame: Day 7, Day 30, Day 90]

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.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Observational model
Other

Study locations

China · 1 center
  • The Second Affiliated Hospital of Nanchang University — Nanchang

Identifiers

NCT: NCT06204926 · [2023] No.(96)

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗