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OpenTrials
Completed

NCT Number: NCT06577012

Potential Risk Factors and Predictive Model Construction of OBL During SMILE

To explore the prediction of OBL by deep learning model in SMILE surgery

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Key information

Age range

18 year–45 year

Sex eligibility

All sexes

Study type

Observational

Primary location

The Second Affiliated Hospital of Nanchang University

Nanchang, Jiangxi, 330000, China

About this study

The DL model was used to predict the OBL area during SMILE surgery by identifying the corneal full-view images before laser scanning. The DL model developed may assist surgeons to predict the possible OBL area of patients in advance, so as to adjust some surgical parameters and reduce the formation of OBL, which can avoid negative effects on surgeons' operation and patients' postoperative visual recovery, which has important practical significance.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Small Incision Lenticule Extraction

Procedure

Small incision lenticule extraction surgeries performed by two Refractive surgery experts (Refractive surgery expert 1: YYF, Associate Professor with 10 years of experience as a refractive surgeon; Refractive surgery expert 2: GF, Associate Professor with 5 years of experience as a refractive surgeon) and a Attending ophthalmologist (XJ, Attending ophthalmologist with 1 year of experience as a refractive surgeon).

Primary outcomes

  1. The area of patients with opaque bubble layer in the SMILE surgeries

    Time frame: Day 0

    The area of patients with opaque bubble layer were observed during the SMILE surgeries.

Secondary outcomes

  1. Deep learning model

    Time frame: Day 0

    Predictive performance of deep learning model on the OBL in SMILE surgeries.

  2. ResNet model

    Time frame: Day 0

    Predictive performance of ResNet model on the OBL in SMILE surgeries.

  3. Vgg19 model

    Time frame: Day 0

    Predictive performance of Vgg model on the OBL in SMILE surgeries.

  4. U-net model

    Time frame: Day 0

    Predictive performance of U-net model on the OBL in SMILE surgeries.

Sponsors and collaborators

Lead sponsor

Second Affiliated Hospital of Nanchang University

Other

Registry information

Official study title

Predicting an Opaque Bubble Layer During Small-Incision Lenticule Extraction Surgery Based on Deep Learning

Important dates

Study start
2024
Primary completion
2024
Study completion
2024
First posted
Aug 29, 2024
Registry last updated
Aug 29, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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