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Predicting an opaque bubble layer during small-incision lenticule extraction surgery based on deep learning.

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Deep learning accurately predicts opaque bubble layer (OBL) formation during small incision lenticule extraction (SMILE) surgery. A novel residual neural network model shows high predictive power for clinical use.

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Area of Science:

  • Ophthalmology
  • Medical Artificial Intelligence
  • Surgical Technology

Background:

  • Femtosecond laser Small Incision Lenticule Extraction (SMILE) is a refractive surgery procedure.
  • Opaque Bubble Layer (OBL) formation is a potential complication during SMILE surgery.
  • Predicting OBL formation can improve surgical outcomes and patient safety.

Purpose of the Study:

  • To develop and evaluate a deep learning model for predicting OBL formation during femtosecond laser SMILE surgery.
  • To assess the predictive performance of different deep neural network architectures.

Main Methods:

  • A retrospective cross-sectional study utilizing surgical videos from SMILE procedures.
  • Development of a deep neural network model based on a SENet-ResNet architecture.
  • Model training and validation using partitioned datasets, with performance evaluated by mean absolute error, Pearson's correlation coefficient, and determination coefficient.

Main Results:

  • A modified deep residual neural network with channel attention achieved the best predictive performance.
  • The model demonstrated strong correlation with Photoshop-based measurements (R²=0.676).
  • Other models like ResNet and Vgg19 showed satisfactory results, while U-net performed poorly.

Conclusions:

  • A novel deep residual neural network model effectively predicts OBL formation using pre-operative corneal images.
  • The model exhibits significant predictive power, indicating potential clinical applicability in SMILE surgery.
  • This AI-driven approach can aid surgeons in anticipating and managing OBL during SMILE procedures.