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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Implantable collamer lens (ICL) surgery is a common refractive procedure.
  • Accurate prediction of postoperative outcomes is crucial for successful ICL implantation.
  • Preoperative anterior segment optical coherence tomography (AS-OCT) provides valuable anatomical information.

Purpose of the Study:

  • To develop and validate a generative artificial intelligence (AI) model for predicting multiple postoperative parameters following ICL surgery.
  • To utilize preoperative AS-OCT images as input for the AI model.
  • To assess the accuracy and reliability of AI-driven predictions for key surgical outcomes.

Main Methods:

  • A retrospective study involving 1010 patients (1585 eyes) for horizontal ICL and 86 patients (86 eyes) for vertical ICL implantation.
  • Development of a Generative Adversarial Network (ICL-GAN) to predict postoperative structures from preoperative AS-OCT.
  • Measurement of postoperative parameters (vault, AOD500, TIA500) from predicted structures and evaluation of prediction error (MAE, RMSE).

Main Results:

  • ICL-GAN demonstrated strong correlations between predicted and achieved vault values for horizontal ICL implantation across different lens sizes (r=0.659 to 0.799, p<0.01).
  • The AI model achieved minimal prediction errors comparable to established formulas (NK, KS) for vault prediction.
  • Good correlation and agreement were observed for predicted AOD500 and TIA500 values, with superior performance on vertical implantation data.

Conclusions:

  • Generative artificial intelligence, specifically ICL-GAN, effectively predicts multiple postoperative parameters after ICL surgery.
  • The AI model shows significant potential for enhancing surgical planning and improving patient outcomes in refractive lens surgery.
  • Preoperative AS-OCT imaging combined with AI offers a promising approach for personalized ICL surgery.