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Generative Artificial Intelligence for Postoperative Parameters Prediction in Implantable Collamer Lens Surgery.

Journal of cataract and refractive surgery·2026
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Prior Anatomical Knowledge-guided GAN for ICL surgery postoperative prediction based on AS-OCT image.

Yinglin Zhang1, Ruiling Xi2, Risa Higashita3

  • 1Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China; School of Computer Science, University of Nottingham Ningbo China, Ningbo 315100, China.

Medical Image Analysis
|June 27, 2025
PubMed
Summary

This study introduces a novel AI model, PAK-GAN, for predicting implantable collamer lens (ICL) surgery outcomes. PAK-GAN enhances prediction accuracy and provides visual insights, improving patient risk assessment.

Keywords:
AS-OCT imageGenerative adversarial networkImplantable collamer lens (ICL)Vault prediction

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate prediction of postoperative vault in implantable collamer lens (ICL) surgery is crucial for successful outcomes.
  • Current regression-based methods lack visual postoperative observations, limiting comprehensive risk assessment.
  • Anterior segment optical coherence tomography (AS-OCT) offers high-resolution visualization of ocular structures.

Purpose of the Study:

  • To develop a generative adversarial network (GAN) capable of predicting postoperative AS-OCT images and quantifying vault.
  • To incorporate prior anatomical knowledge, specifically iris morphology, for improved ICL positioning prediction.
  • To enable both accurate vault prediction and visual observation of postoperative anterior segment structures.

Main Methods:

  • Proposed a Prior Anatomical Knowledge-guided GAN (PAK-GAN) incorporating Iris morphology perception Auxiliary Branches (IAB).
  • Utilized a Gaussian weight map in the loss function to focus on the iris root region.
  • Trained and validated the model using paired pre- and post-operative ICL images from multiple institutions.

Main Results:

  • PAK-GAN demonstrated superior accuracy in predicting postoperative vault and iris position compared to state-of-the-art methods.
  • The model achieved the best correlation and agreement with achieved vault compared to clinical methods (NK-formula, KS-formula).
  • Ablation studies confirmed the effectiveness of the proposed architectural components.

Conclusions:

  • PAK-GAN offers a significant advancement in predicting ICL surgery outcomes by integrating anatomical knowledge into a generative model.
  • The model provides both quantitative vault prediction and qualitative visual assessment, enhancing clinical decision-making.
  • This AI-driven approach holds promise for improving the safety and efficacy of ICL implantation procedures.