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Quantifying optimal inner limiting membrane peeling in macular hole surgery: a machine learning framework for

Xiang Zhang1, Hongjie Ma2, Song Lin1

  • 1Department of Ophthalmology, Eye Institute, Tianjin Medical University Eye Hospital, Tianjin Medical University Eye Institute, Tianjin, China.

BMC Medical Informatics and Decision Making
|August 15, 2025
PubMed
Summary

This study developed a machine learning model to predict the ideal internal limiting membrane (ILM) peeling radius in macular hole (MH) surgery. The tool aids surgeons by providing visual guidance for better surgical planning and outcomes.

Keywords:
Inner limiting membraneMachine learningMacular holeOptical coherence tomographyRidge regression model

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Machine Learning

Background:

  • Internal limiting membrane (ILM) peeling is crucial but challenging in macular hole (MH) surgery.
  • Current methods lack standardized tools for quantifying optimal peeling dimensions.

Purpose of the Study:

  • Develop a machine learning framework to recommend surgeon-specific ILM peeling radius in MH surgery.
  • Integrate predictive modeling with schematic visualization for operative planning.

Main Methods:

  • Retrospective analysis of 95 idiopathic MH patients undergoing vitrectomy with ILM peeling.
  • Utilized preoperative and postoperative OCT images to measure MH parameters.
  • Trained and evaluated 10 regression models, assessing performance with RMSE, MSE, MAE, and R².
  • Developed a GUI for generating ILM peeling schematic diagrams.

Main Results:

  • Ridge Regression model showed superior performance (RMSE: 0.0320, R²: 0.9427).
  • Generated schematic diagrams offered clear visual representations.
  • The tool aids surgical planning and education.

Conclusions:

  • Ridge Regression model accurately predicts optimal ILM peeling radius.
  • Schematic diagram generation improves MH surgery planning and education.
  • Machine learning and visualization tools show potential for enhancing MH surgery outcomes.