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Dynamic Structural Recovery Parameters Enhance Prediction of Visual Outcomes After Macular Hole Surgery.

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  • 1Klinik und Poliklinik für Augenheilkunde, Technische Universität München, München, Germany.

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|January 14, 2026
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Summary

Novel dynamic structural parameters and a deep learning framework accurately predict visual recovery after macular hole surgery. This automated tool aids personalized patient management and improves surgical outcome predictions.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Idiopathic full-thickness macular hole (iFTMH) surgery aims to restore visual function.
  • Predicting postoperative visual recovery is crucial for patient management and surgical planning.
  • Current prediction methods may not fully leverage dynamic structural changes observed over time.

Purpose of the Study:

  • To introduce novel dynamic structural parameters derived from optical coherence tomography (OCT).
  • To evaluate the integration of these dynamic parameters within a multimodal deep learning (DL) framework.
  • To predict postoperative visual recovery in patients with iFTMH.

Main Methods:

  • Utilized a longitudinal OCT dataset across five time stages (preoperative to 12 months).
  • Developed a stage-specific segmentation model and an automated pipeline for feature extraction (quantitative, composite, qualitative, dynamic).
  • Compared binary logistic regression models (with and without dynamic parameters) and a multimodal DL model (clinical, OCT features, raw images) for predicting best-corrected visual acuity (BCVA).

Main Results:

  • The segmentation model achieved high accuracy (mean Dice ≥ 0.89).
  • Significant predictors of BCVA included base diameter, ellipsoid zone integrity, and macular hole area.
  • Incorporating dynamic recovery rates improved logistic regression predictive performance (AUC), particularly at 3 months.
  • The multimodal DL model outperformed logistic regression, showing higher AUCs and accuracy.

Conclusions:

  • Integrating dynamic OCT-derived parameters into a multimodal DL framework significantly enhances prediction accuracy for visual outcomes.
  • This fully automated process serves as a promising clinical decision support tool.
  • Enables personalized postoperative management strategies for macular hole surgery patients.