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

## TL;DR

This study shows that adding dynamic OCT parameters to a deep learning model improves predictions of visual recovery after macular hole surgery.

## Contribution

The novel use of dynamic structural recovery parameters in a multimodal deep learning framework for predicting visual outcomes.

## Key findings

- Segmentation models achieved high accuracy (mean Dice ≥ 0.89) across all OCT time points.
- Dynamic recovery rates improved logistic regression AUC, especially at 3 months post-surgery.
- Multimodal DL models outperformed regression models by up to 0.12 in AUC.

## Abstract

The purpose of this study was to introduce novel dynamic structural parameters and evaluate their integration within a multimodal deep learning (DL) framework for predicting postoperative visual recovery in patients with idiopathic full-thickness macular hole (iFTMH).

We utilized a publicly available longitudinal optical coherence tomography (OCT) dataset at five stages (preoperative, 2 weeks, 3 months, 6 months, and 12 months). A stage-specific segmentation model delineated related structures, and an automated pipeline extracted quantitative, composite, qualitative, and dynamic features. Binary logistic regression models, constructed with and without dynamic parameters, assessed their incremental predictive value for best-corrected visual acuity (BCVA). A multimodal DL model combining clinical variables, OCT-derived features, and raw OCT images was developed and benchmarked against regression models.

The segmentation model achieved high accuracy across all time points (mean Dice ≥ 0.89). Univariate and multivariate analyses identified base diameter, ellipsoid zone integrity, and macular hole area as significant BCVA predictors (P < 0.05). Incorporating dynamic recovery rates consistently improved logistic regression area under the receiver operating characteristic curve (AUC), especially at the 3-month follow-up. The multimodal DL model outperformed logistic regression, yielding higher AUCs and overall accuracy at each stage. The difference is as high as 0.12, demonstrating the complementary value of raw image volume and dynamic parameters.

Integrating dynamic parameters into the multimodal DL model significantly enhances the accuracy of predictions. This fully automated process therefore represents a promising clinical decision support tool for personalized postoperative management in macular hole surgery.

The integration of dynamic OCT-derived structural parameters into the multimodal DL framework enables personalized prediction of visual outcomes after macular hole surgery.