The Role of Artificial Intelligence in Epiretinal Membrane Care: A Scoping Review

Artificial intelligence-based assessment of imaging biomarkers in epiretinal membrane surgery

PurposeThis study investigated the applicability of a validated AI-algorithm for analyzing different retinal biomarkers in eyes affected by epiretinal membranes (ERMs) before and after surgery.MethodsA retrospective study included 40 patients surgically treated for ERMs removal between November 2022 and January 2024. Pars plana vitrectomy with ERM/ILM peeling was performed by a single experienced surgeon. A validated AI algorithm was used to analyze OCT scans, focusing on intraretinal fluid (IRF) and subretinal fluid (SRF) volumes, external limiting membrane (ELM) and ellipsoid zone (EZ) interruption percentages and hyper-reflective foci (HRF) counts.ResultsPostoperative best corrected visual acuity (BCVA) significantly improved (p < 0.01), and central macular thickness (CMT) decreased from 483.61 ± 96.32 to 386.82 ± 94.86 µm (p = 0.001). IRF volume reduced from 0.283 ± 0.39 mm3 to 0.142 ± 0.27 mm3 (p = 0.036) particularly in the central 1 mm-circle. SRF, HRF and EZ/ELM interruption percentages exhibited no significant differences (p > 0.05). Significant correlations (p < 0.05) were found between preoperative BCVA and postoperative BCVA (r = 0.45); CMT reduction and postoperative BCVA (r = 0.42), preoperative IRF and Visual Recovery (r = -0.48), ELM and EZ interruption and visual recovery (r = -0.43 and r = -0.47 respectively). Multivariate analysis demonstrated that fluid distribution, especially in the central subfield, correlated with BCVA recovery (R2 = 0.38; p < 0.05; Pillai's Trace = 0.79).ConclusionThe study highlights AI's potential in quantifying OCT biomarkers in ERMs surgery. The findings suggest that improved BCVA is associated with reduced CMT, IRF, and redistribution of IRF towards the periphery. EZ and ELM integrities remain crucial prognostic factors, emphasizing the importance of the preoperative analysis.

Supervised machine learning statistical models for visual outcome prediction in macular hole surgery: a single-surgeon, standardized surgery study

PURPOSE

To evaluate the predictive accuracy of various machine learning (ML) statistical models in forecasting postoperative visual acuity (VA) outcomes following macular hole (MH) surgery using preoperative optical coherence tomography (OCT) parameters.

METHODS

This retrospective study included 158 eyes (151 patients) with full-thickness MHs treated between 2017 and 2023 by the same surgeon and using the same intraoperative surgical technique. Data from electronic medical records and OCT scans were extracted, with OCT-derived qualitative and quantitative MH characteristics recorded. Six supervised ML models-ANCOVA, Random Forest (RF) regression, K-Nearest Neighbor, Support Vector Machine, Extreme Gradient Boosting, and Lasso regression-were trained using an 80:20 training-to-testing split. Model performance was evaluated on an independent testing dataset using the XLSTAT software. In total, the ML statistical models were trained and tested on 14,652 OCT data points from 1332 OCT images.

RESULTS

Overall, 91% achieved MH closure post-surgery, with a median VA gain of -0.3 logMAR units. The RF regression model outperformed other ML models, achieving the lowest mean square error (MSE = 0.038) on internal validation. The most significant predictors of VA were postoperative MH closure status (variable importance = 43.078) and MH area index (21.328). The model accurately predicted the post-operative VA within 0.1, 0.2 and 0.3 logMAR units in 61%, 78%, and 87% of OCT images, respectively.

CONCLUSION

The RF regression model demonstrated superior predictive accuracy for forecasting postoperative VA, suggesting ML-driven approaches may improve surgical planning and patient counselling by providing reliable insights into expected visual outcomes based on pre-operative OCT features.

CLINICAL TRIAL REGISTRATION NUMBER

Not applicable.