Prediction of vitreomacular traction syndrome outcomes with deep learning: A pilot study

Abstract

Purpose

To investigate the potential of an Optical Coherence Tomography (OCT) based Deep-Learning (DL) model in the prediction of Vitreomacular Traction (VMT) syndrome outcomes.

Design

A single-centre retrospective review.

Methods

Records of consecutive adult patients attending the Royal Adelaide Hospital vitreoretinal clinic with evidence of spontaneous VMT were reviewed from January 2019 until May 2022. All patients with evidence of causes of cystoid macular oedema or secondary causes of VMT were excluded. OCT scans and outcome data obtained from patient records was used to train, test and then validate the models.

Results

For the deep learning model, ninety-five patient files were identified from the OCT (SPECTRALIS system; Heidelberg Engineering, Heidelberg, Germany) records. 25% of the patients spontaneously improved, 48% remained stable and 27% had progression of their disease, approximately. The final longitudinal model was able to predict ‘improved’ or ‘stable’ disease with a positive predictive value of 0.72 and 0.79, respectively. The accuracy of the model was greater than 50%.

Conclusions

Deep-learning models may be utilised in real-world settings to predict outcomes of VMT. This approach requires further investigation as it may improve patient outcomes by aiding ophthalmologists in cross-checking management decisions and reduce the need for unnecessary interventions or delays.