Affiliation:
1. Tianjin Medical University Eye Hospital
Abstract
Abstract
Background
To develop a deep learning (DL) model based on preoperative optical coherence tomography (OCT) training to automatically predict the 6-month postoperative visual outcomes in patients with idiopathic epiretinal membrane (iERM).
Methods
In this retrospective cohort study, a total of 442 eyes (5304 images in total) were enrolled for the development of the DL and multimodal deep fusion network (MDFN) models. All eyes were randomized into a training dataset with 265 eyes (60.0%), a validation dataset with 89 eyes (20.1%), and an external testing dataset with the remaining 88 eyes (19.9%). The input variables for prediction included macular OCT images and various clinical data. Inception-Resnet-v2 network was employed to estimate the 6-month postoperative best-corrected visual acuity (BCVA). The clinical data and OCT parameters were used to develop a regression model for predicting postoperative BCVA. The reliability of the models was further evaluated in the testing dataset.
Results
The prediction DL algorithm showed a mean absolute error (MAE) of 0.070 logMAR and root mean square error (RMSE) of 0.11 logMAR in the testing dataset. The DL model showed promising performance with R2 = 0.80, compared to R2 = 0.50 of the regression model. The percentages of BCVA prediction errors within ± 0.20 logMAR were 94.32% in the testing dataset.
Conclusions
The OCT-based DL model demonstrated sensitive and accurate predictive ability of postoperative BCVA in iERM patients. This novel DL model has great potential to be integrated into surgical planning.
Publisher
Research Square Platform LLC