A multi-center study of prediction of macular hole status after vitrectomy and internal limiting membrane peeling by a deep learning model

BACKGROUND: To develop a deep learning (DL) model for prediction of idiopathic macular hole (MH) status after vitrectomy and internal limiting membrane peeling (VILMP) based on optical coherence tomography (OCT) images from four ophthalmic centers. METHODS: Eyes followed up at 1 month after VILMP for full-thickness MH were included. In the internal training set, 920 preoperative macular OCT images (as the input) and post-operative status of MH (closed or open, as the output) of 256 eyes from two ophthalmic centers were used to train the DL model using VGG16 algorithm. In the external validation set, 72 preoperative macular OCT images of 36 MH eyes treated by VILMP from another two ophthalmic centers were used to validate the prediction accuracy of the DL model. RESULTS: In internal training, the mean of overall accuracy for prediction of MH status after VILMP was 84.6% with a mean area under the receiver operating characteristic (ROC) curve (AUC) of 91.04% (sensitivity 85.37% and specificity 81.99%). In external validation, the overall accuracy of predicting MH status after VILMP was 84.7% with an AUC of 89.32% (sensitivity 83.33% and specificity 87.50%). The heatmaps showed that the area critical for prediction was at the central macula, mainly at the MH and its adjacent retina. CONCLUSIONS: The DL model trained by preoperative macular OCT images can be used to predict postoperative MH status after VILMP. The prediction accuracy of our DL model has been validated by multiple ophthalmic centers.