Deep Learning–Based Estimation of Implantable Collamer Lens Vault Using Optical Coherence Tomography

Abstract

To develop and validate a deep learning neural network for automated measurement of implantable collamer lens (ICL) vault using anterior segment optical coherence tomography (AS-OCT). Cross-sectional retrospective study. Multicentric. A total of 2,647 AS-OCT anonymized eye scans were used from 139 eyes of 82 subjects who underwent ICL surgery in 3 different centers. Using transfer learning, a deep learning neural network algorithm was trained and validated for the regression task of estimating the ICL vault on the MS-39 OCT (CSO, Florence, Italy). A trained operator separately reviewed all OCT scans and measured the central vault using a built-in OCT caliper tool. The model was then separately tested on 191 AS-OCT scans. A Bland-Altman plot was constructed and the mean absolute percentage error (MAPE), the mean absolute error (MAE), the root mean squared error (RMSE), the Pearson correlation coefficient (r), and the determination coefficient (R2) were calculated to evaluate the strength and validity of the model. On the test set, the model achieved a MAPE of 3.42%, an MAE of 15.82 µm, an RMSE of 18.85 µm, a Pearson correlation coefficient r of +0.98 (P-Value < .00001), and a coefficient of determination R2 of +0.96. There was no significant difference between the vaults of the test set labeled by the technician versus those estimated by the model: 478 ± 95 µm versus 475 ± 97 µm, respectively, P-Value = 0.064). Using transfer learning, our deep learning neural network was able to accurately compute the ICL vault from AS-OCT scans, overcoming the limitations of an imbalanced dataset and limited training data. Such an algorithm can assist the postoperative assessment in ICL surgery. PRéCIS: This study develops and validates a deep learning model to accurately estimate implantable collamer lens vault on optical coherence tomography using transfer learning, convoluted neural networks, and regression.