Predicting an opaque bubble layer during small-incision lenticule extraction surgery based on deep learning.

Aim: This study aimed to predict the formation of OBL during femtosecond laser SMILE surgery by employing deep learning technology.

Methods: This was a cross-sectional, retrospective study conducted at a university hospital. Surgical videos were randomly divided into a training (3,271 patches, 73.64%), validation (704 patches, 15.85%), and internal verification set (467 patches, 10.51%). An artificial intelligence (AI) model was developed using a SENet-based residual regression deep neural network. Model performance was assessed using the mean absolute error (E _MA ), Pearson's correlation coefficient (r), and determination coefficient (R ² ).

Results: Four distinct types of deep neural network models were established. The modified deep residual neural network prediction model with channel attention built on the PyTorch framework demonstrated the best predictive performance. The predicted OBL area values correlated well with the Photoshop-based measurements (E _MA = 0.253, r = 0.831, R ² = 0.676). The ResNet (E _MA = 0.259, r = 0.798, R ² = 0.631) and Vgg19 models (E _MA = 0.31, r = 0.758, R ² = 0.559) both displayed satisfactory predictive performance, while the U-net model (E _MA = 0.605, r = 0.331, R ² = 0.171) performed poorest.

Conclusion: We used a panoramic corneal image obtained before the SMILE laser scan to create a unique deep residual neural network prediction model to predict OBL formation during SMILE surgery. This model demonstrated exceptional predictive power, suggesting its clinical applicability across a broad field.