Detecting dry eye from ocular surface videos based on deep learning.

OBJECTIVE

To assess the performance of convolutional neural networks (CNNs) for automated diagnosis of dry eye (DE) in patients undergoing video keratoscopy based on single ocular surface video frames.

DESIGN

This retrospective cohort study included 244 ocular surface videos from 244 eyes of 244 subjects based on corneal topography. A total of 116 eyes were normal while 128 eyes had DE based on clinical evaluations.

METHODS

We developed a deep transfer learning model to directly identify DE from ocular surface videos. We evaluated the performance of the CNN model based on objective accuracy metrics. We assessed the clinical relevance of the findings by evaluating class activations maps.

MAIN OUTCOME MEASURE: Area under the receiver operating characteristics curve (AUC), accuracy, specificity, and sensitivity.

RESULTS

The AUC of the model for discriminating normal eyes from eyes with DE was 0.98. Network activation maps suggested that the lower paracentral cornea was the most important region for detection of DE by the CNN model.

CONCLUSIONS

Deep transfer learning achieved a high diagnostic accuracy in detecting DE based on non-invasive ocular surface videos at levels that may prove useful in clinical practice.