Dry Eye Subtype Classification Using Videokeratography and Deep Learning

Abstract

We previously reported on ‘Tear Film Oriented Diagnosis’ (TFOD), a method for the dry eye (DE) subtype classification using fluorescein staining and an examination of fluorescein breakup patterns via slit-lamp biomicroscopy. Here, we report ‘AI-supported TFOD’, a novel non-invasive method for DE subtype classification using videokeratography (VK) and “Blur Value” (BV), a new VK indicator of the extent of blur in Meyer-ring images and deep learning (DL). This study involved 243 eyes of 243 DE cases (23 males and 220 females; mean age: 64.4 ± 13.9 (SD) years)—i.e., 31 severe aqueous-deficient DE (sADDE) cases, 73 mild-to-moderate ADDE (m/mADDE) cases, 84 decreased wettability DE (DWDE) cases, and 55 increased evaporation DE (IEDE) cases diagnosed via the fluorescein-supported TFOD pathway. For DL, a 3D convolutional neural network classification model was used (i.e., the original image and BV data of eyes kept open for 7 s were randomly divided into training data (146 cases) and the test data (97 cases), with the training data increased via data augmentation and corresponding to 2628 cases). Overall, the DE classification accuracy was 78.40%, and the accuracies for the subtypes sADDE, m/mADDE, DWDE, and IEDE were 92.3%, 79.3%, 75.8%, and 72.7%, respectively. ‘AI-supported TFOD’ may become a useful tool for DE subtype classification.

Keywords

• dry eye
• subtype classification
• videokeratography
• deep learning
• artificial intelligence
• tear-film-oriented diagnosis