Dry Eye Subtype Classification Using Videokeratography and Deep Learning

Norihiko Yokoi; Natsuki Kusada; Hiroaki Kato; Yuki Furusawa; Chie Sotozono; Georgi As Georgiev

doi:10.3390/diagnostics14010052

Dry Eye Subtype Classification Using Videokeratography and Deep Learning

Diagnostics (Basel). 2023 Dec 26;14(1):52. doi: 10.3390/diagnostics14010052.

Authors

Norihiko Yokoi¹, Natsuki Kusada¹, Hiroaki Kato¹, Yuki Furusawa¹, Chie Sotozono¹, Georgi As Georgiev²

Affiliations

¹ Department of Ophthalmology, Kyoto Prefectural University of Medicine, Kyoto 602-0841, Japan.
² Department of Optics and Spectroscopy, Faculty of Physics, St. Kliment Ohridski University of Sofia, 1164 Sofia, Bulgaria.

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: artificial intelligence; deep learning; dry eye; subtype classification; tear-film-oriented diagnosis; videokeratography.

Grants and funding

This research was supported in part by JSPS KAKENHI for Scientific Research from the Japanese Ministry of Education, Culture, Sports, Science and Technology (grant number: JP20K09794). The funding organization had no role in the design or conduct of this research. G. As. Georgiev acknowledges the support of the European Union NextGenerationEU, through the National Recovery and Resilience Plan of the Republic of Bulgaria (Project No.: BG-RRP-2.004-0008-C01).