Diagnosability of Keratoconus Using Deep Learning With Placido Disk-Based Corneal Topography

Abstract

Purpose: Placido disk-based corneal topography is still most commonly used in daily practice. This study was aimed to evaluate the diagnosability of keratoconus using deep learning of a color-coded map with Placido disk-based corneal topography.Methods: We retrospectively examined 179 keratoconic eyes [Grade 1 (54 eyes), 2 (52 eyes), 3 (23 eyes), and 4 (50 eyes), according to the Amsler-Krumeich classification], and 170 age-matched healthy eyes, with good quality images of corneal topography measured with a Placido disk corneal topographer (TMS-4TM, Tomey). Using deep learning of a color-coded map, we evaluated the diagnostic accuracy, sensitivity, and specificity, for keratoconus screening and staging tests, in these eyes.Results: Deep learning of color-coded maps exhibited an accuracy of 0.966 (sensitivity 0.988, specificity 0.944) in discriminating keratoconus from normal eyes. It also exhibited an accuracy of 0.785 (0.911 for Grade 1, 0.868 for Grade 2, 0.920 for Grade 3, and 0.905 for Grade 4) in classifying the stage. The area under the curve value was 0.997, 0.955, 0.899, 0.888, and 0.943 as Grade 0 (normal) to 4 grading tests, respectively.Conclusions: Deep learning using color-coded maps with conventional corneal topography effectively distinguishes between keratoconus and normal eyes and classifies the grade of the disease, indicating that this will become an aid for enhancing the diagnosis and staging ability of keratoconus in a clinical setting.

Artificial intelligence and corneal diseases

“…Two studies used only topography images to detect and stage KCN [43 ▪ ,44]. Both studies had high overall accuracies (79% [43 ▪ ] 93% [44]), with better performance on color-coded maps than the raw topographic indices.…”

Artificial intelligence-assisted diagnosis of ocular surface diseases

“…This AI system effectively discriminated KC from normal corneas (99.1% accuracy) and further classified the grade of the disease (87.4% accuracy). Two studies used topography images to detect and stage KC (Kamiya et al, 2021a;Chen et al, 2021). Both studies had high overall accuracies [78.5% (Kamiya et al, 2021a), 93% (Chen et al, 2021)], with better performance on color-coded maps than the raw topographic indices.…”

“…Two studies used topography images to detect and stage KC (Kamiya et al, 2021a;Chen et al, 2021). Both studies had high overall accuracies [78.5% (Kamiya et al, 2021a), 93% (Chen et al, 2021)], with better performance on color-coded maps than the raw topographic indices. Malyugin et al (2021) trained an ML model using topography images and visual acuity to classify KC stages based on the Amsler-Krumeich classification system.…”

Keratoviz-A multistage keratoconus severity analysis and visualization using deep learning and class activated maps

“…Contribution in [21] led to the implementation and application of deep learning techniques to develop an intelligent Pterygium diagnosis system. Also in 2021, contributors of [22] used disk-based corneal topography as their primary dataset on which they applied deep learning models to detect the presence of keratoconus. A survey on artificial intelligence (AI) in ophthalmology and specifically in the area of keratoconus classification was conducted in 2021 by [23].…”