Keratoconus is a severe eye disease affecting the cornea (the clear, dome-shaped outer surface of the eye), causing it to become thin and develop a conical bulge. The diagnosis of keratoconus requires sophisticated ophthalmic devices which are non-portable and very expensive. This makes early detection of keratoconus inaccessible to large populations in low-and middle-income countries, making it a leading cause for partial/complete blindness among such populations. We propose SmartKC, a low-cost, smartphone-based keratoconus diagnosis system comprising of a 3D-printed placido's disc attachment, an LED light strip, and an intelligent smartphone app to capture the reflection of the placido rings on the cornea. An image processing pipeline analyzes the corneal image and uses the smartphone's camera parameters, the placido rings' 3D location, the pixel location of the reflected placido rings and the setup's working distance to construct the corneal surface, via the Arc-Step method and Zernike polynomials based surface fitting. In a clinical study with 101 distinct eyes, we found that SmartKC achieves a sensitivity of 87.8% and a specificity of 80.4%. Moreover, the quantitative curvature estimates (sim-K) strongly correlate with a gold-standard medical device (Pearson correlation coefficient = 0.77). Our results indicate that SmartKC has the potential to be used as a keratoconus screening tool under real-world medical settings.

No abstract

Keratoconus is a severe eye disease affecting the cornea (the clear, dome-shaped outer surface of the eye), causing it to become thin and develop a conical bulge. The diagnosis of keratoconus requires sophisticated ophthalmic devices which are non-portable and very expensive. This makes early detection of keratoconus inaccessible to large populations in lowand middle-income countries, making it a leading cause for partial/complete blindness among such populations. We propose SmartKC, a low-cost, smartphone-based keratoconus diagnosis system comprising of a 3D-printed placido's disc attachment, an LED light strip, and an intelligent smartphone app to capture the reflection of the placido rings on the cornea. An image processing pipeline analyzes the corneal image and uses the smartphone's camera parameters, the placido rings' 3D location, the pixel location of the reflected placido rings and the setup's working distance to construct the corneal surface, via the Arc-Step method and Zernike polynomials based surface fitting. In a clinical study with 101 distinct eyes, we found that SmartKC achieves a sensitivity of 87.8% and a specificity of 80.4%. Moreover, the quantitative curvature estimates (sim-K) strongly correlate with a gold-standard medical device (Pearson correlation coefficient = 0.77). Our results indicate that SmartKC has the potential to be used as a keratoconus screening tool under real-world medical settings.CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing; • Applied computing → Consumer health.