  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 94.1% and a
specificity of 100.0%. Moreover, the quantitative curvature estimates (sim-K)
strongly correlate with a gold-standard medical device (Pearson correlation
coefficient =0.78). Our results indicate that SmartKC has the potential to be
used as a keratoconus screening tool under real-world medical settings.
