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Smartphone-Assisted Placido Ring Imaging for K1 Stratification in Keratoconus: A Deep Learning Study.

Enes Eroglu¹, Nicholas Tomaras², Kabir Anand Pathak¹

¹Department of Ophthalmology, University of North Carolina at Chapel Hill, 2226 Nelson Highway, Chapel Hill, NC 27517, USA.

Diagnostics (Basel, Switzerland)

|July 15, 2026

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A new deep-learning algorithm uses smartphone images to categorize keratoconus severity. This low-cost method shows high accuracy in predicting keratometric categories, potentially improving accessibility for diagnosing this eye disease.

Area of Science:

Ophthalmology and computational vision
Artificial intelligence in medical diagnostics

Background:

Keratoconus (KC) is a progressive corneal disease impacting vision.
Standard diagnostic tools like corneal topography are not always accessible.
Need for low-cost, accessible KC diagnostic methods.

Purpose of the Study:

To evaluate a novel, low-cost deep-learning algorithm for inferring keratometric categories from smartphone-assisted Placido ring photographs.
To assess the feasibility of using smartphone imaging for KC stratification.

Main Methods:

A Variational Autoencoder (AutoEncoderKL) was trained on healthy and KC eye images.
An ensemble classifier utilized encoder features to predict K1 keratometric categories (<40 D, 40-47 D, >47 D).
Performance evaluated using accuracy, precision, recall, and F1-score on a held-out set.

Keywords:

K1 deep learning keratoconus keratometry smartphone imaging

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Main Results:

The model achieved 91% overall accuracy across all keratometric categories.
High precision and recall were observed, with perfect recall for the >47 D category.
The algorithm successfully predicted K1 keratometric categories without direct tomographic or keratometric inputs.

Conclusions:

Smartphone-assisted Placido ring imaging combined with deep learning offers a promising low-cost approach for KC stratification.
This method provides a proof-of-concept for accessible KC screening.
Further validation in diverse clinical settings is necessary to assess full clinical utility.