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Keratoconus Classifier for Smartphone-based Corneal Topographer.

Siddhartha Gairola, Pallavi Joshi, Anand Balasubramaniam

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
    PubMed
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    A new AI model effectively diagnoses keratoconus (an eye disease) using smartphone-based corneal scans. This technology enhances accessibility for early detection, especially in underserved regions.

    Area of Science:

    • Ophthalmology
    • Artificial Intelligence
    • Medical Imaging

    Background:

    • Keratoconus, a severe corneal deformation, affects young individuals (10-25 years) and is a leading cause of blindness in this age group.
    • Current diagnosis relies on expensive corneal topography, limiting access, particularly in the Global South.
    • Smartphone-based corneal topographers like SmartKC offer a low-cost alternative for keratoconus diagnosis.

    Purpose of the Study:

    • To develop an automated diagnostic scheme for keratoconus using heatmaps from the SmartKC device.
    • To enable efficient screening in areas with limited access to medical specialists.

    Main Methods:

    • A dual-head convolutional neural network (CNN) was designed for classifying keratoconus from SmartKC heatmaps.
    • A two-stage transfer learning strategy was employed, utilizing data from medical-grade topographers and SmartKC.

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  • Domain-specific data augmentations were applied to enhance model training with limited SmartKC data.

    • Main Results:

    • The CNN model achieved high diagnostic accuracy.
    • Sensitivity of 91.3% and specificity of 94.2% were obtained for keratoconus classification.
    • The transfer learning approach effectively addressed the challenge of a small dataset from the novel SmartKC device.

    Conclusions:

    • The proposed dual-head CNN with transfer learning provides a viable automated method for keratoconus screening using smartphone-based corneal topography.
    • This approach significantly improves the accessibility and efficiency of keratoconus diagnosis, especially in resource-limited settings.
    • The study demonstrates the potential of AI and low-cost devices in managing eye diseases globally.