AI at the forefront of the eye: Triaging tool for confocal microscopy images of human cornea

Vlada Rozova, Kh Tohidul Islam, Laura E Downie, Holly Chinnery, Karin Verspoor

Keywords: image quality assessment, medical imaging, convolutional neural networks, Fourier transform

TL;DR: A framework for automated quality assessment and selection of distinct human corneal confocal microscopy images suitable for downstream analysis.

Abstract: Corneal confocal microscopy is used in both ophthalmology and neurology to identify and monitor the immunological and neural effects of ocular and systemic diseases. However, its use in research and clinical settings is limited by the lack of reliable, time-efficient methods to process acquired data. A typical imaging session yields a stack of images varying in quality and field of view that require careful filtering prior to further analysis. Here, we present a framework for automated quality assessment and selection of distinct human corneal confocal microscopy images suitable for downstream analysis.

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Paper Type: novel methodological ideas without extensive validation

Primary Subject Area: Application: Ophthalmology

Secondary Subject Area: Image Acquisition and Reconstruction

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