Automated Keratoconus Diagnosis Using Corneal Topography and Tomography: A Literature Review

Author(s)	Ms. Shalini Rajendra Bakal, Satish R. Sankaye
Country	India
Abstract	Keratoconus is a progressive eye condition in which the cornea becomes thin and irregular, leading to distorted vision. Detecting the disease at an early stage is reaally important to prevent further visual deterioration, but traditional clinical diagnosis often depends on subjective interpretation of corneal maps. With the availability of corneal topography and tomography, it has become possible to assess corneal structure in a more objective and quantitative manner. This literature review explores the existing research on automated keratoconus diagnosis using topographic and tomographic corneal map data. there are Various approaches which are based on clinical indices, machine learning, as well as k deep learning, and hybrid models are also discussed and compared. The review also highlights the practical challenges such as there is limited datasets available , variability across imaging devices, and the need for interpretable models. Recent trends, including multimodal analysis and explainable artificial intelligence, are briefly discussed.
Keywords	Keratoconus, Corneal Topography, Corneal Tomography, Machine Learning, Deep Learning
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 16, Issue 4, October-December 2025
Published On	2025-12-31
DOI	https://doi.org/10.71097/IJSAT.v16.i4.10035
Short DOI	https://doi.org/hbhj5v

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