Keratoconus Severity Detection From Elevation, Topography and Pachymetry Raw Data Using a Machine Learning Approach Article Swipe

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Alexandru Lavric , Liliana Anchidin , Valentin Popa , Ali H. Al‐Timemy , Zaid Abdi Alkareem Alyasseri , Hidenori Takahashi , Siamak Yousefi , Rossen Mihaylov Hazarbassanov ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.1109/access.2021.3086021

Keratoconus (KCN) is an eye condition that affects the cornea. The main objective of this study is to evaluate the accuracy of keratoconus detection from corneal parameters including elevation, topography and pachymetry using machine learning algorithms. We developed several machine learning models to detect keratoconus from corneal elevation, topography and pachymetry parameters that were obtained from 5881 eyes of 2800 patients in Brazil using a Pentacam Scheimpflug instrument. Elevation parameters provided the highest area under the curve (AUC) parameter of 0.99 in detecting normal from keratoconus cases and an AUC of 0.88 in detecting different severity levels when using only three most promising corneal parameters including minimum curvature radius, eccentricity of the cornea and asphericity of the cornea. The developed algorithm can distinguish early KCN eyes from healthy eyes with a high accuracy obtaining an AUC of 0.97. From a clinical point of view the detection of early KCN is very important because KCN patients are usually misdiagnosed due to early symptoms. Results suggest that elevation parameters may retain more useful information for detecting keratoconus than historically believed.

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Artificial Intelligence

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Keratoconus Scheimpflug principle Cornea Elevation (ballistics) Ophthalmology Corneal topography Corneal pachymetry Radius of curvature Curvature Computer science Artificial intelligence Optometry Medicine Mathematics Mean curvature Geometry Mean curvature flow

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Type: article
Language: en
Landing Page: https://doi.org/10.1109/access.2021.3086021
PDF: https://ieeexplore.ieee.org/ielx7/6287639/9312710/09446142.pdf
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Cited By: 29
References: 56
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DOI: https://doi.org/10.1109/access.2021.3086021

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Title: Keratoconus Severity Detection From Elevation, Topography and Pachymetry Raw Data Using a Machine Learning Approach

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Publication year: 2021

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Publication date: 2021-01-01

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Authors: Alexandru Lavric, Liliana Anchidin, Valentin Popa, Ali H. Al‐Timemy, Zaid Abdi Alkareem Alyasseri, Hidenori Takahashi, Siamak Yousefi, Rossen Mihaylov Hazarbassanov

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Landing page: https://doi.org/10.1109/access.2021.3086021

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Concepts: Keratoconus, Scheimpflug principle, Cornea, Elevation (ballistics), Ophthalmology, Corneal topography, Corneal pachymetry, Radius of curvature, Curvature, Computer science, Artificial intelligence, Optometry, Medicine, Mathematics, Mean curvature, Geometry, Mean curvature flow

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Cited by: 29

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