Logistic index for keratoconus detection and severity scoring (Logik)
- Author
- Ikram Issarti, Alejandra Consejo, Marta Jiménez-García, Elke O. Kreps (UGent) , Carina Koppen and Jos J. Rozema
- Organization
- Abstract
- Purpose: To develop an objective severity scoring system for keratoconus for the use in clinical practice. Methods: Corneal elevation and minimum thickness data of 812 subjects were retrospectively collected and divided into two groups: one control group with normal topography in both eyes (304 eyes), and one keratoconus group (508 eyes). Keratoconus cases ranged from suspect to moderate and had at least 1 examination in 1 of 2 recruiting centres. The elevation data were fitted to Zernike polynomial functions up to 8th order. An adapted machine learning algorithm was then applied to derive a platform-independent severity scoring and identification system for keratoconus. Results: The resulting logistic index for keratoconus (Logik) provided consistent and progressing scoring that reflected keratoconus severity. Moreover, the system provided an accurate classification of suspect keratoconus versus normal (sensitivity of 85.2%, specificity of 70.0%) when compared with Belin/Ambrosio Display Deviation (BAD_D) (sensitivity of 75.0%, specificity of 74.4%) and the Pentacam Topographical Keratoconus Classification (TKC) (sensitivity of 9.3%, specificity of 97.0%). Logik also showed better accuracy for grading keratoconus stages with an average accuracy of 99.9% versus (98.2%, 94.7%) with BAD_D and TKC respectively. Conclusion: Logik is a reliable index to identify suspect keratoconus and to score the severity of the disease. It shows an agreement with existing approaches while achieving better performance.
- Keywords
- Health Informatics, Computer Science Applications, Grading system, Cornea, Machine learning, Keratoconus, Refractive surgery, Progression, Severity, ORDER ABERRATIONS, CROSS-LINKING, CORNEAL, MACHINE, VIDEOKERATOGRAPHY, CLASSIFICATION, PROGRESSION, CURVATURE, DIAGNOSIS, ANTERIOR
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8677098
- MLA
- Issarti, Ikram, et al. “Logistic Index for Keratoconus Detection and Severity Scoring (Logik).” COMPUTERS IN BIOLOGY AND MEDICINE, vol. 122, 2020, doi:10.1016/j.compbiomed.2020.103809.
- APA
- Issarti, I., Consejo, A., Jiménez-García, M., Kreps, E. O., Koppen, C., & Rozema, J. J. (2020). Logistic index for keratoconus detection and severity scoring (Logik). COMPUTERS IN BIOLOGY AND MEDICINE, 122. https://doi.org/10.1016/j.compbiomed.2020.103809
- Chicago author-date
- Issarti, Ikram, Alejandra Consejo, Marta Jiménez-García, Elke O. Kreps, Carina Koppen, and Jos J. Rozema. 2020. “Logistic Index for Keratoconus Detection and Severity Scoring (Logik).” COMPUTERS IN BIOLOGY AND MEDICINE 122. https://doi.org/10.1016/j.compbiomed.2020.103809.
- Chicago author-date (all authors)
- Issarti, Ikram, Alejandra Consejo, Marta Jiménez-García, Elke O. Kreps, Carina Koppen, and Jos J. Rozema. 2020. “Logistic Index for Keratoconus Detection and Severity Scoring (Logik).” COMPUTERS IN BIOLOGY AND MEDICINE 122. doi:10.1016/j.compbiomed.2020.103809.
- Vancouver
- 1.Issarti I, Consejo A, Jiménez-García M, Kreps EO, Koppen C, Rozema JJ. Logistic index for keratoconus detection and severity scoring (Logik). COMPUTERS IN BIOLOGY AND MEDICINE. 2020;122.
- IEEE
- [1]I. Issarti, A. Consejo, M. Jiménez-García, E. O. Kreps, C. Koppen, and J. J. Rozema, “Logistic index for keratoconus detection and severity scoring (Logik),” COMPUTERS IN BIOLOGY AND MEDICINE, vol. 122, 2020.
@article{8677098,
abstract = {{Purpose: To develop an objective severity scoring system for keratoconus for the use in clinical practice.
Methods: Corneal elevation and minimum thickness data of 812 subjects were retrospectively collected and divided into two groups: one control group with normal topography in both eyes (304 eyes), and one keratoconus group (508 eyes). Keratoconus cases ranged from suspect to moderate and had at least 1 examination in 1 of 2 recruiting centres. The elevation data were fitted to Zernike polynomial functions up to 8th order. An adapted machine learning algorithm was then applied to derive a platform-independent severity scoring and identification system for keratoconus.
Results: The resulting logistic index for keratoconus (Logik) provided consistent and progressing scoring that reflected keratoconus severity. Moreover, the system provided an accurate classification of suspect keratoconus versus normal (sensitivity of 85.2%, specificity of 70.0%) when compared with Belin/Ambrosio Display Deviation (BAD_D) (sensitivity of 75.0%, specificity of 74.4%) and the Pentacam Topographical Keratoconus Classification (TKC) (sensitivity of 9.3%, specificity of 97.0%). Logik also showed better accuracy for grading keratoconus stages with an average accuracy of 99.9% versus (98.2%, 94.7%) with BAD_D and TKC respectively.
Conclusion: Logik is a reliable index to identify suspect keratoconus and to score the severity of the disease. It shows an agreement with existing approaches while achieving better performance.}},
articleno = {{103809}},
author = {{Issarti, Ikram and Consejo, Alejandra and Jiménez-García, Marta and Kreps, Elke O. and Koppen, Carina and Rozema, Jos J.}},
issn = {{0010-4825}},
journal = {{COMPUTERS IN BIOLOGY AND MEDICINE}},
keywords = {{Health Informatics,Computer Science Applications,Grading system,Cornea,Machine learning,Keratoconus,Refractive surgery,Progression,Severity,ORDER ABERRATIONS,CROSS-LINKING,CORNEAL,MACHINE,VIDEOKERATOGRAPHY,CLASSIFICATION,PROGRESSION,CURVATURE,DIAGNOSIS,ANTERIOR}},
language = {{eng}},
pages = {{10}},
title = {{Logistic index for keratoconus detection and severity scoring (Logik)}},
url = {{http://doi.org/10.1016/j.compbiomed.2020.103809}},
volume = {{122}},
year = {{2020}},
}
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