Artificial intelligence in corneal topography
Abstract
The purpose of this paper is to explore the effectiveness and efficiency of various artificial intelligence (AI) techniques in extracting features from corneal topographies. A considerable number of dated and contemporary related research papers have been reviewed. The author has only checked the studies that considered developing at least one AI-based algorithm for data classification of topographic patterns. The results of this review emphasize the effectiveness and efficiency of machine learning algorithms in the clinical diagnosis of various eye refractive problems.
Keywords
References
- Arbelaez, M. C., Versaci, F., Vestri, G., Barboni, P., & Savini, G. (2012). Use of a support vector machine for keratoconus and subclinical keratoconus detection by topographic and tomographic data. Ophthalmology, 119(11), 2231–2238. https://doi.org/10.1016/j.ophtha.2012.06.005
- Bagherinia, H., Chen, X., Flachenecker, C., Angeles, R., Burger, D., Caroline, P., … Reeder, K. (2008). Support Vector Machine (SVM)-Based Classification of Corneal Topography. Investigative Ophthalmology & Visual Science, 49(13), 1023. Retrieved from http://dx.doi.org/
- Camarillo, T., Choi, K., Hamilton, G., Miles, M., Muller, K., Williams, K., … Schrepel, P. (2002). Athletes as an Ideal Target Population for Orthokeratology Keratoconus : Improving Quality of Life Through Advancements in Detection and Treatment.
- Carvalho, L. A. (2005). Preliminary Results of Neural Networks and Zernike Polynomials for Classification of Videokeratography Maps: Optometry and Vision Science, 82(2), 151–158. https://doi.org/10.1097/01.OPX.0000153193.41554.A1
- de Carvalho, L. A., & Barbosa, M. S. (2008). Neural networks and statistical analysis for classification of corneal videokeratography maps based on Zernike coefficients: a quantitative comparison. Arquivos Brasileiros de Oftalmologia, 71(3), 337–341. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/18641817
- Kabari, L., & Nwachukwu, E. (2012). Neural Networks and Decision Trees For Eye Diseases Diagnosis. In P. Vizureanu (Ed.), Advances in Expert Systems. InTech.
- Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., … Zhang, K. (2018). Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell, 172(5), 1122–1131.e9. https://doi.org/10.1016/j.cell.2018.02.010
- Kotsia, I., & Pitas, I. (2007). Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machines. IEEE Transactions on Image Processing, 16(1), 172–187. https://doi.org/10.1109/TIP.2006.884954
Details
Primary Language
English
Subjects
Computer Software
Journal Section
Review
Publication Date
January 1, 2019
Submission Date
August 31, 2018
Acceptance Date
November 19, 2018
Published in Issue
Year 2019 Volume: 2 Number: 1