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Cited by 108Corneal surgery and disordersArtificial Intelligence in HealthcareOphthalmology and Visual Impairment Studies

Automated Decision Tree Classification of Corneal Shape

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Twa, Michael D., Parthasarathy, Srinivasan, Roberts, Cynthia, Mahmoud, Ashraf M., Raasch, Thomas W., Bullimore, Mark A.·2005·Optometry and Vision Science
DOI 10.1097/01.opx.0000192350.01045.6fPMC PMC3073139
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Abstract

Purpose.: The volume and complexity of data produced during videokeratography examinations present a challenge of interpretation. As a consequence, results are often analyzed qualitatively by subjective pattern recognition or reduced to comparisons of summary indices. We describe the application of decision tree induction, an automated machine learning classification method, to discriminate between normal and keratoconic corneal shapes in an objective and quantitative way. We then compared this method with other known classification methods. Methods.: The corneal surface was modeled with a seventh‐order Zernike polynomial for 132 normal eyes of 92 subjects and 112 eyes of 71 subjects diagnosed with keratoconus. A decision tree classifier was induced using the C4.5 algorithm, and its classification performance was compared with the modified Rabinowitz–McDonnell index, Schwiegerling's Z3 index (Z3), Keratoconus Prediction Index (KPI), KISA%, and Cone Location and Magnitude Index using recommended classification thresholds for each method. We also evaluated the area under the receiver operator characteristic (ROC) curve for each classification method. Results.: Our decision tree classifier performed equal to or better than the other classifiers tested: accuracy was 92% and the area under the ROC curve was 0.97. Our decision tree classifier reduced the information needed to distinguish between normal and keratoconus eyes using four of 36 Zernike polynomial coefficients. The four surface features selected as classification attributes by the decision tree method were inferior elevation, greater sagittal depth, oblique toricity, and trefoil. Conclusion.: Automated decision tree classification of corneal shape through Zernike polynomials is an accurate quantitative method of classification that is interpretable and can be generated from any instrument platform capable of raw elevation data output. This method of pattern classification is extendable to other classification problems.

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Research works printed but not linked: 4

  1. [22]Twa MD, Parthasarathy S, Raasch TW, Bullimore MA. Decision tree classification of spatial data patterns from videokeratography using Zernike polynomials. In: Barbara D, Kamath C, editors. SIAM International Conference on Data Mining. San Francisco, CA: Society for Industrial and Applied Mathematics; 2003. pp. 3–12. 2003. [Google Scholar]
  2. [26]Mahmoud AM, Roberts C, Herderick EE, Lembach RG, Markakis G. The Cone Location and Magnitude Index (CLMI) [ARVO Abstract] Invest Ophthalmol Vis Sci. 2001;42:S898. Abstract nr 4825. [Google Scholar]
  3. [28]Rabinowitz YS. Videokeratographic indices to aid in screening for keratoconus. J Refract Surg. 1995;11:371–379. doi: 10.3928/1081-597X-19950901-14. [DOI] [PubMed] [Google Scholar] · DOI
  4. [32]Mitchell TM. Machine learning. New York: McGraw-Hill; 1997. [Google Scholar]

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Authors
Twa, M. D., Parthasarathy, S., Roberts, C., Mahmoud, A. M., Raasch, T. W., & Bullimore, M. A.
Affiliations
Association for Research in Vision and Ophthalmology (US)
ORCID
Venue
Optometry and Vision Science
Publisher
Wiley
Published
2005-12
Volume
82
Issue
12
Pages
1038-1046
ISSN
  • 1040-5488 (ISSN-L)
  • 1538-9235
DOI
10.1097/01.opx.0000192350.01045.6f
PMC
PMC3073139
OpenAlex
W1978559313
Cited by
108
References
38
Citations by year
2026: 1 · 2025: 5 · 2024: 7 · 2023: 12 · 2022: 8 · 2021: 7 · 2020: 13 · 2019: 4 · 2018: 4 · 2017: 5 · 2016: 3 · 2015: 2 · 2014: 3 · 2013: 4 · 2012: 6
FWCI
3.82
Type
journal article
Language
en
Open access
Licence
Locations
  • Optometry and Vision Science (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Institutes of Health (EY16225, EY13359 (MDT))
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Corneal surgery and disorders
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Epidemiology › Ophthalmology and Visual Impairment Studies
Keywords
keratoconus, corneal shape, decision tree classification, Zernike polynomials, corneal topography, C4.5 algorithm, automatic classification, trefoil
MeSH terms
Cornea, Decision Trees, Female, Humans, Keratoconus, Male, Retrospective Studies, Reproducibility of Results, Corneal Topography

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