A comparison of methods using optical coherence tomography to detect demineralized regions in teeth

J Biophotonics. 2011 Nov;4(11-12):814-23. doi: 10.1002/jbio.201100014. Epub 2011 Jul 25.

Abstract

Optical coherence tomography (OCT) is a three- dimensional optical imaging technique that can be used to identify areas of early caries formation in dental enamel. The OCT signal at 850 nm back-reflected from sound enamel is attenuated stronger than the signal back-reflected from demineralized regions. To quantify this observation, the OCT signal as a function of depth into the enamel (also known as the A-scan intensity), the histogram of the A-scan intensities and three summary parameters derived from the A-scan are defined and their diagnostic potential compared. A total of 754 OCT A-scans were analyzed. The three summary parameters derived from the A-scans, the OCT attenuation coefficient as well as the mean and standard deviation of the lognormal fit to the histogram of the A-scan ensemble show statistically significant differences (p < 0.01) when comparing parameters from sound enamel and caries. Furthermore, these parameters only show a modest correlation. Based on the area under the curve (AUC) of the receiver operating characteristics (ROC) plot, the OCT attenuation coefficient shows higher discriminatory capacity (AUC = 0.98) compared to the parameters derived from the lognormal fit to the histogram of the A-scan. However, direct analysis of the A-scans or the histogram of A-scan intensities using linear support vector machine classification shows diagnostic discrimination (AUC = 0.96) comparable to that achieved using the attenuation coefficient. These findings suggest that either direct analysis of the A-scan, its intensity histogram or the attenuation coefficient derived from the descending slope of the OCT A-scan have high capacity to discriminate between regions of caries and sound enamel.

Publication types

  • Comparative Study
  • Research Support, N.I.H., Extramural

MeSH terms

  • Area Under Curve
  • Bicuspid / pathology
  • Computer Simulation
  • Dental Caries / diagnosis
  • Dental Caries / pathology
  • Dental Enamel / pathology
  • Humans
  • Image Interpretation, Computer-Assisted / methods
  • Models, Statistical
  • Molar / pathology
  • Monte Carlo Method
  • ROC Curve
  • Sensitivity and Specificity
  • Statistical Distributions
  • Statistics, Nonparametric
  • Support Vector Machine
  • Tomography, Optical Coherence / methods*
  • Tooth Demineralization / diagnosis*
  • Tooth Demineralization / pathology