Table 5 The applications of OCT in research and clinical for the assessment of tooth demineralization

Schneider, H., et al. [98]

Identified caries lesion that is completely hidden and cannot be visually assessed from the tooth surface. It appears as subsurface regions of two to three times increased signal intensity images compared to adjacent healthy region due to the increased porosity from mineral loss

Chan, K.H., et al.[100]

Quantified the optical lesion depth in the cross-polarization OCT image based on the refractive index of the tooth (refractive index of the tooth reduces due to the mineral loss)

Cara, A.C., et al. [101]

Quantified the caries lesion depth in human dental enamels from the integrated reflectivity of the demineralized tooth

Amaechi, B., et al. [102]

Maia A.M. et al. [111]

Quantified the depth of demineralized tooth with the reflectivity loss and optical attenuation coefficient

Mandurah, M., et al. [74]

Monitor changes in enamel lesions during remineralization using attenuation coefficient that is extracted by performing curve fitting on average A-scans using the Beer-Lambert equation and least-square method on the OCT images

Habib, M., et al. [103]

Generated en face attenuation coefficient maps to examine regional variation of mineral loss with different duration of erosion

Golde, J., et al. [105]

Recognized demineralized tooth as regions with high depolarization in DOPU image

Salehi et al. [106–108]

Huang et al. [110]

Utilized deep convolutional neural networks for caries detection (i.e. full image classification/grading of caries)