Differentiation of Occlusal Discolorations and Carious Lesions with Hyperspectral Imaging In Vitro

Stains and stained incipient lesions can be challenging to differentiate with established clinical tools. New diagnostic techniques are required for improved distinction to enable early noninvasive treatment. This in vitro study evaluates the performance of artificial intelligence (AI)-based classification of hyperspectral imaging data for early occlusal lesion detection and differentiation from stains. Sixty-five extracted permanent human maxillary and mandibular bicuspids and molars (International Caries Detection and Assessment System [ICDAS] II 0–4) were imaged with a hyperspectral camera (Diaspective Vision TIVITA® Tissue, Diaspective Vision, Pepelow, Germany) at a distance of 350 mm, acquiring spatial and spectral information in the wavelength range 505–1000 nm; 650 fissural spectra were used to train classification algorithms (models) for automated distinction between stained but sound enamel and stained lesions. Stratified 10-fold cross-validation was used. The model with the highest classification performance, a fine k-nearest neighbor classification algorithm, was used to classify five additional tooth fissural areas. Polarization microscopy of ground sections served as reference. Compared to stained lesions, stained intact enamel showed higher reflectance in the wavelength range 525–710 nm but lower reflectance in the wavelength range 710–1000 nm. A fine k-nearest neighbor classification algorithm achieved the highest performance with a Matthews correlation coefficient (MCC) of 0.75, a sensitivity of 0.95 and a specificity of 0.80 when distinguishing between intact stained and stained lesion spectra. The superposition of color-coded classification results on further tooth occlusal projections enabled qualitative assessment of the entire fissure’s enamel health. AI-based evaluation of hyperspectral images is highly promising as a complementary method to visual and radiographic examination for early occlusal lesion detection

Differentiation of Occlusal Discolorations and Carious Lesions with Hyperspectral Imaging In Vitro

Stains and stained incipient lesions can be challenging to differentiate with established clinical tools. New diagnostic techniques are required for improved distinction to enable early noninvasive treatment. This in vitro study evaluates the performance of artificial intelligence (AI)-based classification of hyperspectral imaging data for early occlusal lesion detection and differentiation from stains. Sixty-five extracted permanent human maxillary and mandibular bicuspids and molars (International Caries Detection and Assessment System [ICDAS] II 0–4) were imaged with a hyperspectral camera (Diaspective Vision TIVITA® Tissue, Diaspective Vision, Pepelow, Germany) at a distance of 350 mm, acquiring spatial and spectral information in the wavelength range 505–1000 nm; 650 fissural spectra were used to train classification algorithms (models) for automated distinction between stained but sound enamel and stained lesions. Stratified 10-fold cross-validation was used. The model with the highest classification performance, a fine k-nearest neighbor classification algorithm, was used to classify five additional tooth fissural areas. Polarization microscopy of ground sections served as reference. Compared to stained lesions, stained intact enamel showed higher reflectance in the wavelength range 525–710 nm but lower reflectance in the wavelength range 710–1000 nm. A fine k-nearest neighbor classification algorithm achieved the highest performance with a Matthews correlation coefficient (MCC) of 0.75, a sensitivity of 0.95 and a specificity of 0.80 when distinguishing between intact stained and stained lesion spectra. The superposition of color-coded classification results on further tooth occlusal projections enabled qualitative assessment of the entire fissure’s enamel health. AI-based evaluation of hyperspectral images is highly promising as a complementary method to visual and radiographic examination for early occlusal lesion detection

Differentiation of Occlusal Discolorations and Carious Lesions with Hyperspectral Imaging In Vitro

Stains and stained incipient lesions can be challenging to differentiate with established clinical tools. New diagnostic techniques are required for improved distinction to enable early noninvasive treatment. This in vitro study evaluates the performance of artificial intelligence (AI)-based classification of hyperspectral imaging data for early occlusal lesion detection and differentiation from stains. Sixty-five extracted permanent human maxillary and mandibular bicuspids and molars (International Caries Detection and Assessment System [ICDAS] II 0–4) were imaged with a hyperspectral camera (Diaspective Vision TIVITA® Tissue, Diaspective Vision, Pepelow, Germany) at a distance of 350 mm, acquiring spatial and spectral information in the wavelength range 505–1000 nm; 650 fissural spectra were used to train classification algorithms (models) for automated distinction between stained but sound enamel and stained lesions. Stratified 10-fold cross-validation was used. The model with the highest classification performance, a fine k-nearest neighbor classification algorithm, was used to classify five additional tooth fissural areas. Polarization microscopy of ground sections served as reference. Compared to stained lesions, stained intact enamel showed higher reflectance in the wavelength range 525–710 nm but lower reflectance in the wavelength range 710–1000 nm. A fine k-nearest neighbor classification algorithm achieved the highest performance with a Matthews correlation coefficient (MCC) of 0.75, a sensitivity of 0.95 and a specificity of 0.80 when distinguishing between intact stained and stained lesion spectra. The superposition of color-coded classification results on further tooth occlusal projections enabled qualitative assessment of the entire fissure’s enamel health. AI-based evaluation of hyperspectral images is highly promising as a complementary method to visual and radiographic examination for early occlusal lesion detection

Towards quantitative demineralization imaging for the assessment of carious lesions based on PS-OCT

Assessing the stage and progression of enamel demineralization non-invasively is of high interest in conservative dentistry. By examining tooth samples with suspected occlusal lesions, we show the potential of depolarization imaging based on polarization-sensitive optical coherence tomography for the assessment of carious lesions and validate the results by co-registered X-ray micro-computed tomography volumes

Assessment of occlusal enamel alterations utilizing depolarization imaging based on PS-OCT

While dental caries represents the major chronic disease of humans, visual and tactile inspection are the methods of choice in daily dental practice. Several optical technologies have been developed in recent years for the purpose of dental examination, including near-infrared light transillumination as a promising tool for the supplementation and partial replacement of radiography. In case of occlusal alterations, the incidence of surface discolorations impedes the visual assessment, whereas tactile inspection appears to yield little diagnostic information or might be detrimental. Optical coherence tomography (OCT) facilitates depth-resolved imaging with μm resolution, utilizing near-infrared light, and has already shown its potential for various dental applications. We have recently demonstrated that depolarization imaging utilizing the degree of polarization uniformity (DOPU) extends those abilities by the detection of early carious lesions, as it provides an unambiguous demineralization contrast. Here, we show that this approach also enables the assessment of occlusal enamel lesions in the presence of stains, and compare PS-OCT cross sections with polarized light microscopy (PLM) images of thin sections. For tooth samples with discoloration or demineralization, respectively, PS-OCT and PLM results are in good agreement

Towards quantitative demineralization imaging for the assessment of carious lesions based on PS-OCT

Assessing the stage and progression of enamel demineralization non-invasively is of high interest in conservative dentistry. By examining tooth samples with suspected occlusal lesions, we show the potential of depolarization imaging based on polarization-sensitive optical coherence tomography for the assessment of carious lesions and validate the results by co-registered X-ray micro-computed tomography volumes