Next Article in Journal
A Statistical Approach to Violin Evaluation
Previous Article in Journal
Model for Wall Shear Stress from Obliquely Impinging Planar Underexpanded Jets
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 12
Issue 14
10.3390/app12147312
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

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

by
Robin Vosahlo
1,2,3,*,
Jonas Golde
3,
Julia Walther
4,
Edmund Koch
3,
Christian Hannig
2 and
Florian Tetschke
2,3,†
1
Department of Orthodontics, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
2
Clinic of Operative Dentistry, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
3
Clinical Sensoring and Monitoring, Anesthesiology and Intensive Care Medicine, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
4
Department of Medical Physics and Biomedical Engineering, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
*
Author to whom correspondence should be addressed.
†
Current address: Sonovum GmbH, Deutscher Platz 4, 04103 Leipzig, Germany.
Appl. Sci. 2022, 12(14), 7312; https://doi.org/10.3390/app12147312
Submission received: 29 May 2022 / Revised: 14 July 2022 / Accepted: 17 July 2022 / Published: 21 July 2022
(This article belongs to the Section Applied Dentistry and Oral Sciences)
Browse Figures
Versions Notes

Abstract

:

Featured Application

The digital analysis of hyperspectral images by means of artificial intelligence can contribute to the development of new diagnostic techniques for early caries detection.

Abstract

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.

1. Introduction

Stains in occlusal pits and fissures can completely mask early enamel demineralization and hence confound visual detection of incipient, remineralizable caries states. The clinical standard of visual and radiographic examination for caries detection shows sensitivities of only 49.1% and 36.0%, respectively, for occlusal lesions in Germany [1]. Due to the poor performance of these techniques, some dentists may resort to tactile examination or probing, which can cause traumatic enamel defects in lesions [2,3]. Occlusal caries refers to carious lesions originating from a tooth’s occlusal surface. Due to the jagged cusp-and-fissure relief of occlusal surfaces, dental biofilms (plaque) can more easily persevere despite mechanical plaque removal (tooth brushing). In particular, fissures are often so narrow that a toothbrush’s bristles cannot penetrate to and hence clean the fissure bottom, allowing plaque to remain and mature. Bacteria in dental plaque secrete acids, demineralizing the tooth’s enamel and causing dental caries. When this demineralization is confined to enamel, the lesion can be treated non-invasively by remineralization techniques. However, if gone unnoticed, the lesion progresses, penetrating into dentin at the fissure bottom (Figure 1). Then, restorative therapy is required. Hidden caries are a form of occlusal caries where the lesion originates in the fissure bottom and spreads laterally, undermining a superficial layer of sound enamel. Therefore, the lesion is unlikely to be detected by visual inspection, even by experienced clinicians. The inadequacy of conventional techniques underlines the need for novel techniques with improved incipient lesion detection and caries-monitoring performance [4,5,6].
Demineralization causes changes to the enamel spectrum in the visible (VIS) and near-infrared (NIR) region due to an increase in pore volume [7]. On this basis, spectral imaging has been used previously in vitro for lesion detection, primarily focusing on NIR wavelengths beyond 1000 nm [8,9,10,11,12]. NIR wavelengths below 1000 nm have the advantage of being detectable with moderately priced silicon sensors at higher resolution, as opposed to the more costly and lower-resolved indium gallium arsenide (InGaAs) sensors required for higher wavelengths [13].
Hyperspectral imaging (HSI) was originally developed by NASA as a remote sensing technique [14]. It allows for the acquisition of two-dimensional spatial (morphological) images, while providing chemical information as an additional third, spectral dimension (Scheme 1).
As light enters a material, it is repeatedly scattered, absorbed and reflected, and conclusions on a material’s composition can be drawn from its light-scattering and reflectance properties [15,16]. HSI detects the reflected light and has thus evolved to become a diagnostic technique in numerous medical fields, including cancer detection [17,18,19,20,21], heart disease [22] and retinal pathologies [23,24]. In dental caries detection, HSI relies on changes in enamel’s light reflectance due to biochemical and morphological alterations induced by a carious lesion [7]. HSI shows promising potential for early lesion detection in a clinical setting, with short image acquisition times and noncontact, nonionizing imaging [11,12]. Unlike previously applied spectral imaging approaches that focus on specific wavelengths, here, hyperspectral imaging considers a wide range of wavelengths and combines spatial (morphological) and spectral (chemical) information [13]. Different data processing techniques have been applied to hyperspectral data in dentistry, including the examination of characteristic wavelength intensities [12] and automated quadratic discriminant analysis [11].
Artificial intelligence (AI) is increasingly gaining focus in medical diagnostics. Many correlations not apparent to the human eye can be detected by methods of artificial intelligence. Therefore, AI can provide information complementary to traditional diagnostic techniques to improve diagnostic performance. For example, AI can provide accurate and thorough assessment of dental radiographs [25].
The present in vitro study on extracted teeth revisits the wavelength region 525–1000 nm and considers the entirety of recorded wavelengths in an attempt to characterize the spectral differences between stained but sound enamel and stained demineralized (lesion) enamel. Moreover, classification algorithms (such as support-vector machines, nearest neighbor classifiers and decision trees), all a type of supervised machine learning approach of artificial intelligence, are applied for the first time to automatically parse innocuous stains from incipient lesions. Classification performance is validated by cross-validation. To simulate the classifier’s application as a decision-support system in clinical dentistry, additional extracted teeth’s occlusal surfaces are classified, and enamel health predictions are presented in an occlusal projection of the tooth.

2. Materials and Methods

The procedure for HSI-based automated classification of stained intact and stained demineralized occlusal areas is depicted in Scheme 2.
Tooth acquisition and selection: Sixty-five human permanent bicuspids and molars, of which thirty-five were maxillary and thirty-one were mandibular, were investigated in this study. All teeth were recently extracted in clinical routine for medically justified reasons not related to this study in private practices across the state of Saxony, Germany, and therefore, this study is exempt from ethical approval [26]. No personal or general health information was collected from the donors. The selection of the investigated tooth samples was based on the visual appearance of their occlusal surfaces, which was assessed by three experienced dentists independently, such that no communication occurred between the three raters. None of the raters are authors of this study. All raters were calibrated on ICDAS scoring using the ICDAS II Training Packet prior to tooth assessment [27]. In the case of different scores allocated to a tooth, the most frequent score was taken. If all three raters assigned different scores, the tooth was excluded from the study. Moreover, using his or her clinical experience, each rater independently encircled the most questionable area of each tooth’s fissure on an accompanying photograph of each tooth. Teeth with stained or discolored occlusal fissures were included in this study. Teeth with surface cavitation with dentin exposure (≥ICDAS 5) or with fissure sealants or occlusal restorations were excluded from this study. In order to prevent dehydration, the teeth were stored in a distilled water and thymol solution. For each tooth, a central region of its fissural system was defined for validation of its occlusal health state. Validation was performed by histological cross-section analysis with polarized light microscopy (PLM).
Hyperspectral imaging setup: Hyperspectral images of each tooth’s entire occlusal surface were acquired using a commercially available hyperspectral imaging line-scan camera (Diaspective Vision TIVITA® Tissue, Diaspective Vision, Pepelow, Germany) to measure oxygenation, perfusion, heme and water content of skin and mucous membranes. The camera provides spatially resolved images (640 × 480 pixels) within the wavelength range of 505–1000 nm (100 spectral bands, Δλ = 5 nm). The reflected, broadband light is collected by a 75 mm, coated NIR lens (Azure Photonics, Inc., San Ramon, CA, USA) at an aperture of f/2.8 and passes through a moveable internal slit to an optical grating where the light is dispersed into its wavelengths. The resulting intensities are then projected onto the camera sensor, where one axis represents the coordinates of a single line and the other axis represents the intensities of the wavelengths (x-λ orientation). The second spatial dimension is generated by internally moving the slit along the object. For a thorough explanation of the line-scan hyperspectral acquisition principle, we highly recommend the review of Lu and Fei on medical hyperspectral imaging [13]. The teeth were measured under moist conditions; larger water residues in the fissural system were carefully removed with a paper point prior to imaging. Occlusal surfaces were located 350 mm below the lens and aligned parallel to the tabletop. Two external 40 W halogen bulbs with an aluminum-coated reflector (OSRAM Halopar GU10, OSRAM GmbH, Munich, Germany) illuminated the samples in the analyzed wavelength range. In order to ensure a reproducible and stable spectrum output of the illumination unit, a warm-up time of 60 s prior to measurements was applied and the recommended voltage (230 V) was supplied by a stabilized power supply. The camera calibration and data normalization procedure was described in more detail previously [28]. Briefly, the reflected intensity of the tooth is normalized to a previously measured dark image with closed shutter as well as to a white image with a reflectance standard (>98% reflectance, Optopolymer®, Optopolymer, Munich, Germany) in order to correct sensor noise and illumination inhomogeneities. The resulting normalized reflectance image at each wavelength was represented as a grayscale image according to its reflectance intensity (range: 0–1; dark: low reflectance intensity, bright: high reflectance intensity).
Polarized light microscopy: PLM of histological cross-sections was used to determine the reference occlusal health state. Cross-sections of approx. 80 µm thickness were created at the clinically most questionable region of the central fissure of each tooth using Donath’s thin-section technique [29]. The sections were analyzed using a Leica DMRB polarization microscope (Leica Microsystems GmbH, Wetzlar, Germany) with crossed polarizer and analyzer and an additional full-wave retardation plate (red I plate) prior to the analyzer. Digital photographs were taken of all sections with a full-frame digital camera (SONY Alpha 7, Sony Europe B.V., Surrey, UK) attached to the microscope and in combination with a 2.5× magnification objective. No further image processing was performed. Two dentists experienced in tooth histology and not involved in the teeth’s visual examination consensually determined each tooth’s occlusal health state as intact but with stained enamel (hereafter ‘stained’) or stained and demineralized enamel (hereafter ‘stained lesion’). Fissural areas that showed natural positive birefringence exhibiting a distinctive change of color (yellow—violet) were associated with stained enamel. Stained lesions were identified by increased absorption, i.e., reduced transparency, and specific polarization properties such as negative birefringence or depolarization with a characteristic brown-dark, often droplet-shaped involvement of the surrounding enamel.
Classification and statistical analysis: Using custom MATLAB® (MathWorks, Natick, MA, USA) code, a graphical user interface (GUI) for reading, visualizing and selecting hyperspectral data was developed. Within this GUI, ten data points in close proximity to the histologically validated region were selected manually for each tooth (Figure 2a). The corresponding reflectance spectra were saved for further processing and associated with the histological occlusal state by binarization (0 = stained, 1 = stained lesion). The number of teeth and spectra within each occlusal health state group is shown in Table 1. In order to remove potential noise from the spectra, an additional second dataset was generated by applying a Savitzky–Golay filter (order = 2, frame length = 7) to the selected raw reflectance spectra (Figure 2b). The Savitzky–Golay filter is well established in (hyper)spectral data analysis because it enables an increase in data precision without distorting the underlying signal [30]. Intensities at the wavelengths 505–525 nm were eliminated due to the high noise level of the camera sensor in this region.
The filtered, cropped spectra were used to train 24 classification algorithms of artificial intelligence. These include algorithms of the subgroups support vector machines (SVM), nearest-neighbor (kNN) and decision trees. An SVM tries to find the best plane (line) that divides data points into their respective classes. The mathematical approach by which the plane is found varies between different SVMs [31,32]. k-Nearest-neighbor algorithms count the proportion of one class’s data points within the k number of this data point’s neighbors compared to other classes’ data points in the vicinity of a given data point. kNN algorithms differ in the number of neighbors they consider and the distance metric used in calculating which neighboring data points are closest [33]. A decision tree considers one data feature after another. At each decision node, the path is split and the next feature is considered. Decision tree algorithms vary in their maximum number of decisions and the number of data points belonging to a terminal node [34,35].
The resulting classification models were validated by stratified 10-fold cross-validation using MATLAB®’s Classification Learner toolbox. Automated cross-validation ensures that no single data point is present in the training set and validation set at the same time: The dataset is divided randomly into 10 evenly distributed subsets, which are then sequentially alternated as training sets and validation sets used to train and to validate, respectively, each single classification algorithm. For illustration, in the first round of cross-validation, subsets 1–9 are used for algorithm training, while subset 10 is used for determining the classification model’s performance. Then, cross-validation repeats this procedure 10 times, alternating the validation subset with a different training subset in every round [36,37,38].
Due to the imbalanced distribution of represented groups (see Table 1), Matthews correlation coefficient (MCC) was chosen as a performance indicator. MCC is a more appropriate metric for measuring the performance of imbalanced, binary classification tasks compared to traditional other confusion matrix-based measures, such as accuracy or the F1-score [39]. Nonetheless, this study also mentions sensitivity and specificity as traditional metrics for easier comparability. MCC is calculated as follows:
MCC = TP × TN − FP × FN ( TP + FP ) ( TP − FN ) ( TN + FP ) ( TN + FN )
The calculation of MCC relies on four parameters derived from the confusion matrix for each classification learner: 1. True positives (TP), spectra correctly classified as stained (0); 2. True negatives (TN), spectra correctly classified as stained lesions (1); 3. False positives (FP), spectra incorrectly classified as stained (0); 4. False negatives (FN), spectra incorrectly classified as stained lesions (1).
MCC returns a value between −1 and +1. A MCC of +1 indicates a perfect prediction, 0 indicates random prediction and −1 indicates a perfect inverse prediction. The algorithm that yielded the classification model with the highest MCC value after 10-fold cross validation was selected for further use.
To simulate the algorithm’s application in a clinical setting, five further teeth that were not included in algorithm training and model generation were imaged and processed with the same setup and protocol. Then, the highest-scoring classification model was applied to HSI reflectance images of these five additional teeth and predicted an enamel health state (stained or stained lesion) for the spectrum at each pixel within the tooth’s selected fissural area. These predictions were color-coded and superimposed onto an image of the tooth’s occlusal surface (green = stained, 0; red = stained lesion, 1) for qualitative assessment. Histological cross-sections were prepared for reference according to the method described above.

3. Results

Based on the previously described criteria for occlusal state validation with PLM, the investigated teeth were categorized as ‘stained’ or as ‘stained lesion’. Table 1 shows the data distribution after histological assignment of each sample’s occlusal state. Of the analyzed 65 teeth, 12 were categorized as stained and 53 as stained lesions, resulting in an imbalanced data set.
The spectral characteristics of the stained and stained lesion groups are contrasted in the mean reflectance spectrum (Figure 3). The difference spectrum (difference = reflectancestained − reflectancestained lesion) illustrates more clearly that the mean reflectance of stained enamel is higher than that of stained lesions in the wavelength range 525–710 nm. In contrast, stained lesions showed a higher mean reflectance in the wavelength range 710–1000 nm. This relationship is also visible in the raw reflectance images, where stains appear transparent at near-infrared wavelengths (Figure 3, dotted white lines), but stained lesions show an increased reflectance compared to the surrounding sound enamel (Figure 3, white arrows).
Classification algorithms were able to distinguish well between the spectra of stained enamel and stained lesions. A kNN classification algorithm considering k = 1 neighbor, utilizing Euclidian distance metric and equal distance weights (“fine kNN”) achieved the highest performance (MCC = 0.75, sensitivity of 0.95, specificity of 0.80) when trained on the filtered dataset. Algorithms performed slightly better on when trained on filtered data than when trained on raw, unfiltered reflectance data. The confusion matrices of the highest-performing algorithm trained on the filtered and on the unfiltered dataset, respectively, of 65 teeth are shown in Table 2.
The classification model generated by the fine kNN algorithm was used to classify the fissural systems of five additional teeth, which were not included in the training and evaluation of the algorithm (Table 3). The projection maps show that the classified enamel state is in good agreement with the PLM images in the histologically validated area: Stained samples show a nearly homogeneous distribution of the predicted enamel state “stained”. However, some clearly circumscribed regions away from the histologically validated area are classified as “stained lesion”. Stained lesion samples exhibit a more heterogeneous distribution of enamel health states: A large proportion of fissural areas are classified as “stained lesion”; however, there are numerous “stained” regions of varying sizes dispersed in between.

4. Discussion

Based on HSI measurements, the occlusal surfaces of posterior teeth were classified into stained but sound enamel and stained lesions using a classification algorithm of artificial intelligence, and its classification performance was validated with PLM. The classification model was subsequently used to classify the enamel health of further teeth’s occlusal surfaces, the results of which were color-coded and mapped onto an occlusal projection of each respective tooth, allowing immediate assessment of large parts of its occlusal fissural system.
This study used a wide range of wavelengths, unlike previous research focusing on intensities at specific wavelengths [12]. As dental hard tissue possesses no specific absorption bands in the 505–1000 nm range [40], this approach may aid classification performance. Stains can be detected in the employed wavelength range and there is little interference from water bands, which occur above 1450 nm [41,42,43].
Previous research suggests that stains appear transparent beyond 1150 nm [41,44], and hence no longer confound demineralization detection. This study, however, suggests that stains may appear somewhat transparent at lower NIR wavelengths already, as the underlying demineralization present in stained lesions became visible at wavelengths over approximately 800 nm (see Figure 3), which possibly originates from the increased scattering of incipient demineralizations. Nonetheless, the performance of differentiating stains from stained lesions by means of classification algorithms in the wavelength range 1000–2000 nm would be of great interest. However, costlier InGaAs sensors are then required, which could impede widespread clinical adoption. Further research is also required to investigate the transparency of stains of different origins in the NIR.
The teeth in this study were free of plaque, saliva and blood. For clinical application, these complicating circumstances must be considered. The effect of plaque, saliva and blood present on a tooth’s occlusal surface on the feasibility and quality of hyperspectral imaging and classification performance needs to be assessed.
The results in this study are based on the simplified assumption of a homogeneous distribution of the occlusal health state that was histologically determined at the central region of the fissure. Occlusal caries originates from the fissure in the vast majority of cases. The part of the fissure that clinical experience showed to be the most questionable part (i.e., the most likely to be carious) was selected for further analysis in this study, as this is the clinically most relevant issue. As caries is a dynamic process, however, occlusal classification images show heterogeneous enamel health states (Table 3). Lesions may develop in one or more sites of the fissure and subsequently spread to further areas. Hence, it is likely that some parts of an analyzed tooth are still unaffected, whereas others contain lesions of varying extent. Thus, teeth showing sound but stained enamel in the histological cross-sectional plane may show lesions in other parts of their fissure (Table 3a,b). On the other hand, histologically carious teeth show still unaffected fissural regions (Table 3c,e). In order to refine the validation process, further cross-sections from different parts of the tooth’s fissural system should be considered. Occlusal caries originating from outside the fissure are rare and pose no pertinent clinical issue. In close proximity to the histologically validated areas, the HSI-based classification results show very good agreement with the reference occlusal health state determined by PLM of ground sections (MCC = 0.75).
The uneven distribution of samples between stained but intact and stained demineralized teeth hinders the use of traditional confusion matrix-based measures such as sensitivity, specificity, the F1-Score or accuracy, as these can encounter strong bias from imbalanced data [45]. MCC was calculated as an additional performance metric because of its reduced susceptibility to bias in imbalanced data sets, and it should be considered more frequently when interpreting classification results for easier performance comparison between different uneven sample sets.
For validating classification performance, a cross-validation approach was used due to the limited sample size of only 65 teeth. Classical machine learning concepts, as proposed in our work, frequently utilize cross-validation since a larger proportion of the available data can be used for classifier training and hence the sample’s heterogeneity can be considered more reliably, improving classification performance and validity within the constraints of a small dataset [36,37,38]. By contrast, a hard train–test split would have severely reduced the number of teeth available for training and for validating the classification algorithm. Hence, the classification model’s performance and the results’ validity would have been negatively impacted. With stratified 10-fold cross-validation, a compromise was found to maintain strict separation of training and test data, while utilizing a larger sample size to ensure maximum classification performance and validity. This study shows that a reliable distinction between stained but intact enamel and stained lesions is possible with automated classification based on HSI measurements. Further studies should consider a larger sample size and generate an extensive spectral database of stained and stained lesion enamel to account for interindividual biological variance in enamel spectra and for different causes of stains, in an effort to improve classification performance. Especially the group of stained enamel, with its small number of samples in this study but heterogeneous appearance, would likely profit from such a database to ensure sufficient representation of its biological variety and prevent incorrect classification. A similar database for the 900–1700 nm wavelength region has been constructed [11,46]. With a database’s larger sample size, a train–test split would become feasible with negligible effects on classification performance and result validity. Moreover, the effect of using deep learning algorithms, as opposed to traditional machine learning algorithms, on classification performance should be explored. Multiple histological cross-sections per tooth should be considered, allowing precise evaluation of a tooth’s entire fissure.
In a later phase, results should be validated in vivo, considering the aggravating circumstances of plaque, saliva and possibly blood present on tooth surfaces. The real-time analysis of samples is important to develop to enable routine clinical application. In order to perform in vivo studies, compact handheld HSI devices for intraoral applications are needed. Currently, an endoscopic HSI camera is under development, which will be used in further studies.

5. Conclusions

The automated classification of occlusal HSI measurements into stained but intact enamel and stained lesions shows great clinical potential and may become an objective decision-support approach for assessing visually questionable fissures. It may therefore improve the detection of incipient lesions and their subsequent referral to noninvasive treatment. Further studies should focus on generating an extensive spectral database of enamel, on validating results in vivo, and on devising an intraoral HSI probe.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/app12147312/s1, Document S1: Custom computer code for data analysis.

Author Contributions

Conceptualization, F.T. and C.H.; methodology, R.V. and F.T.; software, F.T., J.G. and R.V.; validation, R.V. and F.T.; formal analysis, R.V., F.T. and J.W.; investigation, R.V. and F.T.; resources, R.V., F.T. and J.G.; data curation, R.V., F.T. and J.G.; writing—original draft preparation, R.V.; writing—review and editing, F.T., C.H., J.W. and E.K.; visualization, R.V. and F.T.; supervision, C.H. and E.K.; project administration, F.T.; funding acquisition, F.T. and J.G. All authors have read and agreed to the published version of the manuscript.

Funding

This project was supported by the European Union/European Social Fund (ESF) and the Free State of Saxony through the ESF junior research group “Optical Technologies in Medicine” (Project No. 100270108). This project was also supported by the Saxon State Parliament (Topic: Innovative optical technologies for optimizing and evaluating the application of adhesive systems and filling materials in dentistry (INNO-FILL)), application number: 100368188. The author Jonas Golde was supported by the European Union/ESF and the Free State of Saxony within a doctoral scholarship (Project No. 100284305).

Institutional Review Board Statement

This study utilized de-identified samples of human tissue previously extracted in clinical routine. All teeth were extracted for medically justified reasons not related to this study. In accordance with the Central German Ethics Committee, such use is deemed exempt from ethical approval [26].

Informed Consent Statement

No information was collected about the donor patients’ name, age, sex or general health.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available because custom code for proprietary software is required for reading and due to the large data size. The code used is provided as Supplementary Material.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Schwendicke, F.; Elhennawy, K.; El Shahawy, O.; Maher, R.; Gimenez, T.; Mendes, F.M.; Willis, B.H. Visual and radiographic caries detection: A tailored meta-analysis for two different settings, Egypt and Germany. BMC Oral Health 2018, 18, 105. [Google Scholar] [CrossRef] [Green Version]
  2. Ekstrand, K.; Qvist, V.; Thylstrup, A. Light Microscope Study of the Effect of Probing in Occlusal Surfaces. Caries Res. 1987, 21, 368–374. [Google Scholar] [CrossRef]
  3. Kühnisch, J.; Dietz, W.; Stösser, L.; Hickel, R.; Heinrich-Weltzien, R. Effects of dental probing on occlusal surfaces—A scanning electron microscopy evaluation. Caries Res. 2006, 41, 43–48. [Google Scholar] [CrossRef] [Green Version]
  4. Ricketts, D.; Kidd, E.; Weerheijm, K.; de Soet, H. Hidden caries: What is it? Does it exist? Does it matter? Int. Dent. J. 1997, 47, 259–265. [Google Scholar] [CrossRef]
  5. Mazur, M.; Jedliński, M.; Ndokaj, A.; Cipollone, A.; Nardi, G.M.; Ottolenghi, L.; Guerra, F.; Mazur, M. Challenges in diagnosing and managing non-cavitated occlusal caries lesions. A Literature overview and a report of a case. Clin. Ter. 2021, 172, 80–86. [Google Scholar] [CrossRef]
  6. Neuhaus, K.W.; Ellwood, R.; Lussi, A.; Pitts, N.B. Traditional Lesion Detection Aids. Monogr. Oral Sci. 2009, 21, 42–51. [Google Scholar] [CrossRef]
  7. Kishen, A.; Shrestha, A.; Rafique, A. Fiber optic backscatter spectroscopic sensor to monitor enamel demineralization and remineralization in vitro. J. Conserv. Dent. 2008, 11, 63–70. [Google Scholar] [CrossRef] [Green Version]
  8. Usenik, P.; Bürmen, M.; Fidler, A.; Pernuš, F.; Likar, B. Improved classification and visualization of healthy and pathological hard dental tissues by modeling specular reflections in NIR hyperspectral images. Proc. SPIE 2012, 8315, 765–770. [Google Scholar]
  9. Usenik, P.; Bürmen, M.; Fidler, A.; Pernuš, F.; Likar, B. Evaluation of cross-polarized near infrared hyperspectral imaging for early detection of dental caries. Proc. SPIE 2012, 8208, 89–91. [Google Scholar]
  10. Usenik, P.; Bürmen, M.; Fidler, A.; Pernuš, F.; Likar, B. Near-infrared hyperspectral imaging of water evaporation dynamics for early detection of incipient caries. J. Dent. 2014, 42, 1242–1247. [Google Scholar] [CrossRef]
  11. Usenik, P.; Bürmen, M.; Fidler, A.; Pernuš, F.; Likar, B. Automated classification and visualization of healthy and diseased hard dental tissues by near-infrared hyperspectral imaging. Appl. Spectrosc. 2012, 66, 1067–1074. [Google Scholar] [CrossRef]
  12. Zakian, C.; Pretty, I.; Ellwood, R. Near-infared hyperspectral imaging of teeth for dental caries detection. J. Biomed. Opt. 2009, 14, 064047. [Google Scholar] [CrossRef] [Green Version]
  13. Lu, G.; Fei, B. Medical hyperspectral imaging: A review. J. Biomed. Opt. 2014, 19, 010901. [Google Scholar] [CrossRef]
  14. Goetz, A.F.H. Three decades of hyperspectral remote sensing of the Earth: A personal view. Remote Sens. Environ. 2009, 113, S5–S16. [Google Scholar] [CrossRef]
  15. Patterson, M.S.; Wilson, B.C.; Wyman, D.R. The propagation of optical radiation in tissue. II: Optical properties of tissues and resulting fluence distributions. Lasers Med. Sci. 1991, 6, 379–390. [Google Scholar] [CrossRef]
  16. Mobley, J.; Vo-Dinh, T. Optical Properties of Tissue. In Biomedical Photonics Handbook; CRC Press: Boca Raton, FL, USA, 2003; pp. 42–116. [Google Scholar]
  17. Ferris, D.G.; Lawhead, R.A.; Dickman, E.D.; Holtzapple, N.; Miller, J.A.; Grogan, S.; Bambot, S.; Agrawal, A.; Faupel, M.L. Multimodal hyperspectral imaging for the noninvasive diagnosis of cervical neoplasia. J. Low. Genit. Tract. Dis. 2001, 5, 65–72. [Google Scholar] [CrossRef] [Green Version]
  18. Argov, S.; Ramesh, J.; Salman, A.; Sinelnikov, I.; Goldstein, J.; Guterman, H.; Mordechai, S. Diagnostic potential of Fourier-transform infrared microspectroscopy and advanced computational methods in colon cancer patients. J. Biomed. Opt. 2002, 7, 248–254. [Google Scholar] [CrossRef]
  19. Roblyer, D.; Kurachi, C.; Gillenwater, A.M.; Richards-Kortum, R. In vivo fluorescence hyperspectral imaging of oral neoplasia. Proc. SPIE 2009, 7169, 96–105. [Google Scholar]
  20. Dicker, D.T.; Lerner, J.; Van Belle, P.; Barth, S.F.; Guerry, D.P., IV; Herlyn, M.; Elder, D.E.; El-Deiry, W.S. Differentiation of normal skin and melanoma using high resolution hyperspectral imaging. Cancer Biol. Ther. 2006, 5, 1033–1038. [Google Scholar] [CrossRef]
  21. Zuzak, K.J.; Naik, S.C.; Alexandrakis, G.; Hawkins, D.; Behbehani, K.; Livingston, E.H. Characterization of a Near-Infrared Laparoscopic Hyperspectral Imaging System for Minimally Invasive Surgery. Anal. Chem. 2007, 79, 4709–4715. [Google Scholar] [CrossRef]
  22. Chin, J.A.; Wang, E.C.; Kibbe, M.R. Evaluation of hyperspectral technology for assessing the presence and severity of peripheral artery disease. J. Vasc. Surg. 2011, 54, 1679–1688. [Google Scholar] [CrossRef] [Green Version]
  23. Johnson, W.R.; Wilson, D.W.; Fink, W.; Humayun, M.S.; Bearman, G.H. Snapshot hyperspectral imaging in ophthalmology. J. Biomed. Opt. 2007, 12, 014036. [Google Scholar] [CrossRef] [Green Version]
  24. Schweizer, J.; Hollmach, J.; Steiner, G.; Knels, L.; Funk, R.H.W.; Koch, E. Hyperspectral imaging—A new modality for eye diagnostics. Biomed. Tech. 2012, 57, 293–296. [Google Scholar] [CrossRef]
  25. Schwendicke, F.; Golla, T.; Dreher, M.; Krois, J. Convolutional neural networks for dental image diagnostics: A scoping review. J. Dent. 2019, 91, 103226. [Google Scholar] [CrossRef]
  26. Central German Ethics Committee. The use of human body materials for the purpose of medical research. Dtsch. Arztebl. 2003, 100, A1632–A1635. [Google Scholar]
  27. Ekstrand, K.R.; ICDAS Criteria For Detecting Coronal Caries. International Caries Detection and Assessment System. 2005, pp. 1–52. Available online: https://www.icdas.org/uploads/ICDASII_training_packet.ppt (accessed on 3 February 2018).
  28. Tetschke, F.; Kirsten, L.; Golde, J.; Walther, J.; Galli, R.; Koch, E.; Hannig, C. Application of optical and spectroscopic technologies for the characterization of carious lesions in vitro. Biomed. Eng. Biomed. Tech. 2018, 63, 595–602. [Google Scholar] [CrossRef]
  29. Donath, K. Preparation of Histologic Sections by the Cutting-Grinding Technique for Hard Tissue and Other Materials Not Suitable to Be Sectioned by Routine Methods; EXAKT–Kulzer–Publication: Norderstedt, Germany, 1995; pp. 1–16. [Google Scholar]
  30. Ruffin, C.; King, R.L. The analysis of hyperspectral data using Savitzky-Golay filtering-theoretical basis. In Proceedings of the IEEE 1999 International Geoscience and Remote Sensing Symposium, IGARSS’99 (Cat. No. 99CH36293). Hamburg, Germany, 28 June–2 July 1999; pp. 756–758. [Google Scholar]
  31. Schölkopf, B.; Smola, A.J. A Tutorial Introduction. In Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond; The MIT Press: Cambridge, MA, USA, 2001; pp. 1–22. [Google Scholar]
  32. Cortes, C.; Vapnik, V.N. Support-Vector Networks. Mach. Learn. 2004, 20, 273–297. [Google Scholar] [CrossRef]
  33. Cover, T.M.; Hart, P.E. Nearest Neighbor Pattern Classification. IEEE Trans. Inf. Theory 1967, 13, 21–27. [Google Scholar] [CrossRef] [Green Version]
  34. Quinlan, J.R. Induction of Decision Trees. Mach. Learn. 2004, 1, 81–106. [Google Scholar] [CrossRef] [Green Version]
  35. Kohavi, R.; Quinlan, R. Decision Tree Discovery. In Handbook of Data Mining and Knowledge Discovery; Oxford University Press: Oxford, UK, 1999; pp. 267–276. [Google Scholar]
  36. Alpaydin, E. Introduction to Machine Learning, 4th ed.; The MIT Press: Cambridge, MA, USA, 2020; pp. 1–712. [Google Scholar]
  37. Yadav, S.; Shukla, S. Analysis of k-Fold Cross-Validation over Hold-Out Validation on Colossal Datasets for Quality Classification. In Proceedings of the 2016 IEEE 6th International Conference on Advanced Computing (IACC), Bhimavaram, India, 27–28 February 2016; pp. 78–83. [Google Scholar]
  38. Stone, M. Cross-Validatory Choice and Assessment of Statistical Predictions. J. R. Stat. Soc. 1974, 36, 111–133. [Google Scholar] [CrossRef]
  39. Boughorbel, S.; Jarray, F.; El-Anbari, M. Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric. PLoS ONE 2017, 12, e0177678. [Google Scholar] [CrossRef]
  40. Fried, D.; Glena, R.E.; Featherstone, J.D.B.; Seka, W. Nature of light scattering in dental enamel and dentin at visible and near-infrared wavelengths. Appl. Opt. 1995, 34, 1278–1285. [Google Scholar] [CrossRef]
  41. Ng, C.; Almaz, E.C.; Simon, J.C.; Fried, D.; Darling, C.L. Near-infrared imaging of demineralization on the occlusal surfaces of teeth without the interference of stains. J. Biomed. Opt. 2019, 24, 036002. [Google Scholar] [CrossRef] [Green Version]
  42. Hale, G.M.; Querry, M.R. Optical Constants of Water in the 200-nm to 200-μm Wavelength Region. Appl. Opt. 1973, 12, 555–563. [Google Scholar] [CrossRef]
  43. Jones, R.S.; Fried, D. Attenuation of 1310- and 1550-nm laser light through sound dental enamel. Proc. SPIE 2002, 4610, 187–190. [Google Scholar]
  44. Almaz, E.C.; Simon, J.C.; Fried, D.; Darling, C.L. Influence of stains on lesion contrast in the pits and fissures of tooth occlusal surfaces from 800–1600-nm. Proc. SPIE 2016, 9692, 141–146. [Google Scholar]
  45. Chicco, D.; Jurman, G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom. 2020, 21, 6. [Google Scholar] [CrossRef] [Green Version]
  46. Bürmen, M.; Usenik, P.; Fidler, A.; Pernuš, F.; Likar, B. A construction of standardized near infrared hyper-spectral teeth database: A first step in the development of reliable diagnostic tool for quantification and early detection of caries. Proc. SPIE 2011, 7884, 81–90. [Google Scholar]
Applsci 12 07312 g001 550
Figure 1. (a) The fissure is a predilection site for occlusal caries as its narrow anatomy hampers mechanical plaque removal. (b) Carious lesions often originate from the bottom of the fissure and are characterized by demineralized enamel. If detected at this stage, non-invasive remineralization therapy can be performed. (c) Going unnoticed, the lesion progresses, infecting dentin and (d) eventually causing cavitation. Here, remineralization is no longer sufficient; restorative therapy is needed.
Figure 1. (a) The fissure is a predilection site for occlusal caries as its narrow anatomy hampers mechanical plaque removal. (b) Carious lesions often originate from the bottom of the fissure and are characterized by demineralized enamel. If detected at this stage, non-invasive remineralization therapy can be performed. (c) Going unnoticed, the lesion progresses, infecting dentin and (d) eventually causing cavitation. Here, remineralization is no longer sufficient; restorative therapy is needed.
Applsci 12 07312 g001
Applsci 12 07312 sch001 550
Scheme 1. Hyperspectral imaging captures both spatial (morphological) and spectral (chemical) information. A two-dimensional image is taken, and for each pixel a spectrum is captured as the third dimension. Light penetrates the sample, becomes scattered, absorbed and reflected, and the hyperspectral camera registers the proportion of light reflected to generate the hyperspectral image.
Scheme 1. Hyperspectral imaging captures both spatial (morphological) and spectral (chemical) information. A two-dimensional image is taken, and for each pixel a spectrum is captured as the third dimension. Light penetrates the sample, becomes scattered, absorbed and reflected, and the hyperspectral camera registers the proportion of light reflected to generate the hyperspectral image.
Applsci 12 07312 sch001
Applsci 12 07312 sch002 550
Scheme 2. Methodology flowchart for HSI-based automated classification of occlusal stains and stained lesions. After selection and preparation of 65 tooth samples, hyperspectral images were acquired and preprocessed. Classification algorithms were trained on hyperspectral data and subsequently assessed by calculating Matthews correlation coefficient (MCC). The analysis of histological cross-sections by means of PLM served as the reference method for validating enamel health states. Five further teeth were imaged, preprocessed and classified using the previously trained classification model, and enamel health prediction results were presented color-coded in projections of the samples’ occlusal surfaces.
Scheme 2. Methodology flowchart for HSI-based automated classification of occlusal stains and stained lesions. After selection and preparation of 65 tooth samples, hyperspectral images were acquired and preprocessed. Classification algorithms were trained on hyperspectral data and subsequently assessed by calculating Matthews correlation coefficient (MCC). The analysis of histological cross-sections by means of PLM served as the reference method for validating enamel health states. Five further teeth were imaged, preprocessed and classified using the previously trained classification model, and enamel health prediction results were presented color-coded in projections of the samples’ occlusal surfaces.
Applsci 12 07312 sch002
Applsci 12 07312 g002 550
Figure 2. (a) HSI spectra were selected (white crosses) within the central fissure near the histological cross-sectional plane (dashed line) and (b) preprocessed, including (i) the elimination of wavelengths with high noise (505–525 nm; shaded red) and (ii) filtering of spectra to find the optimal data configuration for classifier training.
Figure 2. (a) HSI spectra were selected (white crosses) within the central fissure near the histological cross-sectional plane (dashed line) and (b) preprocessed, including (i) the elimination of wavelengths with high noise (505–525 nm; shaded red) and (ii) filtering of spectra to find the optimal data configuration for classifier training.
Applsci 12 07312 g002
Applsci 12 07312 g003 550
Figure 3. Mean reflectance spectra of stains and stained lesions, respectively, and their difference spectrum (dotted blue line, difference = reflectancestained − reflectancestained lesion), together with sample reflectance images. The mean reflectance of stained lesions exceeds that of stained enamel for wavelengths above 710 nm, whereas it is lower or indifferent for wavelengths below 710 nm. The wavelength region 505–525 nm was eliminated due to high noise. The reflectance images show how stains visible at a wavelength of 600 nm are rendered transparent at a wavelength of 900 nm, and underlying demineralization (white arrows) becomes visible. a.u.: arbitrary units.
Figure 3. Mean reflectance spectra of stains and stained lesions, respectively, and their difference spectrum (dotted blue line, difference = reflectancestained − reflectancestained lesion), together with sample reflectance images. The mean reflectance of stained lesions exceeds that of stained enamel for wavelengths above 710 nm, whereas it is lower or indifferent for wavelengths below 710 nm. The wavelength region 505–525 nm was eliminated due to high noise. The reflectance images show how stains visible at a wavelength of 600 nm are rendered transparent at a wavelength of 900 nm, and underlying demineralization (white arrows) becomes visible. a.u.: arbitrary units.
Applsci 12 07312 g003
Table
Table 1. Distribution of analyzed samples in this study. From each of the 12 stained but intact and 53 stained lesion samples, 10 fissural enamel spectra were selected for classifier training and validation. To simulate the classifier’s application as a clinical decision-support system, an additional two stained but intact and three stained lesion samples were included.
Table 1. Distribution of analyzed samples in this study. From each of the 12 stained but intact and 53 stained lesion samples, 10 fissural enamel spectra were selected for classifier training and validation. To simulate the classifier’s application as a clinical decision-support system, an additional two stained but intact and three stained lesion samples were included.
Occlusal Enamel
Health State		Main Dataset (n = 65 Teeth, 650 Spectra)Clinical Simulation Set
(n = 5 Teeth)
Number of TeethNumber of Selected SpectraNumber of Teeth
Stained121202
Stained lesion535303
Table
Table 2. Overview of the performance characteristics of the highest-performing classification algorithm trained on unfiltered and filtered spectra, respectively. A fine kNN algorithm trained on the filtered dataset of n = 65 teeth achieved the highest MCC, indicating its remarkable performance despite the imbalanced distribution of the sample.
Table 2. Overview of the performance characteristics of the highest-performing classification algorithm trained on unfiltered and filtered spectra, respectively. A fine kNN algorithm trained on the filtered dataset of n = 65 teeth achieved the highest MCC, indicating its remarkable performance despite the imbalanced distribution of the sample.
Preprocessing ApproachHighest-Performing Classification AlgorithmMatthews Correlation Coefficient (MCC)Confusion Matrix of Algorithm Trained on the Main Dataset
(n = 650 Spectra)
UnfilteredSubspace kNN
(learners = 30, subspace dimension = 48)		0.72True Class196
(True negatives)		24
(False positives)
232
(False negatives)		498
(True positives)
12
Predicted Class
FilteredFine kNN
(k = 1, equal distance weights,
Euclidian distance metric)		0.75True Class196
(True negatives)		24
(False positives)
225
(False negatives)		505
(True positives)
12
Predicted Class
Table
Table 3. Clinical simulation of enamel health predictions using HSI-based automated classification as a decision-support system. Five additional teeth (two stained [(a) and (b)], three stained lesion [(c) through (e)]) were imaged and classified by the previously trained fine kNN classification algorithm (i.e., the samples in this table were not included in algorithm training). Stained sound enamel is displayed green, whereas lesions (stained or unstained) are displayed red. Polarization microscopy images from each tooth’s sectional plane (dotted lines) serve as reference. All scale bars = 1 mm.
Table 3. Clinical simulation of enamel health predictions using HSI-based automated classification as a decision-support system. Five additional teeth (two stained [(a) and (b)], three stained lesion [(c) through (e)]) were imaged and classified by the previously trained fine kNN classification algorithm (i.e., the samples in this table were not included in algorithm training). Stained sound enamel is displayed green, whereas lesions (stained or unstained) are displayed red. Polarization microscopy images from each tooth’s sectional plane (dotted lines) serve as reference. All scale bars = 1 mm.
Occlusal PhotographHSI-Based Classification Applsci 12 07312 i001Polarized Light
Microscopy		Histologically Determined Enamel State
(a) Applsci 12 07312 i002 Applsci 12 07312 i003 Applsci 12 07312 i004sound enamel
(b) Applsci 12 07312 i005 Applsci 12 07312 i006 Applsci 12 07312 i007sound enamel
(c) Applsci 12 07312 i008 Applsci 12 07312 i009 Applsci 12 07312 i010lesion
(d) Applsci 12 07312 i011 Applsci 12 07312 i012 Applsci 12 07312 i013lesion
(e) Applsci 12 07312 i014 Applsci 12 07312 i015 Applsci 12 07312 i016lesion
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Vosahlo, R.; Golde, J.; Walther, J.; Koch, E.; Hannig, C.; Tetschke, F. Differentiation of Occlusal Discolorations and Carious Lesions with Hyperspectral Imaging In Vitro. Appl. Sci. 2022, 12, 7312. https://doi.org/10.3390/app12147312

AMA Style

Vosahlo R, Golde J, Walther J, Koch E, Hannig C, Tetschke F. Differentiation of Occlusal Discolorations and Carious Lesions with Hyperspectral Imaging In Vitro. Applied Sciences. 2022; 12(14):7312. https://doi.org/10.3390/app12147312

Chicago/Turabian Style

Vosahlo, Robin, Jonas Golde, Julia Walther, Edmund Koch, Christian Hannig, and Florian Tetschke. 2022. "Differentiation of Occlusal Discolorations and Carious Lesions with Hyperspectral Imaging In Vitro" Applied Sciences 12, no. 14: 7312. https://doi.org/10.3390/app12147312

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop