The authors thank Dirk Leonhardt for his excellent assistance in the additive manufacturing of the custom-made spacers. Lene Gr&#248;nkj&#230;r, Javier E. Garcia, Charlotte K. Vindbjerg, and Sussi B. Eriksen are acknowledged for their technical support during the study. The authors would also like to thank Matthias Beck for technical support on the use of the fluorescence camera and Mette R. J&#248;rgensen for fruitful discussions.

The study was approved by the Ethical Committee of Region Midtjylland (1-10-72-259-21) and performed in compliance with the Helsinki Declaration and its amendments.
1. Fabrication of a custom-made spacer (optional)
NOTE: A custom-made 3D-printed spacer may be used during image acquisition to standardize the positioning of the camera head. The spacer is not mandatory for the recording of the fluorescence images.
<ol>
	<li>Design of the spacer
	<ol>
		<li>Design a spacer that fits the camera head of the intraoral fluorescence camera. To do so, perform a scan of the camera head with a digital scanner. Import the scan into dedicated software.</li>
		<li>Design the spacer to fit the camera head with the desired morphology and positioning distance to the camera head (i.e., 4 mm). Export as an STL file (an example of a design is attached as Supplemental File S1).</li>
	</ol>
	</li>
	<li>Additive manufacturing of the spacer
	<ol>
		<li>Open the printer-associated additive manufacturing software, and select the basic settings. Click on Printer | Select the available 3D printer | Next | Shape: Clear | Next | Print mode: 50 microns | Next | Build Style: Standard | Next.</li>
		<li>Import the STL file by clicking on File | Import | Select the STL file | Open.</li>
		<li>Define the position of the spacer on the print platform; click on Transform, and drag the spacer to a corner of the platform as close to the surface of the platform as possible.</li>
		<li>To print additional spacers, click on Copy | Linear pattern. Adjust the count and distance to fit additional objects to the print platform, and click on Set.</li>
		<li>To design the support of the objects, click on Smart support | Style: General | Generate | Type: Gate | Create support.</li>
		<li>Send the print job to the 3D printer. Click on Add to queue. The software automatically performs a quality check of the STL file to identify errors when adding to the queue. Then, click on Add to queue | Job name | F4X | Add to queue.</li>
		<li>Mount a clean print platform on the 3D printer, and add an appropriate resin. Click on Start job, and scan the QR code of the resin. Confirm that the print platform is empty and clean, that the resin tray is full, and that the resin has been stirred before addition. Click on Start job.</li>
		<li>When the print job is finished, remove the spacers from the print platform.</li>
		<li>Clean the spacers in an ultrasound bath with isopropanol for 3 min. Repeat the cleaning using fresh isopropanol. Air-dry the spacers.</li>
		<li>Secure the total polymerization of the material by polymerizing the spacers in a post-curing oven for 10 min.</li>
		<li>Remove the support material, and stain the spacers to prevent light from penetrating through the material.</li>
	</ol>
	</li>
</ol>
2. Plaque disclosure and image acquisition
<ol>
	<li>Mount the custom-made spacer on the fluorescence camera (optional). Connect the intraoral camera to a computer, and open the camera software.</li>
	<li>Click on Patient | New patient to create the patient in the system. Fill out the patient information. Click on Patient | Save to save the patient data. Click on Video. The intraoral camera is now ready to use.</li>
	<li>Dim the room lights.</li>
	<li>Apply a red disclosing dye (i.e., 5% erythrosine) with a cotton pellet on the tooth surfaces of interest to disclose the plaque.</li>
	<li>Instruct the patient to rinse with water for 10 s to remove excess dye. Remove any gingival stain using a cotton pellet. Air-dry each tooth for 3 s.</li>
	<li>Place the intraoral camera in a horizontal position in front of the tooth of interest, with the spacer touching the gingiva/adjacent teeth. Acquire the fluorescence image by pressing the camera button. 
	NOTE: Ensure that the entire tooth surface of interest is in focus and captured in the image without including antagonist or contralateral tooth surfaces.</li>
	<li>Repeat steps 2.4-2.6 for all the teeth of interest.</li>
	<li>Mark all the images in the camera software. Click on Save images/videos in the menu. 
	NOTE: Make sure that the images are saved in &#34;plaque&#34;-mode and not in &#34;caries&#34;-mode. The symbol P/C in the menu indicates the current mode.</li>
	<li>To export the images, go to the Viewer. Choose the images to be exported. Click on File | Export (save as&#8230;) | All images of the patient to export the images. In the export window, choose the following settings: Mode: Standard | Export path: Choose the desired folder | Image type selection: Check off the left box | Image state: Original data. Expand the export window to display more options. Select the following: File name contains: Cardnumber OR User input OR Patient name | Format: TIF. Click on OK to export the images.
	<ol>
		<li>Alternatively, set up an automated file export prior to imaging. Click on Options | Show configuration | Modules | Viewer | Export/Email | Export options | Mode: Autoexport| Export path: Choose the desired folder | Image state: Original data. Select the following: File name contains: Cardnumber OR User input OR Patient name | Format: TIF. Click on OK to set up the default export settings. When the automated file export is set up, the images will automatically be exported when saved (step 2.8).</li>
	</ol>
	</li>
</ol>
3. Digital image analysis
NOTE: The digital image analysis can be performed at any time after the image acquisition. Batches of up to 1,000 fluorescence images can be processed in parallel. If the analysis of large image batches exceeds the computing power, the image size may be reduced prior to analysis.
<ol>
	<li>Quantification of the total tooth area
	<ol>
		<li>Rename all the images with sequential index numbers (i.e., Planimetry_001, Planimetry_002,&#8230;). Import the fluorescence image series into a dedicated image analysis software (i.e. Daime15) in Red-Green-Blue (RGB) mode by clicking on File | Import images | Import as color.</li>
		<li>Perform a threshold-based segmentation of the image series by clicking Segment | Automatic segmentation | Custom threshold. Set the &#34;Low&#34; threshold above the intensity of the oral soft tissues (i.e., 80). Leave the &#34;High&#34; threshold at 255. Thus, only the teeth (both clean and plaque-covered areas) are recognized as objects in the software. Click on Apply | OK | Segment! to initiate the segmentation.</li>
		<li>Open the visualizer by double-clicking on the name of the image series. Enter the object editor (OBJ). Perform a visual quality control of the segmented images, and delete artifacts by rejecting and deleting such objects.</li>
		<li>Merge the remaining objects in all the images (In all images | Merge selected objects). Now, there is one object per image. Quantify the total tooth area in each image (Analysis | Measure objects | Clear all | Pixels). Export the data.</li>
	</ol>
	</li>
	<li>Quantification of the plaque-covered areas
	<ol>
		<li>Import the fluorescence image series again into the software, this time with split red, green, and blue color channels (File | Import images | Import as grey). Close the blue channel images. Transfer the object layer from the RGB images to the red channel images (Segment | Transfer object layer).</li>
		<li>Delete non-object pixels in the red channel images using the object editor (In all images | Delete non-object pixels (voxels)). Soft tissues are now removed from the images.</li>
		<li>To enhance the contrast between plaque-covered and clean tooth areas, multiply the red channel image series by a factor of two (Edit | Image calculator | Multiplication | Parameters: Factor 2.00 | Apply | OK).</li>
		<li>To remove clean tooth areas from the images, subtract the green channel image series from the enhanced red channel image series (Edit | Image calculator | Second operand images: Planimetry_green | Subtraction | Apply | OK).</li>
		<li>To identify the plaque-covered areas on the teeth, perform a threshold-based segmentation of the resulting image series (Segment | Automatic segmentation | Custom threshold). Set the &#34;Low&#34; threshold above the intensity of the clean tooth areas (i.e., 80). Leave the &#34;High&#34; threshold at 255. Only plaque-covered areas are recognized as objects in the software. Click on Apply | OK | Segment! to initiate the segmentation.</li>
		<li>Perform a visual quality control of the segmented images in the object editor, and delete artifacts by rejecting and deleting such objects. Merge the remaining objects in all the images (In all images | Merge selected objects). Quantify the plaque-covered area in each image (Analysis | Measure objects | Clear all | Pixels). Export the data.</li>
		<li>Open the exported data tables in a dedicated software. Calculate the PPI according to equation (1): 
		Equation (1)&#160; &#160; &#160;<img alt="Equation 1" src="/files/ftp_upload/65035/65035eq01.jpg" style="margin: auto;" /></li>
	</ol>
	</li>
</ol>

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K., Schlafer, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+fast+and+reliable+method+for+semi-automated+planimetric+quantification+of+dental+plaque+in+clinical+trials.">A fast and reliable method for semi-automated planimetric quantification of dental plaque in clinical trials.</a> Journal of Clinical Periodontology. , (2022).</li><li>Baab, D. A., Broadwell, A. H., Williams, B. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparison+of+antimicrobial+activity+of+four+disclosant+dyes.">A comparison of antimicrobial activity of four disclosant dyes.</a> Journal of Dental Research. 62 (7), 837-841 (1983).</li><li>Begue, W. J., Bard, R. C., Koehne, G. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microbial+inhibition+by+erythrosin.">Microbial inhibition by erythrosin.</a> Journal of Dental Research. 45 (5), 1464-1467 (1966).</li><li>Marsh, P. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Antibacterial+activity+of+some+plaque-disclosing+agents+and+dyes+(short+communication).">Antibacterial activity of some plaque-disclosing agents and dyes (short communication).</a> Caries Research. 23 (5), 348-350 (1989).</li><li>Shakibaie, F., Walsh, L. 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The study was funded by Novozymes A/S and the Innovation Fund Denmark (grant number 9065-00244B). The funders had no role in the collection, analysis, and interpretation of data or in the writing of the report.

The presented method for semi-automated planimetry based on fluorescence images constitutes an improvement in dental plaque quantification on sound tooth surfaces in the research environment compared to traditional planimetry20. Semi-automated planimetry allows for the simultaneous determination of the PPI in up to 1,000 images using a pre-determined post-processing algorithm. Thereby, the method is considerably more time-efficient than conventional planimetry, where the total tooth areas and plaque-covered areas are determined manually by drawing regions of interest in an image processing software7,12. Additionally, the extent of human judgment in the image analysis is reduced to the choice of a brightness threshold for image segmentation. Thereby, all the images are treated alike, and the influence of examiner subjectivity is greatly reduced11.
The critical steps in the protocol are predominantly related to the clinical procedures, which need to be performed in a highly standardized fashion for optimal image quality. The disclosing solution must be applied gently and uniformly, and the images should be acquired right after rinsing and air-drying to avoid a washout of the dye and, thus, a loss of image contrast. Moreover, gingival bleeding needs to be avoided, since hemoglobin may enhance the recorded fluorescence in the red channel19. The image capture should be performed with the room lights dimmed to reduce the interference of ambient light, and the patients need to open their mouths sufficiently, such that the antagonist teeth do not appear in the images. The camera head must be positioned perpendicularly to the tooth axis to avoid capturing part of the occlusal surface and contralateral teeth.
Artifacts that result from sub-optimal image acquisition can - in most cases - be removed during the image analysis, although at the expense of a considerably increased processing time. Some artifacts that are recognized as objects during segmentation can be cleared by simple deletion in the object editor. If artifacts are confluent with the areas recognized as plaque, the resulting objects have to be split in the object editor prior to removal. In extreme cases, the operator may have to recur to the manual determination of the clean tooth and plaque-covered areas by drawing regions of interest in the software. If all the clinical procedures are performed accurately, the only subjective input of the operator during the image analysis consists of determining the cutoff values for the threshold-based segmentations. Generally, the plaque-covered and clean tooth areas are well-defined in the images, but it needs to be mentioned that small differences in the chosen thresholds do influence the calculated PPI values, albeit to a relatively low extent. As all images acquired for a particular study can be segmented with identical thresholds, the subjective choice of cutoff values does not affect the differences between the treatment or patient groups.
Just like manual planimetry, semi-automated planimetry is not suitable for longitudinal recordings of plaque build-up due to the use of a disclosing solution. Erythrosine may interfere with biofilm growth through an antibacterial activity21,22,23, but most importantly, the prominent stain necessitates professional plaque removal before the patient is sent home. However, the described method may be used for the regular quantification of habitual plaque levels in the clinic. Another limitation of semi-automated planimetry arises due to the size differences between individual teeth. Although the distance between the camera and tooth surface and, hence, the size of the field of view can be standardized, the acquired images may include parts of the neighboring teeth. These cannot be removed by a batch operation but only by manual cropping of the images during the analysis. While semi-automated planimetry is appropriate for the quantification of supragingival plaque and calculus24 on sound tooth surfaces, future work will have to determine how the described method is affected by developmental defects25, cavitated and non-cavitated caries lesions, as well as severe stain.
In conclusion, semi-automated planimetry is a method that allows for the rapid and reliable quantification of plaque area coverage using a fluorescence camera. It may be employed in clinical trials that assess de novo plaque formation in different patient groups or the effect of different treatment regimens on plaque removal.

The quantification of dental plaque in the research environment is either performed using clinical indices or, otherwise, by recording the planimetric plaque index (PPI)1. Clinical indices, such as the Turesky modified Quigley-Hein plaque index, rely on the visual assessment of plaque coverage by an operator and the subsequent assignment of a score on an ordinal scale2. While the scoring is quick, the use of clinical indices requires laborious inter-examiner and intra-examiner calibration, and the rating always suffers from a certain degree of subjectivity3,4,5. Moreover, as the number of scores is limited, clinical indices may fail to detect relevant differences in plaque coverage6.
For planimetric recordings, the extent of plaque coverage is determined on digital images by dividing the plaque-covered area by the total area of the tooth surface7. The use of a continuous scale increases the accuracy and shows high discriminatory power in statistical analysis8,9,10. Moreover, one may argue that planimetry is less subjective, as the index is calculated and not estimated by the examiner11. Traditionally, plaque-covered and total tooth areas have been determined manually for PPI recordings by drawing regions of interest in each image using image-processing software7,12. Consequently, planimetric analysis was previously very time-consuming, which reduced its applicability for larger clinical studies6.
On traditional white-light images, the contrast between plaque-covered areas, clean tooth areas, and the surrounding tissues is faint, and, thus, automated image processing, which typically relies on the intensity-based detection of objects, is severely hampered13,14. Images that are acquired with a fluorescence camera show a significantly enhanced contrast between disclosed plaque, clean teeth that auto-fluoresce strongly in the green spectrum, and non-fluorescent soft tissues1.
Here, we present a method for semi-automated planimetry that greatly reduces the time spent on image analysis compared to traditional PPI recordings. The method employs standard disclosing procedures, a commercially available fluorescence camera, and an image analysis freeware. The parameters important for image acquisition and image analysis, as well as typical mistakes and limitations of the method, are discussed.

<table><tbody><tr><td>3D Sprint Basic</td><td>3D systems</td><td></td><td>Additive manufacturing software</td></tr><tr><td>5% erythrosine; Top Dent Rondell R&amp;ouml;d</td><td>Top&amp;nbsp;Dent&amp;nbsp;Lifco Dental AB</td><td>6327</td><td>Disclosing solution</td></tr><tr><td>D1000 lab scanner</td><td>3 Shape</td><td></td><td>Lab scanner used to scan the camera head</td></tr><tr><td>DBSWIN 5.17.0</td><td>D&amp;uuml;rr Dental</td><td></td><td>Software for VistaCam</td></tr><tr><td>Digital image analysis in microbial ecology (Daime), version 2.2.2</td><td></td><td></td><td>Freeware for image analysis</td></tr><tr><td>LC-3D Print Box</td><td>NextDent</td><td></td><td>Polymerization unit</td></tr><tr><td>Meshmixer 3.5</td><td>Autodesk</td><td></td><td>Freeware for designing custom-made spacer</td></tr><tr><td>NextDent 5100</td><td>3D systems</td><td></td><td>3D-printer</td></tr><tr><td>NextDent Ortho IBT</td><td>3D systems</td><td></td><td>Material for spacer</td></tr><tr><td>Ultrasound bath T660/H</td><td>Elma Schmidbauer GmbH</td><td></td><td></td></tr><tr><td>VistaCam iX HD Smart intraoral camera&amp;nbsp;</td><td>D&amp;uuml;rr Dental</td><td></td><td>Coupled with a fluorescence camera head</td></tr></tbody></table>

semi-automated planimetric quantification of dental plaque using an intraoral fluorescence camera

Dental plaque accumulation is quantified using clinical indices or, otherwise, the planimetric plaque index (PPI),&#160;which measures the relative area of a tooth that is covered by plaque deposits. Compared to clinical indices, the PPI has a higher discriminatory power, but traditional planimetry is a time-consuming analysis, as the plaque-covered and clean tooth areas have to be determined manually for each image using image-processing software. Here, we present a method for the semi-automated planimetric quantification of dental plaque, which allows for the rapid processing of up to 1,000 images simultaneously. The method exploits the enhanced contrast between disclosed plaque, sound tooth surfaces, and soft tissues in fluorescence images acquired with an intraoral camera. Careful execution of the clinical procedures and accurate image acquisition are crucial steps for the successful semi-automated identification of the plaque-covered areas. The method is suitable for planimetry on sound facial and oral tooth surfaces, on most composite resin restorations, and on teeth with orthodontic brackets, but not on metallic restorations. Compared to traditional PPI recordings, semi-automated planimetry considerably reduces the amount of time spent on the analysis, as well as the subjective human input, thus increasing the reproducibility of planimetric measurements.

The presented method allows for the rapid, semi-automated planimetric quantification of plaque-covered areas on teeth (Figure 1). Plaque deposits are visualized by erythrosine, while clean tooth areas as well as the acquired pellicle are left unstained16 (Figure 2A). When images are acquired with a fluorescence camera, the contrast between the clean tooth areas, plaque-covered areas, and surrounding soft tissues is enhanced considerably (Figure 2B,C). The fluorescence camera operates with two detection windows, one in the green and one in the red spectrum. Compared to the clean tooth areas, the plaque-covered areas appear slightly brighter in the red channel (Figure 2D,E). In the green channel, the autofluorescence of the tooth is masked considerably in the plaque-covered areas (Figure 2F). This masking effect is exploited during image analysis, when the green channel images are subtracted from the red channel images (Figure 2G). The strong contrast between the clean and plaque-covered areas in the resulting images (Figure 2H) allows for an intensity threshold-based, semi-automated determination of the PPI. Up to 1,000 fluorescence images can be processed simultaneously.
A custom-made 3D-printed spacer may be used to improve the standardized positioning of the camera head at an identical distance from the tooth of interest. The spacer also shields the tooth from ambient light and thereby enhances the contrast between the disclosed plaque, clean tooth areas, and surrounding soft tissues in the acquired images. The spacer is mounted on the camera head with the help of three retention elements (Figure 3).
The described method can be used for planimetric recordings of the supragingival plaque and calculus on both facial and oral tooth surfaces (Figure 4A-D). Depending on the curvature of the dental arch, it can be difficult to position the spacer in close contact with the gums and thereby keep the same distance between the camera head and the tooth. As the plaque area coverage is determined relative to the total tooth area, such differences are unlikely to affect the PPI recordings. Different tooth-colored materials fluoresce in the green spectrum with varying intensities17,18,19. Hence, the PPI can usually be determined with the standard image analysis algorithm on teeth with glass ionomer cements and composite resin restorations (Figure 4E-H). In contrast, amalgam and cast restorations usually emit faintly in both the red and the green channels, and it is, thus, not possible to determine the plaque coverage on such surfaces (Figure 4I,J). The same holds true for metallic orthodontic brackets, but since the bracket surface is typically excluded from PPI recordings, semi-automated planimetry is suitable for orthodontic patients (Figure 4K,L).
The successful semi-automated identification of plaque-covered areas on fluorescence images is highly dependent on the careful execution of all the steps of the clinical procedure. If too much ambient light enters the images, the brightness of the background in the red channel is increased, which renders the differentiation between the teeth and soft tissues difficult (Figure 5A,B). Therefore, the room lights need to be dimmed during the image capture. If the patient does not open the&#160;mouth sufficiently during the image acquisition, antagonist teeth may be imaged along with the tooth of interest and hamper the semi-automated processing (Figure 5C). When planimetry is performed on premolars or molars, the correct angulation of the camera is important to avoid imaging parts of the occlusal surface (Figure 5D,E). Once the plaque deposits are disclosed, the operator should immediately proceed to the image acquisition. Otherwise, the erythrosine may be washed out, and the contrast between the plaque-covered and clean tooth areas may become too faint. In some instances, however, the disclosing solution may strongly stain the gingiva, and the stain may not be removed during the following rinse (Figure 5F). To avoid an overestimation of the plaque-covered area, the stain may be reduced by an additional rinse or by gentle wiping of the gingiva with a cotton pellet.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65035/65035fig01.jpg" /> 
Figure 1: Workflow for the semi-automated quantification of plaque coverage on tooth surfaces. Abbreviation: PPI = planimetric plaque index. <a href="https://www.jove.com/files/ftp_upload/65035/65035fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65035/65035fig02.jpg" /> 
Figure 2: Digital image analysis procedure. (A) White-light image of disclosed plaque (tooth 26, facial aspect). (B) Corresponding image acquired with a fluorescence camera (Red-Green-Blue [RGB] mode). Note the enhanced contrast between the plaque-covered and clean tooth areas. (C)&#160;The total tooth area, marked by the orange outline, is identified by intensity threshold-based segmentation. (D)&#160;The object layer from the RGB image is transferred to the red channel image (orange outline), and the non-object pixels (background, soft tissues) are deleted. (E)&#160;The brightness of the red channel images is enhanced by a factor of two. (F) The green channel image. Note the reduced autofluorescence in the plaque-covered areas. (G)&#160;After subtraction of the green channel image (F) from the modified red channel image (E), the contrast between the plaque-covered areas and clean tooth areas is evident. (H)&#160;After intensity threshold-based segmentation, the plaque-covered areas are identified as objects (orange outline), and the planimetric plaque index (PPI) can be calculated (PPI = 81.6%). <a href="https://www.jove.com/files/ftp_upload/65035/65035fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/65035/65035fig03.jpg" /> 
Figure 3: Custom-made spacer. A custom-made spacer viewed from the (A) front, (B) side, and (C) back. (D) Fluorescence camera with the mounted spacer (orange outline). <a href="https://www.jove.com/files/ftp_upload/65035/65035fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/65035/65035fig04.jpg" /> 
Figure 4: Applications and limitations of semi-automated planimetry. (A)&#160;Fluorescence image of a facial tooth surface. (B) Corresponding processed image showing the plaque-covered areas (orange outline; planimetric plaque index [PPI] = 51.9%). (C) Fluorescence image of an oral tooth surface. (D) Corresponding processed image showing the plaque-covered areas (orange outline; PPI = 14.5%). (E-H) Images of teeth with composite resin restorations. The restoration in E fluoresces strongly in the green spectrum, whereas the restoration in G appears slightly fainter than the surrounding clean tooth areas. In both images, the PPI can be determined using the standard image analysis algorithm. (F,H) Processed images showing the plaque-covered areas (orange outlines; PPI = 20.3% and 20.2%, respectively).&#160;(I,J) Fluorescence images of a tooth with an amalgam restoration (I) and a tooth with a metal-ceramic crown (J, blue outline, added manually). Both restorations are non-fluorescent, and the plaque deposits cannot be quantified by semi-automated planimetry. (K) Fluorescence image of a tooth with a metallic orthodontic bracket. As the bracket is excluded from the analysis, the PPI can be determined using the standard image analysis algorithm (L, orange outline, PPI = 31.5%). <a href="https://www.jove.com/files/ftp_upload/65035/65035fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/65035/65035fig05.jpg" /> 
Figure 5: Influence of the clinical procedures on image quality and the results of semi-automated planimetry. (A)&#160;Fluorescence image acquired with dimmed room lights. The total tooth area is determined correctly after threshold-based image segmentation (orange outline). (B) Fluorescence image of the same tooth acquired with the room lights turned on. Due to an increased background emission in the red spectrum, the threshold-based segmentation fails to differentiate accurately between the tooth surfaces and the surrounding soft tissues (orange outline). (C)&#160;Fluorescence image acquired with insufficient mouth opening. The undisclosed antagonist teeth are visible in the image and, thus, included in the total tooth area (orange outlines). To obtain a correct planimetric plaque index, they have to be removed manually during the image analysis. (D) Fluorescence image acquired with&#160;optimal positioning of the camera head. The total tooth area (orange outline) is limited to the facial aspect. (E)&#160;Fluorescence image of the tooth in D acquired with suboptimal angulation of the camera head. Part of the occlusal surface is captured, resulting in an increased total tooth area (orange outline). (F)&#160;White-light image of disclosed plaque with prominent staining of the gingiva. The high emission in the red spectrum may lead to an overestimation of the plaque-covered area. <a href="https://www.jove.com/files/ftp_upload/65035/65035fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental File S1: <a href="https://www.jove.com/files/ftp_upload/65035/Supplemental file S1.stl" target="_blank">Please click here to download this File.</a>

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Semi-Automated Planimetric Quantification of Dental Plaque Using an Intraoral Fluorescence Camera

This study presents a semi-automated digital image analysis procedure for the planimetric quantification of disclosed dental plaque based on images acquired with an intraoral fluorescence camera. The method allows for the rapid and reliable quantification of dental plaque in the research environment.

Dental plaque accumulation is quantified using clinical indices or, otherwise, the planimetric plaque index (PPI),&#160;which measures the relative area ...

semi-automated-planimetric-quantification-dental-plaque-using-an

Research

JoVE Journal

Medicine

1.9K Views.  Aarhus University. Semi-geautomatiseerde planimetrie maakt een snelle, nauwkeurige en objectieve kwantificering mogelijk van gebieden bedekt met tandplak in klinische onderzoeken. In vergelijking met traditionele planimetrie, waarbij met plaque bedekte gebieden handmatig door de operator worden gedefinieerd, is semi-geautomatiseerde planimetrie objectiever en vermindert het de verwerkingstijd aanzienlijk. Monteer om te beginnen de op maat gemaakte afstandhouder op de fluorescentiecamera, sluit vervolgens de int...

Video: Semi-Automated Planimetric Quantification of Dental Plaque Using an Intraoral Fluorescence Camera

Oral Biofilm Analysis of Palatal Expanders by Fluorescence In-Situ Hybridization and Confocal Laser Scanning Microscopy

Confocal laser scanning microscopy (CLSM) of natural heterogeneous biofilm is today facilitated by a comprehensive range of staining techniques, one of them being fluorescence in situ hybridization (FISH).1,2 We performed a pilot study in which oral biofilm samples collected from fixed orthodontic appliances (palatal expanders) were stained by FISH, the objective being to assess the three-dimensional organization of natural biofilm and plaque accumulation.3,4 FISH creates an opportunity to stain cells in their native biofilm environment by the use of fluorescently labeled 16S rRNA-targeting probes.4-7,19 Compared to alternative techniques like immunofluorescent labeling, this is an inexpensive, precise and straightforward labeling technique to investigate different bacterial groups in mixed biofilm consortia.18,20 General probes were used that bind to Eubacteria (EUB338 + EUB338II + EUB338III; hereafter EUBmix),8-10 Firmicutes (LGC354 A-C; hereafter LGCmix),9,10 and Bacteroidetes (Bac303).11 In addition, specific probes binding to Streptococcus mutans (MUT590)12,13 and Porphyromonas gingivalis (POGI)13,14 were used. The extreme hardness of the surface materials involved (stainless steel and acrylic resin) compelled us to find new ways of preparing the biofilm. As these surface materials could not be readily cut with a cryotome, various sampling methods were explored to obtain intact oral biofilm. The most workable of these approaches is presented in this communication. Small flakes of the biofilm-carrying acrylic resin were scraped off with a sterile scalpel, taking care not to damage the biofilm structure. Forceps were used to collect biofilm from the steel surfaces. Once collected, the samples were fixed and placed directly on polysine coated glass slides. FISH was performed directly on these slides with the probes mentioned above. Various FISH protocols were combined and modified to create a new protocol that was easy to handle.5,10,14,15 Subsequently the samples were analyzed by confocal laser scanning microscopy. Well-known configurations3,4,16,17 could be visualized, including mushroom-style formations and clusters of coccoid bacteria pervaded by channels. In addition, the bacterial composition of these typical biofilm structures were analyzed and 2D and 3D images created.

We present a protocol for structural and compositional analysis of natural oral biofilm from orthodontic appliances with in situ hybridization (FISH) and confocal laser scanning microscopy (CLSM). Oral biofilm samples were collected from palatal expanders, scraping acrylic-resin flakes off their surface and referring them for molecular processing.

Confocal laser scanning microscopy (CLSM) of natural heterogeneous biofilm is today facilitated by a comprehensive range of staining techniques, one of ...

Concurrent Quantification of Cellular and Extracellular Components of Biofilms

Confocal laser scanning microscopy (CLSM) is a powerful tool for investigation of biofilms. Very few investigations have successfully quantified concurrent distribution of more than two components within biofilms because: 1)&#160;selection of fluorescent dyes having minimal spectral overlap is complicated, and 2)&#160;quantification of multiple fluorochromes poses a multifactorial problem. Objectives: Report a methodology to quantify and compare concurrent 3-dimensional distributions of three cellular/extracellular components of biofilms grown on relevant substrates. Methods: The method consists of distinct, interconnected steps involving biofilm growth, staining, CLSM imaging, biofilm structural analysis and visualization, and statistical analysis of structural parameters. Biofilms of Streptococcus mutans (strain UA159) were grown for 48 hr on sterile specimens of Point 4 and TPH3 resin composites. Specimens were subsequently immersed for 60 sec in either Biot&#232;ne PBF (BIO) or Listerine Total Care (LTO) mouthwashes, or water (control group; n=5/group). Biofilms were stained with fluorochromes for extracellular polymeric substances, proteins and nucleic acids before imaging with CLSM. Biofilm structural parameters calculated using ISA3D image analysis software were biovolume and mean biofilm thickness. Mixed models statistical analyses compared structural parameters between mouthwash and control groups (SAS software; &#945;=0.05). Volocity software permitted visualization of 3D distributions of overlaid biofilm components (fluorochromes). Results: Mouthwash BIO produced biofilm structures that differed significantly from the control (p&#60;0.05) on both resin composites, whereas LTO did not produce differences (p&#62;0.05) on either product. Conclusions: This methodology efficiently and successfully quantified and compared concurrent 3D distributions of three major components within S. mutans biofilms on relevant substrates, thus overcoming two challenges to simultaneous assessment of biofilm components. This method can also be used to determine the efficacy of antibacterial/antifouling agents against multiple biofilm components, as shown using mouthwashes. Furthermore, this method has broad application because it facilitates comparison of 3D structures/architecture of biofilms in a variety of disciplines.

A protocol for the concurrent quantification and comparison of three cellular and extracellular components within biofilms is presented. The methodology involves the use of confocal laser scanning microscopy, biofilm structural analysis and visualization software, and statistical analysis software.

Confocal laser scanning microscopy (CLSM) is a powerful tool for investigation of biofilms. Very few investigations have successfully quantified ...

Immunology and Infection

Confocal Time Lapse Imaging as an Efficient Method for the Cytocompatibility Evaluation of Dental Composites

It is generally accepted that in vitro cell material interaction is a useful criterion in the evaluation of dental material biocompatibility. The objective of this study was to use 3D CLSM time lapse confocal imaging to assess the in vitro biocompatibility of dental composites. This method provides an accurate and sensitive indication of viable cell rate in contact with dental composite extracts. The ELS extra low shrinkage, a dental composite used for direct restoration, has been taken as example. In vitro assessment was performed on cultured primary human gingival fibroblast cells using Live/Dead staining. Images were obtained with the FV10i confocal biological inverted system and analyzed with the FV10-ASW 3.1 Software. Image analysis showed a very slight cytotoxicity in the presence of the tested composite after 5 hours of time lapse. A slight decrease of cell viability was shown in contact with the tested composite extracts compared to control cells. The findings highlighted the use of 3D CLSM time lapse imaging as a sensitive method to qualitatively and quantitatively evaluate the biocompatibility behavior of dental composites.

The most studied aspect of the biocompatibility of dental composites is their cytotoxicity 3D CLSM time lapse imaging combined with fluorescent signal quantification allows sensitive evaluation of the cell fate over time. Utilizing this method could be an efficient tool to assess the cytocompatibility of dental composites.

It is generally accepted that in vitro cell material interaction is a useful criterion in the evaluation of dental material biocompatibility. The ...

Ratiometric Imaging of Extracellular pH in Dental Biofilms

The pH in bacterial biofilms on teeth is of central importance for dental caries, a disease with a high worldwide prevalence. Nutrients and metabolites are not distributed evenly in dental biofilms. A complex interplay of sorption to and reaction with organic matter in the biofilm reduces the diffusion paths of solutes and creates steep gradients of reactive molecules, including organic acids, across the biofilm. Quantitative fluorescent microscopic methods, such as fluorescence life time imaging or pH ratiometry, can be employed to visualize pH in different microenvironments of dental biofilms. pH ratiometry exploits a pH-dependent shift in the fluorescent emission of pH-sensitive dyes. Calculation of the emission ratio at two different wavelengths allows determining local pH in microscopic images, irrespective of the concentration of the dye. Contrary to microelectrodes the technique allows monitoring both vertical and horizontal pH gradients in real-time without mechanically disturbing the biofilm. However, care must be taken to differentiate accurately between extra- and intracellular compartments of the biofilm. Here, the ratiometric dye, seminaphthorhodafluor-4F 5-(and-6) carboxylic acid (C-SNARF-4) is employed to monitor extracellular pH in in vivo grown dental biofilms of unknown species composition. Upon exposure to glucose the dye is up-concentrated inside all bacterial cells in the biofilms; it is thus used both as a universal bacterial stain and as a marker of extracellular pH. After confocal microscopic image acquisition, the bacterial biomass is removed from all pictures using digital image analysis software, which permits to exclusively calculate extracellular pH. pH ratiometry with the ratiometric dye is well-suited to study extracellular pH in thin biofilms of up to 75 &#181;m thickness, but is limited to the pH range between 4.5 and 7.0.

A pH-sensitive ratiometric dye is used in combination with confocal laser scanning microscopy and digital image analysis to monitor extracellular pH in dental biofilms in real-time.

The pH in bacterial biofilms on teeth is of central importance for dental caries, a disease with a high worldwide prevalence. Nutrients and metabolites ...

Real-time Imaging and Quantification of Fungal Biofilm Development Using a Two-Phase Recirculating Flow System

In oropharyngeal candidiasis, members of the genus Candida must adhere to and grow on the oral mucosal surface while under the effects of salivary flow. While models for the growth under flow have been developed, many of these systems are expensive, or do not allow imaging while the cells are under flow. We have developed a novel apparatus that allows us to image the growth and development of Candida albicans cells under flow and in real-time. Here, we detail the protocol for the assembly and use of this flow apparatus, as well as the quantification of data that are generated. We are able to quantify the rates that the cells attach to and detach from the slide, as well as to determine a measure of the biomass on the slide over time. This system is both economical and versatile, working with many types of light microscopes, including inexpensive benchtop microscopes, and is capable of extended imaging times compared to other flow systems. Overall, this is a low-throughput system that can provide highly detailed real-time information on the biofilm growth of fungal species under flow.

We describe the assembly, operation, and cleaning of a flow apparatus designed to image fungal biofilm formation in real time while under flow. We also provide and discuss quantitative algorithms to be used on the acquired images.

In oropharyngeal candidiasis, members of the genus Candida must adhere to and grow on the oral mucosal surface while under the effects of salivary flow. ...

Imaging and Quantification of the Area of Fast-Moving Microbubbles Using a High-Speed Camera and Image Analysis

An experimental and image analysis technique is presented for imaging cavitation bubbles and calculating their area. The high-speed imaging experimental technique and image analysis protocol presented here can also be applied for imaging microscopic bubbles in other fields of research; therefore, it has a wide range of applications. We apply this to image cavitation around dental ultrasonic scalers. It is important to image cavitation to characterize it and to understand how it can be exploited for various applications. Cavitation occurring around dental ultrasonic scalers can be used as a novel method of dental plaque removal, which would be more effective and cause less damage than current periodontal therapy techniques. We present a method for imaging the cavitation bubble clouds occurring around dental ultrasonic scaler tips using a high-speed camera and a zoom lens. We also calculate the area of cavitation using machine learning image analysis. Open source software is used for image analysis. The image analysis presented is easy to replicate, does not require programming experience, and can be modified easily to suit the application of the user.

Cavitation microbubbles are imaged using a high-speed camera attached to a zoom lens. The experimental setup is explained, and image analysis is used to calculate the area of the cavitation. Image analysis is done using ImageJ.

An experimental and image analysis technique is presented for imaging cavitation bubbles and calculating their area. The high-speed imaging experimental ...

Bioengineering

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Virtual, hybrid three-dimensional (3D) model acquisition is presented in this article, utilizing the sequence of radiographic image segmentation, spatial registration, and free-form surface modeling. Firstly cone-beam computed tomography datasets were reconstructed with a semi-automatic segmentation method. Alveolar bone and teeth are separated into different segments, allowing 3D morphology, and localization of periodontal intrabony defects to be assessed. The severity, extent, and morphology of acute and chronic alveolar ridge defects are validated concerning adjacent teeth. On virtual complex tissue models, positions of dental implants can be planned in 3D. Utilizing spatial registration of IOS and CBCT data and subsequent free-form surface modeling, realistic 3D hybrid models can be acquired, visualizing alveolar bone, teeth, and soft tissues. With the superimposition of IOS and CBCT soft tissue, thickness above the edentulous ridge can be assessed about the underlying bone dimensions; therefore, flap design and surgical flap management can be determined, and occasional complications may be avoided.

A workflow for creating three-dimensional (3D) virtual hybrid models has been designed based on cone-beam computed tomography dataset and intraoral optical scans utilizing radiographic image segmentation methods and free-form surface modeling. Digital models are used for the virtual planning of reconstructive dentoalveolar surgical procedures.

Virtual, hybrid three-dimensional (3D) model acquisition is presented in this article, utilizing the sequence of radiographic image segmentation, spatial ...

A Standardized Approach to Extra-Oral and Intra-Oral Digital Photography

Contemporary dentistry mandates a more comprehensive and personalized analysis of each patient. Technological advances in digital photography have played vital roles in diagnostic accuracy, treatment planning, execution of therapies, and outcome evaluations, including esthetic enhancement. Digital photography also provides an excellent platform for patient education, communication, and co-management of cases with other healthcare providers. However, intra-oral photography often faces challenges such as inaccessibility of areas to be captured, different moveable vs. fixed tissues involved, contamination with saliva or blood, and differing illumination needs on various locations. Thus, a more standardized and systematic approach is proposed for intra- and extra-oral documentation via digital photography to overcome the existing technical challenges. The current work will outline the appropriate equipment specifications (camera bodies, macro lens, and flashes), positions and postures of the operator and patients, proper techniques of tissue retraction, the use of appropriate intra-oral mirrors, and the essential elements such as aperture settings (F-stop), ISO, shutter speed, and white balance. This article aims to provide all dental professionals with an approachable linear array of guidelines to produce simplified and standardized visual tools for more efficient and effective documentation.

The present protocol describes a method to capture intra- and extra-oral photographs for digital documentation in the clinical practice of dentistry. The protocol highlights all the critical components involved and the commonly utilized settings to capture dental photographs for clinical documentation.

Contemporary dentistry mandates a more comprehensive and personalized analysis of each patient. Technological advances in digital photography have played ...

Precision of In Vivo Quantitative Tooth Wear Measurement Using Intra-Oral Scans

Quantitative wear measurement is of increasing interest for measuring tooth wear progression. However, most research on quantitative wear measurement has focused on simulated wear or scanned gypsum casts. A 3D Wear Analysis (3DWA) protocol has been developed that analyzes tooth wear in vivo through intra-oral scanners available to dental clinicians. This study investigated the precision of the 3DWA protocol for measuring wear through maximum height loss (mm) and volume change (mm3). Observational prospective wear data from 55 patients were analyzed after 0-1-, 0-3-, and 0-5-year intervals to determine rates of wear, and convenience samples were chosen to test the protocol&#39;s precision on dentitions scanned twice in one sitting and its intra- and inter-rater precision on scans with 0-3- and 0-5-year intervals. Scans were made using intra-oral scanners (IOS) and superimposed using 3D measurement software. T-tests were performed to determine the structural and random error, and trimmed ranges were calculated to interpret the error. For protocol precision, the mean difference was 0.015 mm (-0.002; 0.032, p = 0.076) for height and -0.111 mm3 (-0.250; 0.023, p = 0.101) for volume. The duplicate measurement error was 0.062 mm for height and 0.268 mm3 for volume. The height measurements were precise enough to measure wear after intervals of 0-3 or 0-5 years; however, volume measurements were susceptible to procedural error and operator sensitivity. The 3DWA protocol is precise enough to adequately measure tooth height loss after intervals of a minimum of 3 years or in patients with severe wear progression, but it is not suited to measuring volumetric changes.

Precision of In Vivo Quantitative Tooth Wear Measurement Using Intra-Oral Scans

Quantitative wear measurement is a method of increasing importance in measuring tooth wear progression. We here describe a protocol, its precision, and its intra/inter-rater precision for the acquisition and superimposition of repeated in vivo scanned dentitions in patients with moderate to severe wear, reporting on both height and volume measurements.

Quantitative wear measurement is of increasing interest for measuring tooth wear progression. However, most research on quantitative wear measurement has ...

Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities

Pulp canal obliterations (PCO) are often a consequence of dental trauma, such as luxation injuries. Even though dentin apposition is a sign of vital pulp, pulpitis or apical periodontitis may develop in the long term. Root canal treatment of teeth with severe PCO and pulpal or periapical pathosis is challenging for general practitioners and even for well-equipped endodontic specialists. To ensure detection of the calcified root canal and avoid excessive loss of tooth structure or root perforation, static navigation using templates ("Guided Endodontics") was introduced a few years ago. The general workflow includes three-dimensional imaging using cone-beam computed tomography (CBCT), a digital surface scan, and superimposition of both in a planning software. This is followed by virtual planning of the access cavity and the design of a template that will guide the drill to the desired target point. To do this, a true-to-scale virtual image of the drill must be placed in a way that the tip of the drill reaches the orifice of the calcified root canal. Once the template has been fabricated using computer-aided design and computer-aided manufacturing (CAD/CAM) or a 3D printer, guided preparation of the access cavity can be performed clinically. For research purposes, a postoperative CBCT image can be used to quantify the accuracy of the access cavity performed. This work aims to present the technique of static guided endodontics from imaging to clinical implementation.

Guided endodontics describes a template-aided approach for access cavity preparation. The procedure requires cone-beam computed tomography and a surface scan to produce a template. An incorporated sleeve guides the drill to the target point. This allows the preparation of minimally invasive endodontic access cavities in calcified teeth.

Pulp canal obliterations (PCO) are often a consequence of dental trauma, such as luxation injuries. Even though dentin apposition is a sign of vital pulp, ...

Semi-Automated Planimetric Quantification of Dental Plaque Using an Intraoral Fluorescence Camera

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