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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G., 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=Evaluation+of+reliability+and+reproducibility+of+dental+indices.">Evaluation of reliability and reproducibility of dental indices.</a> Journal of Clinical Periodontology. 20 (1), 54-58 (1993).</li><li>Matthijs, S., Sabzevar, M. M., Adriaens, P. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intra-examiner+reproducibility+of+4+dental+plaque+indices:+Dental+plaque+indices.">Intra-examiner reproducibility of 4 dental plaque indices: Dental plaque indices.</a> Journal of Clinical Periodontology. 28 (3), 250-254 (2001).</li><li>Shaloub, A., Addy, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+accuracy+and+variability+of+scoring-area-based+plaque+indices.">Evaluation of accuracy and variability of scoring-area-based plaque indices.</a> Journal of Clinical Periodontology. 27 (1), 16-21 (2000).</li><li>S&#246;der, P. -. &. #. 2. 1. 4. ;., Jin, L. 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B., 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=Computer-based+intraoral+image+analysis+of+the+clinical+plaque+removing+capacity+of+3+manual+toothbrushes.">Computer-based intraoral image analysis of the clinical plaque removing capacity of 3 manual toothbrushes.</a> Journal of Clinical Periodontology. 28 (8), 746-752 (2001).</li><li>Smith, M. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Parametric+vs.+nonparametric.+Analyzing+the+periodontal+and+gingival+indicies.">Parametric vs. nonparametric. Analyzing the periodontal and gingival indicies.</a> Journal of Periodontal Research. 17 (5), 514-517 (1982).</li><li>Quirynen, M., Dekeyser, C., van Steenberghe, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Discriminating+power+of+five+plaque+indices.">Discriminating power of five plaque indices.</a> Journal of Periodontology. 62 (2), 100-105 (1991).</li><li>Al-Anezi, S. A., Harradine, N. W. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantifying+plaque+during+orthodontic+treatment.">Quantifying plaque during orthodontic treatment.</a> The Angle Orthodontist. 82 (4), 748-753 (2012).</li><li>Smith, R. N., Brook, A. H., Elcock, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+quantification+of+dental+plaque+using+an+image+analysis+system:+reliability+and+validation.">The quantification of dental plaque using an image analysis system: reliability and validation.</a> Journal of Clinical Periodontology. 28 (12), 1158-1162 (2001).</li><li>Kang, J., Ji, Z., Gong, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Segmentation+and+quantification+of+dental+plaque+using+modified+kernelized+fuzzy+C-means+clustering+algorithm.">Segmentation and quantification of dental plaque using modified kernelized fuzzy C-means clustering algorithm.</a> 2010 Chinese Control and Decision Conference. , 788-791 (2010).</li><li>Klaus, K., Glanz, T., Glanz, A. G., Ganss, C., Ruf, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+quantitative+light-induced+fluorescence-digital+(QLF-D)+images+and+images+of+disclosed+plaque+for+planimetric+quantification+of+dental+plaque+in+multibracket+appliance+patients.">Comparison of quantitative light-induced fluorescence-digital (QLF-D) images and images of disclosed plaque for planimetric quantification of dental plaque in multibracket appliance patients.</a> Scientific Reports. 10 (1), 4478 (2020).</li><li>Daims, H., L&#252;cker, S., Wagner, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Daime,+a+novel+image+analysis+program+for+microbial+ecology+and+biofilm+research.">Daime, a novel image analysis program for microbial ecology and biofilm research.</a> Environmental Microbiology. 8 (2), 200-213 (2006).</li><li>Arnim, S. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+use+of+disclosing+agents+for+measuring+tooth+cleanliness.">The use of disclosing agents for measuring tooth cleanliness.</a> Journal of Periodontology. 34 (3), 227-245 (1963).</li><li>Meller, C., Klein, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fluorescence+properties+of+commercial+composite+resin+restorative+materials+in+dentistry.">Fluorescence properties of commercial composite resin restorative materials in dentistry.</a> Dental Materials Journal. 31 (6), 916-923 (2012).</li><li>Kiran, R., Chapman, J., Tennant, M., Forrest, A., Walsh, L. 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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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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&#246;d</td>
			<td>Top&#160;Dent&#160;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&#252;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&#160;</td>
			<td>D&#252;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>

Watch this Scientific Journal Video about Semi-Automated Planimetric Quantification of Dental Plaque Using an Intraoral Fluorescence Camera at JoVE.com

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 ...

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1.8K Views.  Aarhus University. 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.

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

In vivo Near Infrared Fluorescence (NIRF) Intravascular Molecular Imaging of Inflammatory Plaque, a Multimodal Approach to Imaging of Atherosclerosis

The vascular response to injury is a well-orchestrated inflammatory response triggered by the accumulation of macrophages within the vessel wall leading to an accumulation of lipid-laden intra-luminal plaque, smooth muscle cell proliferation and progressive narrowing of the vessel lumen. The formation of such vulnerable plaques prone to rupture underlies the majority of cases of acute myocardial infarction. The complex molecular and cellular inflammatory cascade is orchestrated by the recruitment of T lymphocytes and macrophages and their paracrine effects on endothelial and smooth muscle cells.1
Molecular imaging in atherosclerosis has evolved into an important clinical and research tool that allows in vivo visualization of inflammation and other biological processes. Several recent examples demonstrate the ability to detect high-risk plaques in patients, and assess the effects of pharmacotherapeutics in atherosclerosis.4 While a number of molecular imaging approaches (in particular MRI and PET) can image biological aspects of large vessels such as the carotid arteries, scant options exist for imaging of coronary arteries.2 The advent of high-resolution optical imaging strategies, in particular near-infrared fluorescence (NIRF), coupled with activatable fluorescent probes, have enhanced sensitivity and led to the development of new intravascular strategies to improve biological imaging of human coronary atherosclerosis.
Near infrared fluorescence (NIRF) molecular imaging utilizes excitation light with a defined band width (650-900 nm) as a source of photons that, when delivered to an optical contrast agent or fluorescent probe, emits fluorescence in the NIR window that can be detected using an appropriate emission filter and a high sensitivity charge-coupled camera. As opposed to visible light, NIR light penetrates deeply into tissue, is markedly less attenuated by endogenous photon absorbers such as hemoglobin, lipid and water, and enables high target-to-background ratios due to reduced autofluorescence in the NIR window. Imaging within the NIR 'window' can substantially improve the potential for in vivo imaging.2,5
Inflammatory cysteine proteases have been well studied using activatable NIRF probes10, and play important roles in atherogenesis. Via degradation of the extracellular matrix, cysteine proteases contribute importantly to the progression and complications of atherosclerosis8. In particular, the cysteine protease, cathepsin B, is highly expressed and colocalizes with macrophages in experimental murine, rabbit, and human atheromata.3,6,7 In addition, cathepsin B activity in plaques can be sensed in vivo utilizing a previously described 1-D intravascular near-infrared fluorescence technology6, in conjunction with an injectable nanosensor agent that consists of a poly-lysine polymer backbone derivatized with multiple NIR fluorochromes (VM110/Prosense750, ex/em 750/780nm, VisEn Medical, Woburn, MA) that results in strong intramolecular quenching at baseline.10 Following targeted enzymatic cleavage by cysteine proteases such as cathepsin B (known to colocalize with plaque macrophages), the fluorochromes separate, resulting in substantial amplification of the NIRF signal. Intravascular detection of NIR fluorescence signal by the utilized novel 2D intravascular NIRF catheter now enables high-resolution, geometrically accurate in vivo detection of cathepsin B activity in inflamed plaque.
In vivo molecular imaging of atherosclerosis using catheter-based 2D NIRF imaging, as opposed to a prior 1-D spectroscopic approach,6 is a novel and promising tool that utilizes augmented protease activity in macrophage-rich plaque to detect vascular inflammation.11,12 The following research protocol describes the use of an intravascular 2-dimensional NIRF catheter to image and characterize plaque structure utilizing key aspects of plaque biology. It is a translatable platform that when integrated with existing clinical imaging technologies including angiography and intravascular ultrasound (IVUS), offers a unique and novel integrated multimodal molecular imaging technique that distinguishes inflammatory atheromata, and allows detection of intravascular NIRF signals in human-sized coronary arteries.

In vivo Near Infrared Fluorescence NIRF Intravascular Molecular Imaging of Inflammatory Plaque, a Multimodal Approach to Imaging of Atherosclerosis

We detail a new near-infrared fluorescence (NIRF) catheter for 2-dimensional intravascular molecular imaging of plaque biology in vivo. The NIRF catheter can visualize key biological processes such as inflammation by reporting on the presence of plaque-avid activatable and targeted NIR fluorochromes. The catheter utilizes clinical engineering and power requirements and is targeted for application in human coronary arteries. The following research study describes a multimodal imaging strategy that utilizes a novel in vivo intravascular NIRF catheter to image and quantify inflammatory plaque in proteolytically active inflamed rabbit atheromata.

The vascular response to injury is a well-orchestrated inflammatory response triggered by the accumulation of macrophages within the vessel wall leading ...

Quantification of Atherosclerotic Plaque Activity and Vascular Inflammation using [18-F] Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (FDG-PET/CT)

Conventional non-invasive imaging modalities of atherosclerosis such as coronary artery calcium (CAC)1 and carotid intimal medial thickness (C-IMT)2 provide information about the burden of disease. However, despite multiple validation studies of CAC3-5, and C-IMT2,6, these modalities do not accurately assess plaque characteristics7,8, and the composition and inflammatory state of the plaque determine its stability and, therefore, the risk of clinical events9-13.
[18F]-2-fluoro-2-deoxy-D-glucose (FDG) imaging using positron-emission tomography (PET)/computed tomography (CT) has been extensively studied in oncologic metabolism14,15. Studies using animal models and immunohistochemistry in humans show that FDG-PET/CT is exquisitely sensitive for detecting macrophage activity16, an important source of cellular inflammation in vessel walls. More recently, we17,18 and others have shown that FDG-PET/CT enables highly precise, novel measurements of inflammatory activity of activity of atherosclerotic plaques in large and medium-sized arteries9,16,19,20. FDG-PET/CT studies have many advantages over other imaging modalities: 1) high contrast resolution; 2) quantification of plaque volume and metabolic activity allowing for multi-modal atherosclerotic plaque quantification; 3) dynamic, real-time, in vivo imaging; 4) minimal operator dependence. Finally, vascular inflammation detected by FDG-PET/CT has been shown to predict cardiovascular (CV) events independent of traditional risk factors21,22 and is also highly associated with overall burden of atherosclerosis23. Plaque activity by FDG-PET/CT is modulated by known beneficial CV interventions such as short term (12 week) statin therapy24 as well as longer term therapeutic lifestyle changes (16 months)25.
The current methodology for quantification of FDG uptake in atherosclerotic plaque involves measurement of the standardized uptake value (SUV) of an artery of interest and of the venous blood pool in order to calculate a target to background ratio (TBR), which is calculated by dividing the arterial SUV by the venous blood pool SUV. This method has shown to represent a stable, reproducible phenotype over time, has a high sensitivity for detection of vascular inflammation, and also has high inter-and intra-reader reliability26. Here we present our methodology for patient preparation, image acquisition, and quantification of atherosclerotic plaque activity and vascular inflammation using SUV, TBR, and a global parameter called the metabolic volumetric product (MVP). These approaches may be applied to assess vascular inflammation in various study samples of interest in a consistent fashion as we have shown in several prior publications.9,20,27,28

Quantification of Atherosclerotic Plaque Activity and Vascular Inflammation using [18-F] Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography FDG-PET/CT

There is great need to identify atherosclerosis non-invasively, and here we demonstrate how FDG-PET/CT can be used to detect and quantify atherosclerotic plaque activity and vascular inflammation.

Conventional non-invasive imaging modalities of atherosclerosis such as coronary artery calcium (CAC)1 and carotid intimal medial thickness (C-IMT)2 ...

Using Quantitative Real-time PCR to Determine Donor Cell Engraftment in a Competitive Murine Bone Marrow Transplantation Model

Murine bone marrow transplantation models provide an important tool in measuring hematopoietic stem cell (HSC) functions and determining genes/molecules that regulate HSCs. In these transplant model systems, the function of HSCs is determined by the ability of these cells to engraft and reconstitute lethally irradiated recipient mice. Commonly, the donor cell contribution/engraftment is measured by antibodies to donor- specific cell surface proteins using flow cytometry. However, this method heavily depends on the specificity and the ability of the cell surface marker to differentiate donor-derived cells from recipient-originated cells, which may not be available for all mouse strains. Considering the various backgrounds of genetically modified mouse strains in the market, this cell surface/ flow cytometry-based method has significant limitations especially in mouse strains that lack well-defined surface markers to separate donor cells from congenic recipient cells. Here, we reported a PCR-based technique to determine donor cell engraftment/contribution in transplant recipient mice. We transplanted male donor bone marrow HSCs to lethally irradiated congenic female mice. Peripheral blood samples were collected at different time points post transplantation. Bone marrow samples were obtained at the end of the experiments. Genomic DNA was isolated and the Y chromosome specific gene, Zfy1, was amplified using quantitative Real time PCR. The engraftment of male donor-derived cells in the female recipient mice was calculated against standard curve with known percentage of male vs. female DNAs. Bcl2 was used as a reference gene to normalize the total DNA amount. Our data suggested that this approach reliably determines donor cell engraftment and provides a useful, yet simple method in measuring hematopoietic cell reconstitution in murine bone marrow transplantation models. Our method can be routinely performed in most laboratories because no costly equipment such as flow cytometry is required.

Determining donor cell engraftment presents a challenge in mouse bone marrow transplant models that lack well-defined phenotypical markers. We described a methodology to quantify male donor cell engraftment in female transplant recipient mice. This method can be used in all mouse strains for the study of HSC functions.

Murine bone marrow transplantation models provide an important tool in measuring hematopoietic stem cell (HSC) functions and determining genes/molecules ...

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 ...

Fluorescence-mediated Tomography for the Detection and Quantification of Macrophage-related Murine Intestinal Inflammation

Murine models of disease are indispensable to scientific research. However, many diagnostic tools such as endoscopy or tomographic imaging are not routinely employed in animal models. Conventional experimental readouts often rely on post mortem and ex vivo analyses, which prevent intra-individual follow-up examinations and increase the number of study animals needed. Fluorescence-mediated tomography enables the non-invasive, repetitive, quantitative, three-dimensional assessment of fluorescent probes. It is highly sensitive and permits the use of molecular makers, which allows for the specific detection and characterization of distinct molecular targets. In particular, targeted probes represent an innovative tool for analyzing gene activation and protein expression in inflammation, autoimmune disease, infection, vascular disease, cell migration, tumorigenesis, etc. In this article, we provide step-by-step instructions on this sophisticated imaging technology for the in vivo detection and characterization of inflammation (i.e., F4/80-positive macrophage infiltration) in a widely used murine model of intestinal inflammation. This technique might also be used in other research areas, such as immune cell or stem cell tracking.

Target-specific probes represent an innovative tool for analyzing molecular mechanisms, such as protein expression in various types of disease (e.g., inflammation, infection, and tumorigenesis). In this study, we describe a quantitative three-dimensional tomographic assessment of intestinal macrophage infiltration in a murine model of colitis using F4/80-specific fluorescence-mediated tomography.

Murine models of disease are indispensable to scientific research. However, many diagnostic tools such as endoscopy or tomographic imaging are not ...

Semi-automated Analysis of Mouse Skeletal Muscle Morphology and Fiber-type Composition

For years, distinctions between skeletal muscle fiber types were best visualized by myosin-ATPase staining. More recently, immunohistochemical staining of myosin heavy chain (MyHC) isoforms has emerged as a finer discriminator of fiber-type. Type I, type IIA, type IIX and type IIB fibers can now be identified with precision based on their MyHC profile; however, manual analysis of these data can be slow and down-right tedious. In this regard, rapid, accurate assessment of fiber-type composition and morphology is a very desirable tool. Here, we present a protocol for state-of-the-art immunohistochemical staining of MyHCs in frozen sections obtained from mouse hindlimb muscle in concert with a novel semi-automated algorithm that accelerates analysis of fiber-type and fiber morphology. As expected, the soleus muscle displayed staining for type I and type IIA fibers, but not for type IIX or type IIB fibers. On the other hand, the tibialis anterior muscle was composed predominantly of type IIX and type IIB fibers, a small fraction of type IIA fibers and little or no type I fibers. Several image transformations were used to generate probability maps for the purpose of measuring different aspects of fiber morphology (i.e., cross-sectional area (CSA), maximal and minimal Feret diameter). The values obtained for these parameters were then compared with manually-obtained values. No significant differences were observed between either mode of analysis with regards to CSA, maximal or minimal Feret diameter (all p &#62; 0.05), indicating the accuracy of our method. Thus, our immunostaining analysis protocol may be applied to the investigation of effects on muscle composition in many models of aging and myopathy.

Immunohistochemical staining of myosin heavy chain isoforms has emerged as the state-of-the-art discriminator of skeletal muscle fiber-type (i.e., type I, type IIA, type IIX, type IIB). Here, we present a staining protocol along with a novel semi-automated algorithm that facilitates rapid assessment of fiber-type and fiber morphology.

For years, distinctions between skeletal muscle fiber types were best visualized by myosin-ATPase staining. More recently, immunohistochemical staining of ...

Controlled Semi-Automated Laser-Induced Injuries for Studying Spinal Cord Regeneration in Zebrafish Larvae

Zebrafish larvae possess a fully functional central nervous system (CNS) with a high regenerative capacity only a few days after fertilization. This makes this animal model very useful for studying spinal cord injury and regeneration. The standard protocol for inducing such lesions is to transect the dorsal part of the trunk manually. However, this technique requires extensive training and damages additional tissues. A protocol was developed for laser-induced lesions to circumvent these limitations, allowing for high reproducibility and completeness of spinal cord transection over many animals and between different sessions, even for an untrained operator. Furthermore, tissue damage is mainly limited to the spinal cord itself, reducing confounding effects from injuring different tissues, e.g., skin, muscle, and CNS. Moreover, hemi-lesions of the spinal cord are possible. Improved preservation of tissue integrity after laser injury facilitates further dissections needed for additional analyses, such as electrophysiology. Hence, this method offers precise control of the injury extent that is unachievable manually. This allows for new experimental paradigms in this powerful model in the future.

The present protocol describes a method to induce tissue-specific and highly reproducible injuries in zebrafish larvae using a laser lesion system combined with an automated microfluidic platform for larvae handling.

Zebrafish larvae possess a fully functional central nervous system (CNS) with a high regenerative capacity only a few days after fertilization. This makes ...

Robotic Left Hepatectomy using Indocyanine Green Fluorescence Imaging for an Intrahepatic Complex Biliary Cyst

Biliary cysts (BC) are rare congenital dilatations of intra- and extrahepatic parts of the biliary tract and bear a significant risk of carcinogenesis. Surgery is the cornerstone treatment for patients with BC. While total BC excision and Roux-Y hepaticojejunostomy is the treatment method of the choice in patients with extrahepatic BC (i.e., Todani I-IV), patients with intrahepatic BC (i.e., Todani V) benefit the most from a surgical liver resection. In recent years, minimally invasive liver surgery (MILS) including robotic MILS has gained more acceptance as a feasible, safe, and effective procedure for the treatment of both benign and malignant indications. Robotic major MILS is still considered technically demanding and a detailed description of the technical approach during robotic major MILS has only been limitedly discussed in the literature. The current article describes the main steps for a robotic left hepatectomy in a patient with a large BC Todani Type V. The patient is in French position with 5 trocars placed (4 robotic, 1 laparoscopic assistant). After mobilizing the left hemiliver, the left and right hepatic artery are dissected carefully followed by a cholecystectomy. Intraoperative ultrasound is performed to confirm localization and margins of the BC. The Left hepatic artery and left portal vein are isolated, clipped, and divided. Indocyanine green (ICG) fluorescence imaging is used regularly during the entire procedure to visualize and confirm biliary tract anatomy and the BC. Parenchymal transection is performed with robotic cautery hook for the superficial part and robotic cautery spatula, bipolar cautery, and vessel sealer for the deeper parenchyma. The postoperative course was uncomplicated. A robotic left hepatectomy is technically demanding, yet a feasible and safe procedure. ICG-fluorescence imaging aids in delineating the BC and bile duct anatomy. Further, comparative studies are needed to confirm clinical benefits of robotic MILS for benign and malignant indications.

Robotic liver surgery has gained more acceptance as a feasible, safe, and effective procedure for the treatment of both benign and malignant indications. However, robotic left hepatectomy is still technically demanding. We describe our surgical technique of a robotic left hepatectomy using indocyanine green fluorescence imaging for a large biliary cyst.

Biliary cysts (BC) are rare congenital dilatations of intra- and extrahepatic parts of the biliary tract and bear a significant risk of carcinogenesis. ...

Dynamic Navigation for Dental Implant Placement

In modern implantology, the application of surgical navigation systems is becoming increasingly important. In addition to static surgical navigation methods, a guide-independent dynamic navigation implant placement procedure is becoming more widespread. The procedure is based on computer-guided dental implant placement utilizing optical control. This work aims to demonstrate the technical steps of a new dynamic computer-aided implant surgery (DCAIS)&#160;system (design, calibration, surgery) and check the accuracy of the results. Based on cone-beam computed tomography (CBCT) scans, the exact positions of implants are determined with dedicated software. The first step of the operation is the calibration of the navigation system, which can be performed in two ways: 1) based on CBCT images taken with a marker or 2) based on CBCT images without markers. Implants are inserted with the aid of real-time navigation according to the preoperative plans. The accuracy of the interventions can be evaluated based on postoperative CBCT images. The preoperative images containing the planned positions of the implants and postoperative CBCT images were compared based on the angulation (degree), platform, and apical deviation (mm) of the implants. To evaluate the data, we calculated the standard deviation (SD), mean, and standard error of the mean (SEM) of deviations within planned and performed implant positions. Differences between the two calibration methods were compared based on this data. Based on the interventions performed so far, the use of DCAIS allows for high-precision implant placement. A calibration system that does not require labeled CBCT recording allows for surgical intervention with similar accuracy as a system that uses labeling. The accuracy of the intervention can be improved by training.

Dynamic computer-aided implant surgery (DCAIS) is a controlled implant surgical placement method performed without a surgical template using optical control. The real-time intraoperative control of movement and position of the surgical device simplifies the procedure and gives more freedom to the surgeon, providing similar precision as static navigation methods.

In modern implantology, the application of surgical navigation systems is becoming increasingly important. In addition to static surgical navigation ...

Detection and Removal of Tooth-Colored Composite Resin Using the Fluorescence-Aided Identification Technique

The detection and removal of tooth-colored filling materials is a major challenge for every dentist. The Fluorescence-aided Identification Technique (FIT) is a noninvasive tool to facilitate the distinction of composite resin material from sound tooth substance. Compared to conventional illumination, FIT is a very accurate, reliable, and fast diagnostic method. When composite resin is illuminated with a wavelength of approximately 398 &plusmn; 5 nm, certain fluorescent components make the composite resin appear brighter than the tooth structure. Any fluorescence-inducing light source with the appropriate wavelength can be used for this method. Optimally, this technique is used without additional natural or artificial lighting. The application of FIT can be used for diagnostic purposes, for example, dental charts, and additionally for the complete and minimally invasive removal of composite resin restorations, bracket debonding, and trauma splint removal. The assessment of volumetric changes after composite removal can be provided by overlapping pre- and postoperative scans and subsequent calculation using suitable software.

The Fluorescence-aided Identification Technique is a practicable, fast, and reliable approach for the differentiation of composite resin restorations from tooth substance, and facilitates the minimally invasive and complete removal of composite resin restorations and composite bonded trauma splints.

The detection and removal of tooth-colored filling materials is a major challenge for every dentist. The Fluorescence-aided Identification Technique (FIT) ...