This study was conducted in complete accordance with the Declaration of Helsinki. Before manuscript preparation, written informed consent was provided and signed by the patient. The patient granted permission for data usage for the demonstration of the protocol.
1. Radiographic image processing
<ol>
	<li>Load DICOM files into the software
	<ol>
		<li>Download the newest version of the medical imaging software and open it. 
		NOTE: After opening the software, the home screen will appear.</li>
		<li>Click Load DICOM Data on the sidebar. 
		NOTE: The DICOM database will pop up, showing the previously loaded DICOM datasets.
		<ol>
			<li>Click Import DICOM files in the DICOM database, select the DICOM dataset in the destination folder and click Import. 
			NOTE: The newly added DICOM dataset will appear in the list of studies.</li>
		</ol>
		</li>
		<li>Select the study and click Load at the bottom of the window. 
		NOTE: The DICOM dataset will be opened, and four views (coronal, axial, sagittal, and 3D) of the loaded data will be visible. Nodes are listed on the left-hand side. Theoretically, the described method can be performed on any CT or CBCT regardless of image quality (voxel size, artifacts). However, the segmentation process of higher-quality CBCT/CT scans is more straightforward, and higher-quality 3D models can be acquired. A shown CBCT scan was taken with the following parameters: voxel size: 150 &#181;m, anode voltage: 84 kV, tube current: 40 mA, Field-of-view: 8 x 5 cm. The process can be stopped at any stage; make sure to save the scene before exiting. To save, click the save icon on the left-hand side of the toolbar and save it as a &#34;medical record bundle&#34; (.mrb) by clicking on the box icon in the &#34;save scene&#34; window.</li>
	</ol>
	</li>
	<li>Volume rendering and cropping volume
	<ol>
		<li>Crop the area of interest (upper or lower jaw) to reduce the file size and rendering time. Click the Modules bar visible on the left-hand side of the toolbar to view a scroll-down window showing frequently used modules.</li>
		<li>Select the Volume Rendering module from the dropdown window. To make volume rendering visible, click the eye icon next to the &#34;Volumes&#34; bar.</li>
		<li>Select the desired preset to view the volume render and move the &#34;Shift&#34; slider until the hard tissues can be viewed clearly. 
		NOTE: For CBCT scans, the CT-Bone preset is recommended.</li>
		<li>Check the box next to &#34;Enable&#34; and click the eye icon next to &#34;Display ROI&#34; in the &#34;Crop&#34; section to make the ROI (Region of interest) visible. 
		NOTE: A wireframe box around the dataset in all 2D views and the 3D view will appear. By dragging the sides of the box, the volume will be cropped to the desired area.</li>
		<li>Access the &#34;Crop Volume&#34; module to finalize cropping. Select the original dataset as the input volume. 
		NOTE: Input ROI is automatically set to ROI that was previously created.</li>
		<li>Select Create new volume from the &#34;Output volume&#34; dropdown bar to create a new output volume. Uncheck Interpolated cropping in the advanced settings section and click Apply. 
		NOTE: When returning to the &#34;Data module,&#34; the new cropped volume will appear as a new node.</li>
	</ol>
	</li>
	<li>Segmentation of CBCT dataset
	<ol>
		<li>Access the Segment Editor module for segmentation. 
		NOTE: Segmentation is when 3D reconstructions of anatomical structures are generated based on the CBCT dataset to allow more accessible analysis.</li>
		<li>Select the previously created cropped volume as the Master Volume of the active segmentation. Click +Add to add, and -Remove to remove segments. Rename them according to the anatomical structure they will represent. 
		NOTE: Alveolar bone and all teeth will be separate segments within the segmentation</li>
		<li>Start with the segmentation of the alveolar bone. From the list of effects, select Level Tracing, a semi-automatic tool that outlines the region where pixels have the same background value as the selected pixel.</li>
		<li>Drag the mouse to the perimeter of the bone on one of the 2D views for a yellow line to appear around the selected area and press the left mouse button to generate the segment on the selected slice of the dataset. 
		NOTE: Segmentation can be done in any of the 2D views; however, sagittal and axial orientations work the best.</li>
		<li>Use the Paint and Erase hand tools to modify the segment and correct mistakes if the &#34;Level Tracing&#34; tool did not outline the entire section of the bone or if artifacts present on the slice were also included.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62743/62743fig01.jpg" /> 
Figure 1: Application of &#34;Level Tracing&#34; semi-automatic segmentation tool in sagittal orientation. (A) Outlining the region of pixels with the same background value with a yellow line. (B) Results of &#34;Level Tracing&#34; and subsequent manual segmentation. (C) Refinement of semi-automatic segmentation with the help of manual tools (paint, erase). <a href="https://www.jove.com/files/ftp_upload/62743/62743fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
NOTE: Using number keys to allow fast switching between tools.
<ol start="6" style="margin-left: 40px;">
	<li>Exclude both teeth and implants from the bone segment. Outline teeth and implants using the Erase tool and delete all highlighted pixels representing them.</li>
	<li>Repeat the same process on every 5th slice of the dataset in the selected orientation. 
	NOTE: Click Show 3D to view the segmentation in three dimensions. Set the smoothening factor slider to 0.00.</li>
	<li>Compute the missing segments upon completion of this phase-select Fill between slices from the effects list. 
	NOTE: This tool calculates the missing segments based on those created previously using a morphological contour interpolation algorithm.</li>
	<li>Click Initialize to activate contour interpolation, and if the results are satisfactory, click Apply. Scroll through the dataset upon completion to check and correct occasional mistakes.</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/62743/62743fig02.jpg" /> 
Figure 2: Morphological contour interpolation with &#34;Fill Between Slices,&#34; light green areas indicating the automatically reconstructed part of the segment. (A) Axial view. (B) Sagittal view. (C) Coronal view. <a href="https://www.jove.com/files/ftp_upload/62743/62743fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
NOTE: Make sure that only the segment is visible on which the interpolation is applied. The visibility of the segments can be toggled in the segments list.
<ol start="10" style="margin-left: 40px;">
	<li>Make segment boundaries smoother by removing protrusions using the Smoothing effect. Select Median as the smoothing method and set the &#34;Kernel size&#34; to 5 x 5 x 5 pixels by adjusting the mm value in the bracket and clicking Apply.</li>
	<li>Repeat the same steps for the segmentation of teeth once the segmentation of alveolar bone is completed.</li>
</ol>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/62743/62743fig03.jpg" /> 
Figure 3: Finished segmentation, the brown segment representing bone and the blue segment representing teeth. (A) Axial view. (B) Sagittal view. (C) Coronal view. (D) The&#160;3D model is generated automatically from the segments created previously. <a href="https://www.jove.com/files/ftp_upload/62743/62743fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="12" style="margin-left: 40px;">
	<li>Set the &#34;Modify other segments&#34; bar to Allow overlap before tooth segmentation so that the newly created segments will not overwrite the previously created ones.</li>
</ol>
<ol start="4">
	<li>Spatial registration of CBCT dataset and IOS 
	NOTE: Spatial registration is necessary because the coordinate systems for the CBCT dataset and the IOS are different.
	<ol>
		<li>Select Extension Manager From the &#34;View&#34; menu bar and click Install Extensions. Type IGT into the search bar in the right-hand corner, install the SlicerIGT extension and restart the program.</li>
		<li>Load the previously saved .mrb file of the scene by clicking the Data icon and Choose file(s) to add.</li>
		<li>Import the .stl file of IOS by clicking the Data icon in the upper left corner. In the &#34;Add data into the scene&#34; pop-up window, click Choose file(s) to add, go to the destination folder, select the .stl file of the IOS, and click Open.</li>
		<li>Add the .stl file of the intraoral scan as segmentation by selecting Segmentation from the dropdown bar. 
		NOTE: The installed &#34;IGT&#34; module will now appear in the &#34;Modules&#34; dropdown menu.</li>
		<li>Move the cursor over the module, and in the sidebar that appears, select Fiducial Registration Wizard.</li>
		<li>Select Create new Markups Fiducial from the dropdown bar in both the &#34;From fiducials&#34; and the &#34;To fiducials&#34; sections. 
		NOTE: The software will automatically name the two lists &#34;From&#34; and &#34;To.&#34; The &#34;From&#34; list represents the moving volume, which in this case will be the IOS. The &#34;To&#34; list represents the fixed volume, which will be the CBCT dataset.</li>
		<li>Place marker points on well-defined anatomical landmarks on the IOS using the &#34;Place a markup point&#34; icon next to the dropdown bar in the &#34;From&#34; section. Markup points will be numbered in order of placement. 
		NOTE: Place at least 6 points on the cusps and incisal edges of teeth.</li>
		<li>Place markers in the same position to create the &#34;To&#34; list and in the same order on the CBCT dataset. Markup points with the same number must represent the same anatomical landmark.</li>
		<li>Create a transformation by selecting Create new LinearTransform from the dropdown menu in the &#34;Registration result transform&#34; section of the sidebar after the two lists are ready.</li>
		<li>Access the &#34;Transforms&#34; module and select the previously created transformation as the Active transform. In the &#34;Apply transform&#34; section, move the IOS segmentation and the &#34;From&#34; markups list from the &#34;Transformable&#34; box to the &#34;Transformed&#34; box to superimpose the IOS over the CBCT dataset.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/62743/62743fig04.jpg" /> 
Figure 4: Spatial registration of IOS by placing fiducial markers on well-defined anatomical landmarks. <a href="https://www.jove.com/files/ftp_upload/62743/62743fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
NOTE: If necessary, the accuracy of the transformation can be improved by moving the markup points or by adding additional points.
2. Export models as .stl files for free-form surface modeling
<ol>
	<li>Export the matched hard- and soft-tissue models for further surface modeling after segmentation and spatial registration.</li>
	<li>Go to the Segmentations module and select the segmentation with the alveolar bone and teeth models as the active segmentation. Scroll down to the &#34;Export to files&#34; section, choose the destination folder, and select STL as the file format.</li>
	<li>Uncheck the Merge into single file box, set the Coordinate system to RAS, and click Export.</li>
	<li>Repeat the same process for the IOS, visible as a separate segmentation, save the scene, and close the imaging software.</li>
</ol>
3. Free-form surface modeling
<ol>
	<li>Surface smoothing
	<ol>
		<li>Open the CAD software, and on the home screen, click Import. Select the .stl models that were previously exported from the DICOM image processing software. 
		NOTE: Even though smoothing was performed previously, the surface of the models reconstructed from the CBCT dataset will still appear pixelated, so further surface smoothing is necessary.</li>
		<li>Go to Sculpt in the menu bar, and from the brush inventory, select Adaptive reduce. 
		NOTE: Brush size and strength must be adjusted, depending on the amount of smoothing.</li>
	</ol>
	</li>
	<li>Separate crown of the teeth from the IOS 
	NOTE: Crowns of teeth are depicted more accurately on the IOS than on segmented models; therefore, crowns of the segmented teeth models must be replaced with crowns from the IOS.
	<ol>
		<li>Click Select in the sidebar and select Brush as the selection tool. Use Unwrap brush brush mode and adjust the size of the brush. Using the brush, select the crown of each tooth until the marginal gingiva on the IOS. 
		NOTE: Selected surfaces are indicated with an orange color.</li>
		<li>Move the cursor to Modify in the sidebar and select Smooth Boundary. Click Apply if the results are satisfactory. 
		NOTE: Now, the boundary of the selection precisely follows the marginal gingiva.</li>
		<li>Go to Edit in the Select sidebar and click Separate to create an individual object from the selected area.</li>
		<li>Repeat the same process for all teeth.</li>
		<li>Go to Analysis in the menu bar and select Inspect. 
		NOTE: The program will indicate errors in the models. Holes are marked with a blue color.</li>
		<li>Select Flat fill as the &#34;Hole fill mode&#34; and click Auto repair all to create closed models from the IOS model and the separated teeth models.Go to Sculpt and smooth the edges of the filled hole using Shrinksmooth brush.</li>
		<li>Repeat the process for all tooth crowns and the rest of the IOS.</li>
	</ol>
	</li>
	<li>Merge crowns of the teeth with the segmented tooth models. 
	NOTE: If spatial registration was correctly done, the positions of the crowns of teeth on the IOS and the crowns of segmented teeth should match.
	<ol>
		<li>Use the Shrinksmooth brush on the segmented tooth model until they are completely cover by the tooth crowns separated from the IOS. 
		NOTE: Due to imperfections in both the segmentation and the IOS, the crowns don&#39;t always overlap completely.</li>
		<li>Select both the separated crown and the segmented model of the same tooth in the Object Browser. In the sidebar that appears, select Boolean union, and click Accept. 
		NOTE: Now, the crown of the segmented tooth model is replaced by the crown separated from the IOS.</li>
		<li>Use Shrinksmooth to smooth the transition.</li>
	</ol>
	</li>
	<li>Subtractions and model composition
	<ol>
		<li>Subtract the bone model from the soft-tissue model to represent the clinical situation realistically. 
		NOTE: The original IOS without teeth became the model of the soft tissues.</li>
		<li>Select both bone and soft-tissue models in the Object Browser and select Boolean Difference.</li>
		<li>Smooth transitions with the Shrinksmooth brush and remove protrusions from the bottom side of the soft-tissue model.</li>
		<li>Subtract teeth from the soft-tissue model using the same process and smooth transitions.</li>
	</ol>
	</li>
	<li>Color models
	<ol>
		<li>Color the surfaces of the models to give a more realistic look since the model is complete now with the teeth, soft tissues, and alveolar bone being separated from one another, representing the clinical situation in 3D.</li>
		<li>Select Sculpt from the sidebar and switch the little slider from Volume to Surface.</li>
		<li>Select PaintVertex from the brush inventory and select the desired color using the color wheel in the Color section of the sidebar. Color the surface of each model (e.g., bone: brown, soft tissue: pink, teeth: white)</li>
	</ol>
	</li>
</ol>
Animated Figure 1: Animation of the final, colored model, ready for virtual surgical planning. <a href="https://www.jove.com/files/ftp_upload/62743/Animated Figure1.mp4" target="_blank">Please click here to download this Figure.</a>

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The authors declare no conflict of interest.

With the presented protocol, periodontal and alveolar defect morphologies can be visualized in three dimensions (3D), providing a more accurate depiction of the clinical situation than can be achieved by 2D diagnostic methods and 3D models generated with thresholding algorithms. The protocol can be divided into three major phases: (1) semi-automatic segmentation of CBCT datasets, (2) spatial registration of CBCT and IOS, and (3) free-form surface modeling. Technically, segmentation can be performed on any three-dimension...

Technological advancements in dentistry have enabled computer-aided treatment planning and simulation of surgical procedures and prosthetic rehabilitation. Two essential methods for 3D data acquisition in digital dentistry are: (1) cone-beam computed tomography (CBCT)1 and (2) intraoral optical scanning (IOS)2. Digital information of all relevant anatomical structures (alveolar bone, teeth, soft tissues) can be acquired using these tools to plan reconstructive dentoalveolar surgical procedures.
Cone-beam technology was first introduced in 1996 by an Italian research group. Delivering significantly lower radiation dose and higher resolution (compared to conventional computed tomography), CBCT has quickly become the most frequently used 3D imaging modality in dentistry and oral surgery3. CBCT is often used to plan different surgical procedures (e.g., periodontal regenerative surgery, alveolar ridge augmentation, dental implant placement, orthognathic surgery)1. CBCT datasets are viewed and can be processed in radiographic imaging software that provides 2D images, and 3D renders-however, most imaging software use threshold-based algorithms for 3D image reconstruction. Thresholding methods set the upper and lower bounds of a voxel grey value interval. Voxels that fall in between these bounds will be rendered in 3D. This method allows speedy model acquisition; however, since the algorithm cannot differentiate anatomical structures from metal artifacts and scattering, the 3D renders are highly inaccurate and have very little diagnostic value4,5. For the reasons mentioned above, many fields within dentistry still rely on conventional 2D radiographs (intraoral radiographs, panoramic X-ray) or the 2D images of CBCT datasets5. Our research group presented a semi-automatic image segmentation method in a recently published article, using open-source radiographic image processing software6 wherein anatomically based 3D reconstruction of CBCT datasets is performed7. With the help of this method, anatomical structures were differentiated from metal artifacts, and, more importantly, alveolar bone and teeth could be separated. Therefore, a realistic virtual model of hard tissues could be acquired. 3D models were used to evaluate intrabony periodontal defects and for treatment planning before regenerative periodontal surgeries.
Intraoral optical surface scanners provide digital information on clinical conditions (clinical crown of the teeth and soft tissues). The original intended purpose of these devices was to directly acquire digital models of patients for the planning and fabrication of dental prostheses with computer-aided design (CAD) and computer-aided manufacturing (CAM) technologies8. However, due to the wide range of applications, their use was quickly implemented in other fields of dentistry. Maxillo-facial surgeons combine IOS and CBCT into a hybrid setup that can be utilized for virtual osteotomy and digital planning of orthognathic surgeries9,10. Dental implantology is probably the field that uses digital planning and guided execution most commonly. Navigated surgery eliminates most complications related to implant mispositioning. The combination of CBCT datasets and stereolithography (.stl) files of IOS is routinely used to plan the guided implant placement and the fabrication of static implant drilling guides11,12. Intraoral scans superimposed over CBCT datasets have also been used to prepare esthetic crown lengthening13; however, soft tissues were superimposed only over CBCT datasets reconstructed with thresholding algorithms. Yet, to perform accurate 3D virtual planning of regenerative-reconstructive surgical interventions and dental implant placement, realistic 3D hybrid models of patients must be composed of CBCT and IOS data.
Hence, this article aims to present a step-by-step method to acquire realistic hybrid digital models for virtual surgical planning before reconstructive dentoalveolar surgical interventions.

<table><tbody><tr><td>3DSlicer</td><td>3DSlicer (The software was first developed at Queen&amp;rsquo;s University Canada and since it is open source it is constantly developed by it&amp;rsquo;s community)</td><td>4.13.0-2021-03-19</td><td>Open source radiographic image processing software platform. Software is primarily intended for general medicine, however the wide range of segmentation an modelling tools allow it&amp;rsquo;s use for dental purposes as well</td></tr><tr><td>Meshmixer</td><td>Autodesk Inc.</td><td>3.5</td><td>Open source free form surface modelling software developed for prototype development and basic 3D sculpting. However, due to the usefulness of tools for dental purpose, not just 3D models, but even static guides for navigated surgery can be designed.</td></tr></tbody></table>

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.

Virtual allowing three-dimensional (3D) models can be generated using radiographic image segmentation, spatial registration, and free-form modeling. The models digitally depict the clinical situation, making three-dimensional planning of various surgical interventions possible. With separate segmentation of bone and teeth, the boundary between the two anatomical structures is visible, 3D morphology and localization of periodontal intrabony defects are to be assessed. The severity, extent, and morphology of acute and chro...

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

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 ...

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JoVE Journal

Medicine

1.7K Views.  Department of Periodontology, Semmelweis University. Realistische virtuele modellen kunnen parodontale en alveolaire rijke defecten in drie dimensies laten zien. Daarom kunnen ze het chirurgische behandelingsproces ondersteunen en een dieper inzicht geven in postoperatieve genezingsmechanismen. In vergelijking met eerdere en bestaande methoden geeft de huidige aanpak elke anatomische structuur afzonderlijk weer.Daarom geven deze virtuele 3D-modellen het echte klinische scenario realistisch weer. Deze methode helpt de beperkingen van trad...

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

Designing CAD/CAM Surgical Guides for Maxillary Reconstruction Using an In-house Approach

Computer-aided design/computer-assisted manufacturing (CAD/CAM) is now being evaluated as a preparative technique for maxillofacial surgery. Because this technique is expensive and available in only limited areas of the world, we developed a novel CAD/CAM surgical guide using an in-house approach. By using the CAD software, the maxillary resection area and cutting planes and the fibular cutting planes and angles are determined. Once the resection area is decided, the necessary faces are extracted using a Boolean modifier. These superficial faces are united to fit the surface of the bones and thickened to stabilize the solids. Not only the cutting guides for the fibula and maxilla but also the location arrangement of the transferred bone segments is defined by thickening the superficial faces. The CAD design is recorded as .stl files and three-dimensionally (3-D) printed as actual surgical guides. To check the accuracy of the guides, model surgery using 3-D-printed facial and fibular models is performed. These methods may be used to assist surgeons where commercial guides are not available.

Methods for designing a computer-aided design/computer-aided manufacturing (CAD/CAM) surgical guide are shown. Cutting planes are separated, united, and thickened to easily visualize the necessary bone transfer. These designs can be three-dimensional printed and checked for accuracy.

Computer-aided design/computer-assisted manufacturing (CAD/CAM) is now being evaluated as a preparative technique for maxillofacial surgery. Because this ...

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

Valid comparisons of postoperative accuracy results in computer-assisted reconstruction of the mandible are difficult due to heterogeneity in imaging modalities, mandibular defect classification, and evaluation methodologies between studies. This guideline uses a step-by-step approach guiding the process of imaging, classification of mandibular defects and volume assessment of three-dimensional (3D) models, after which a legitimized quantitative accuracy evaluation method can be performed between the postoperative clinical situation and the preoperative virtual plan. The condyles and the vertical and horizontal corners of the mandible are used as bony landmarks to define virtual lines in the computer-assisted surgery (CAS) software. Between these lines the axial, coronal, and both sagittal mandibular angles are calculated on both pre- and postoperative 3D models of the (neo)mandible and subsequently the deviations are calculated. By superimposing the postoperative 3D model to the preoperative virtually planned 3D model, which is fixed to the XYZ axis, the deviation between pre- and postoperative virtually planned dental implant positions can be calculated. This protocol continues and specifies an earlier publication of this evaluation guideline.

Here, we propose a practical, feasible and reproducible evaluation guideline for computer-assisted reconstruction of the mandible in order to create uniformity between studies regarding postoperative accuracy evaluation. This protocol continues and specifies an earlier publication of this evaluation guideline.

Valid comparisons of postoperative accuracy results in computer-assisted reconstruction of the mandible are difficult due to heterogeneity in imaging ...

3D Planning and Printing of Patient Specific Implants for Reconstruction of Bony Defects

We are in the midst of the 3D era in most aspects of life, and especially in medicine. The surgical discipline is one of the major players in the medical field using the constantly developing 3D planning and printing capabilities. Computer-assisted design (CAD) and computer assisted manufacturing (CAM) are used to describe the 3D planning and manufacturing of the product. The planning and manufacturing of 3D surgical guides and reconstruction implants is performed almost exclusively by engineers. As technology advances and software interfaces become more user-friendly, it raises a question regarding the possibility of transferring the planning and manufacturing to the clinician. The reasons for such a shift are clear: the surgeon has the idea of what he wants to design, and he also knows what is feasible and could be used in the operating room. It allows him to be prepared for any scenario/unexpected results during the operation and allows the surgeon to be creative and express his new ideas using the CAD software. The purpose of this method is to provide clinicians with the ability to create their own surgical guides and reconstruction implants. In this manuscript, a detailed protocol will provide a simple method for segmentation using segmentation software and implant planning using a 3D design software. Following the segmentation and stl file production using segmentation software, the clinician could create a simple patient specific reconstruction plate or a more complex plate with a cradle for bone graft positioning. Surgical guides can be created for accurate resection, hole preparation for proper reconstruction plate positioning or for bone graft harvesting and re-contouring. A case of lower jaw reconstruction following plate fracture and nonunion healing of a trauma sustained injury is detailed.

This protocol describes the use of 3D planning and printing for reconstruction of bony defects. We use segmentation tools to create 3D models followed by 3D design software to create patient specific implants for reconstruction purposes concomitant to ablative surgery or as a second stage.

We are in the midst of the 3D era in most aspects of life, and especially in medicine. The surgical discipline is one of the major players in the medical ...

Treatment of Facial Deformities using 3D Planning and Printing of Patient-Specific Implants

Technological advancements in surgical planning and patient-specific implants are constantly evolving. One can either adopt the technology to achieve better results, even in the less experienced hand, or continue without it. As technology develops and becomes more user-friendly, we believe it is time to allow the surgeon the option to plan his/her operations and create his/her own patient-specific surgical guides and fixation plates allowing him full control over the process. We present here a protocol for 3D planning of the operation followed by 3D planning and printing of surgical guides and patient-specific fixation implants. During this process we use two commercial computer-assisted design (CAD) software. We also use a fused deposition&#160;modeling printer for the surgical guides and a selective laser sintering printer for the titanium patient-specific fixation implants. The process includes computed tomography (CT) imaging acquisition, 3D segmentation of the skull and facial bones from the CT, 3D planning of the operations, 3D planning of patient-specific fixation implant according to the final position of the bones, 3D planning of surgical guides for performing an accurate osteotomy and preparing the bone for the fixation plates, and 3D printing of the surgical guides and the patient-specific fixation plates. The advantages of the method include full control over the surgery, planned osteotomies and fixation plates, significant reduction in price, reduction in operation duration, superior performance and highly accurate results. Limitations include the need to master the CAD programs.

As technology develops and becomes more user-friendly, planning of operations and patient-specific surgical guides and fixation plates should be performed by the surgeon. We present a protocol for 3D planning of orthognathic skeletal movements and 3D planning and printing of patient-specific fixation plates and surgical guides.

Technological advancements in surgical planning and patient-specific implants are constantly evolving. One can either adopt the technology to achieve ...

Dynamic Navigation in Endodontics: Guided Access Cavity Preparation by Means of a Miniaturized Navigation System

In the case of teeth with pulp canal calcification (PCC) and apical pathology or pulpitis, root canal treatment can be very challenging. PCC are common sequelae of dental trauma but can also occur with stimuli such as caries, bruxism, or after placing a restoration. In order to access the root canal as minimally invasive as possible in case of a necessary root canal treatment, dynamic navigation has recently been introduced in endodontics in addition to static navigation. The use of a dynamic navigation system (DNS) requires pre-operative cone-beam computed tomography (CBCT) imaging and a digital surface scan. If necessary, reference markers must be placed on the teeth before the CBCT scan; with some systems, these can also be planned and created digitally afterward. By means of a stereo camera connected to the planning software, the drill can now be coordinated with the help of reference markers and virtual planning. As a result, the position of the drill can be displayed on the monitor in real-time during preparation in different planes. In addition, the spatial displacement, the angular deviation, and the depth position are also displayed separately. The few commercially available DNS mostly consist of relatively large camera-marker-systems. Here, the DNS contains miniaturized components: a low-weight camera (97 g) mounted on the micromotor of the electric handpiece utilizing a manufacturer-specific connecting mechanism and a small marker (10 mm x 15 mm), which can be easily attached to an individually manufactured intraoral tray. For research purposes, a post-operative CBCT scan can be matched with the pre-operative one, and the volume of tooth structure removed can be calculated by the software. This work aims to present the technique of guided access cavity preparation by means of a miniaturized navigation system from imaging to clinical implementation.

Dynamic navigation systems (DNS) provide real-time visualization and guidance to the operator during endodontic access cavities preparation. The planning of the procedure requires three-dimensional imaging utilizing cone beam computed tomography and surface scans. After the export of the planning data to the DNS, access cavities can be prepared with minimal invasion.

In the case of teeth with pulp canal calcification (PCC) and apical pathology or pulpitis, root canal treatment can be very challenging. PCC are common ...

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, ...

Three-Dimensional Preoperative Virtual Planning in Derotational Proximal Femoral Osteotomy

Anterior knee pain (AKP) is a common pathology among adolescents and adults. Increased femoral anteversion (FAV) has many clinical manifestations, including AKP. There is growing evidence that increased FAV plays a major role in the genesis of AKP. Furthermore, this same evidence suggests that derotational femoral osteotomy is beneficial for these patients, as good clinical results have been reported. However, this type of surgery is not widely used among orthopedic surgeons.
The first step in attracting orthopedic surgeons to the field of rotational osteotomy is to give them a methodology that simplifies preoperative surgical planning and allows for the previsualization of the results of surgical interventions on computers. To that end, our working group uses 3D technology. The imaging dataset used for surgical planning is based on a CT scan of the patient. This 3D method is open access (OA), meaning it is accessible to any orthopedic surgeon at no economic cost. Furthermore, it not only allows for the quantification of femoral torsion but also for carrying out virtual surgical planning. Interestingly, this 3D technology shows that the magnitude of the intertrochanteric rotational femoral osteotomy does not present a 1:1 relationship with the correction of the deformity. Additionally, this technology allows for the adjustment of the osteotomy so that the relationship between the magnitude of the osteotomy and the correction of the deformity is 1:1. This paper outlines this 3D protocol.

This work presents a detailed surgical planning protocol using 3D technology with free open-source software. This protocol can be used to correctly quantify femoral anteversion and simulate derotational proximal femoral osteotomy for the treatment of anterior knee pain.

Anterior knee pain (AKP) is a common pathology among adolescents and adults. Increased femoral anteversion (FAV) has many clinical manifestations, ...

Evaluation of Maxillary Posterior Tooth Movement Using Digital Superimposition

Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition

Since the introduction of Invisalign by Align Technology, Inc. in 1999, questions and debates have persisted regarding the precision of Invisalign (clear aligner) therapy, particularly when compared to the use of traditional fixed appliances. This becomes particularly significant in cases involving anteroposterior, vertical, and transverse corrections, where precise comparisons are of paramount importance. To address these inquiries, this study introduces a meticulously devised protocol, placing a primary emphasis on digitally superimposing the movement of maxillary posterior teeth to facilitate accurate analysis. The sample included 25 patients who had completed their first series of Invisalign (clear) aligners. Four maxillary digital models (pre-treatment, post-treatment, ClinCheck-initial, and final models) were digitally superimposed using the palate rugae and dentitions as stable references. A software combination was used for model superimposition and tooth segmentation. Transformation matrices then expressed the differences between the achieved and predicted tooth positions. Thresholds for clinically relevant differences were at &plusmn;0.25 mm for linear displacement and &plusmn;2&#176; for rotation. Differences were assessed using Hotelling's T-squared tests with Bonferroni correction. The mean differences in rotation (2.036&#176; &plusmn; 4.217&#176;) and torque (-2.913&#176; &plusmn; 3.263&#176;) were significant statistically and clinically, with p-values of 0.023 and 0.0003 respectively. De-rotation of premolars and torque control for all posterior teeth were less predictable. All mean differences for the linear measurements were statistically and clinically insignificant, except that the first molars seemed slightly (0.256 mm) more intruded than their predicted position. The clear aligner system appears to meet its prediction for most translational tooth movements and mesial-distal tipping in maxillary posterior teeth for non-extraction cases with mild to moderate malocclusions.

Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment

This manuscript presents a comprehensive protocol to evaluate the three-dimensional (3D) movement of maxillary posterior teeth with clear aligners using digital model superimposition, an invaluable tool in orthodontics and dentofacial orthopedics.

Since the introduction of Invisalign by Align Technology, Inc. in 1999, questions and debates have persisted regarding the precision of Invisalign (clear ...

Artificial Intelligence-Based Integration of Dental Images

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

This study aimed to introduce cone-beam computed tomography (CBCT) digitization and integration of digital dental images (DDI) based on artificial intelligence (AI)-based registration (ABR) and to evaluate the reliability and reproducibility using this method compared with those of surface-based registration (SBR). This retrospective study consisted of CBCT images and DDI of 17 patients who had undergone computer-aided bimaxillary orthognathic surgery. The digitization of CBCT images and their integration with DDI were repeated using an AI-based program. CBCT images and DDI were integrated using a point-to-point registration. In contrast, with the SBR method, the three landmarks were identified manually on the CBCT and DDI, which were integrated with the iterative closest points method.
After two repeated integrations of each method, the three-dimensional coordinate values of the first maxillary molars and central incisors and their differences were obtained. Intraclass coefficient (ICC) testing was performed to evaluate intra-observer reliability with each method&#39;s coordinates and compare their reliability between the ABR and SBR. The intra-observer reliability showed significant and almost perfect ICC in each method. There was no significance in the mean difference between the first and second registrations in each ABR and SBR and between both methods; however, their ranges were narrower with ABR than with the SBR method. This study shows that AI-based digitization and integration are reliable and reproducible.

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

A process of registering cone-beam computed tomography scans and digital dental images has been presented using artificial intelligence (AI) -assisted identification of landmarks and merging. A comparison with surface-based registration shows that AI-based digitization and integration are reliable and reproducible.

This study aimed to introduce cone-beam computed tomography (CBCT) digitization and integration of digital dental images (DDI) based on artificial ...

Virtual Reality-3D Modeling for Minimally Invasive Neuro-Interventional Planning

Pioneering Patient-Specific Approaches for Precision Surgery Using Imaging and Virtual Reality

Endovascular treatment of complex vascular anomalies shifts the risk of open surgical procedures to the benefit of minimally invasive endovascular procedural solutions. Complex open surgical procedures used to be the only option for the treatment of a myriad of conditions like pulmonary and aortic valve replacement as well as cerebral aneurysm repair. However, due to advancements in catheter-delivered devices and operator expertise, these procedures (along with many others) can now be performed through minimally invasive procedures delivered through a central or peripheral vein or artery. The decision to shift from an open procedure to an endovascular approach is based on multi-modal imaging, often including 3D Digital Imaging and Communications in Medicine (DICOM) imaging datasets. Utilizing these 3D images, our lab generates 3D models of the pathologic anatomy, thereby allowing the pre-procedural analysis necessary to pre-plan critical components of the catheterization lab procedure, namely, C-arm positioning, 3D measurement, and idealized road-map generation. This article describes how to take segmented 3D models of patient-specific pathology and predict generalized C-arm positions, how to measure critical two-dimensional (2D) measurements of 3D structures relevant to the 2D fluoroscopy projections, and how to generate 2D fluoroscopy roadmap analogs that can assist in proper C-arm positioning during catheterization lab procedures.

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Advancements in endovascular treatment have replaced complex open surgical procedures with minimally invasive options, like valve replacement and aneurysm repair. This paper proposes using three-dimensional (3D) modeling and virtual reality to aid in C-arm positioning, angle measurements, and roadmap generation for neuro-interventional catheterization lab procedural planning, minimizing procedure time.

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

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