This study was supported by Seoul National University Bundang Hospital (SNUBH) Research Fund. (Grant no. 14-2019-0023).

This retrospective study was reviewed and approved by the Institutional Review Board of Seoul National University Bundang Hospital (B-2205-759-101) and complied with the principles of the Declaration of Helsinki. Digital Imaging and Communications in Medicine (DICOM) files from CBCT and DDI in Standard Tessellation Language (STL) format from the dental cast were utilized in the study. The need for informed consent was waived due to the retrospective nature of the study.
1. CBCT and Digital Dental Images (DDI) acquisition
<ol>
	<li>Select patients based on the following inclusion criteria: skeletal Class III malocclusion; bimaxillary surgery via computer-aided planning; and orthodontic treatment with fixed edgewise appliances.</li>
	<li>Exclude patients with craniofacial syndromes, cleft lip/palate, or missing maxillary first molars or right central incisor.</li>
	<li>Obtain CBCT scans with a field of view of 200 mm x 180 mm, a voxel size of 0.2 mm, and exposure conditions of 80 kVp, 15 mA, and 10.8 s. Ensure that patients are in an upright position with their teeth in maximum intercuspation. Save the scans as Digital Imaging and Communications in Medicine (DICOM) data files.</li>
	<li>Acquire DDIs from dental stone casts or direct intraoral scanning and save them in the Standard Tessellation Language (STL) format as separate maxillary and mandibular dentition.</li>
</ol>
2. AI-based Registration Protocol (ABR)
<ol>
	<li>CBCT reorientation and digitization
	<ol>
		<li>Open the software and click the Load DICOM File button to import CBCT DICOM files into the software.</li>
		<li>Select any one of the DICOM files in the DICOM data folder and click open. 
		NOTE: When DICOM files are loaded, the software automatically reconstructs them into a CBCT craniofacial volume.</li>
		<li>Click on&#160;the Reorientation button in the Landmark panel (Figure 1).</li>
		<li>N (Nasion): click the V notch of the frontal bone in the 3D view (Figure 2). Immediately after the click, observe that the blue dot (activated) turns into a red cross that will appear in the axial, sagittal, and coronal views as well. Click the blue triangular arrows back and forth to identify the landmark.
		<ol>
			<li>In the sagittal view, scroll the mouse wheel up and down to find the most anterior point where the frontonasal suture meets the nasal and frontal bones and click to determine the vertical and anteroposterior position of the landmark.</li>
			<li>In the coronal view, scroll the mouse wheel up and down to find the moment just before the nasal bone disappears to ensure the most anterior point and click to determine the horizontal position of the Nasion.</li>
			<li>In the axial view, adjust the anteroposterior position as it is on the most anterior point.</li>
		</ol>
		</li>
		<li>R Or (Orbitale): click the most inferior point on the margin of the right orbital contour in the 3D model (Figure 2).
		<ol>
			<li>In the coronal view, scroll the mouse wheel up and down to find the lowest point on the inferior margin of the right orbit and click.</li>
			<li>In the sagittal view, click the most superior point of the right maxilla or zygomatic bone structure that constitutes the lower boundary of the orbit.</li>
			<li>In the axial view, scroll through the mouse and click so that the red cross is positioned where the eye orbit rim meets.</li>
		</ol>
		</li>
		<li>L Or (Orbitale): click the most inferior point on the margin of the left orbital contour in the 3D model (Figure 2) and modify the point on the three views as in the process for R Or.</li>
		<li>R Po (Porion): click the most superior point of the outline of the right external auditory meatus in the 3D model (Figure 2).
		<ol>
			<li>In the coronal view, click the lowest point of the right temporal bone to determine the horizontal and vertical positions.</li>
			<li>In the sagittal view, click the most superior point of the outline of the right external auditory meatus to adjust the vertical and anterior-posterior positions.</li>
			<li>In the axial view, scroll the mouse wheel to click where the external auditory canal appears, in which the line of the temporal bone disappears.</li>
		</ol>
		</li>
		<li>L Po (Porion): click the most superior point of the outline of the left external auditory meatus in the 3D model (Figure 2) and modify the point in the three multiplanar views as in the process for R Po. 
		NOTE: The five basic skeletal landmarks, including Nasion, right and left orbitales, and right and left porions in the reconstructed craniofacial model (Figure 2), are now identified.</li>
		<li>Click on the Done button to complete the reorientation of the reconstructed craniofacial model.</li>
		<li>Click on the Preliminary Landmark Picking button in the Landmark panel and select the Dentition I landmark group. 
		NOTE: Landmark groups of cranial base, TMJ, Maxillary Skeletal, Mandibular Skeletal, Dentition I, and Soft tissue are already selected for craniofacial analysis.</li>
		<li>Click on the Execute button in the Preliminary Landmark Picking panel and let the software automatically pick preliminary landmarks and determine their coordinates.</li>
		<li>When modifying the landmarks, press the Manual Landmark Picking button in the Volume tab, make the necessary adjustments, and click on the Done button to confirm (Figure 3).</li>
	</ol>
	</li>
</ol>
3. DDI merging procedure
<ol>
	<li>Click on the Registration of Dentition Scan button in the Tools panel (Figure 4).</li>
	<li>Select maxilla dentition and click on the Load button in the Dentition Registration panel.</li>
	<li>Select the STL files of the same patient with the CBCT model in the folder to load maxilla dentition STL files. Once the STL files are open, look for DDIs on the right side of the screen and four views (3D, axial, sagittal, and coronal) of the CBCT on the left side of the screen.</li>
	<li>Pick the registration landmarks on the loaded DDI: the mesiobuccal cusps of the right maxillary first molar (R U6CP), the right maxillary central incisor midpoint on incisal edge (R U1CP), and the mesiobuccal cusp of the left maxillary first molar (L U6CP) (Figure 5) by switching the blue triangular arrows back and forth. 
	NOTE: Left-click and drag the mouse to rotate the DDI and right-click and drag to zoom in and out. The registration landmarks are simultaneously calibrated by machine-learned automation after being digitized manually.</li>
	<li>Click on the Done button in the Dentition Registration panel.</li>
	<li>Click on the Yes button to confirm the automatic registration (Figure 6).</li>
	<li>For mandibular dentition merging, select mandible dentition and click on the Load button in the Dentition Registration panel. Repeat steps 3.2 to 3.6. Pick the registration landmarks on the mandibular dentition: the mesiobuccal cusp of the right/left lower first molar (R-/L- L6CP), right lower first incisor midpoint on the incisal edge (R L1CP).</li>
	<li>The DDI is now merged with the reconstructed CBCT model (Figure 7).
	<ol>
		<li>When modifying the merging, click on the Pick Registration Landmark button in the Dentition Registration panel (Figure 8).</li>
	</ol>
	</li>
</ol>
4. Obtaining the 3D coordinate values (x, y, and z) of each landmark
<ol>
	<li>Click on the Manual Landmark Picking button in the Volume tab or click the Analysis tab to obtain the 3D coordinate values of the landmarks. For data export, go to analysis tab<s>&#8594;</s>data export panel, and click on the Landmark button to save the data as a file. 
	NOTE: The X-plane (horizontal) is the plane that passes through the Nasion, parallel to the Frankfort horizontal (FH) plane which passes through the left and right Orbitales and right Porion. The Y-plane (midsagittal) is perpendicular to the X-plane, passing through the Nasion and basion. The Z-plane (coronal) sets the plane perpendicular to the horizontal and midsagittal planes via the Nasion (zero point; 0, 0, and 0) (Figure 9).</li>
</ol>

Artificial Intelligence-Based Integration of Dental Images

<ol>
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S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Registration+accuracy+in+the+integration+of+laser-scanned+dental+images+into+maxillofacial+cone-beam+computed+tomography+images.">Registration accuracy in the integration of laser-scanned dental images into maxillofacial cone-beam computed tomography images.</a> Am J Orthod Dentofacial Orthop. 140 (4), 585-591 (2011).</li><li>Zou, 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=Accuracy+of+a+surface-based+fusion+method+when+integrating+digital+models+and+the+cone+beam+computed+tomography+scans+with+metal+artifacts.">Accuracy of a surface-based fusion method when integrating digital models and the cone beam computed tomography scans with metal artifacts.</a> Sci Rep. 12 (1), 8034 (2022).</li><li>Lin, H. H., 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=Artifact-resistant+superimposition+of+digital+dental+models+and+cone-beam+computed+tomography+images.">Artifact-resistant superimposition of digital dental models and cone-beam computed tomography images.</a> J Oral Maxillofac Surg. 71 (11), 1933-1947 (2013).</li><li>Park, J. H., 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=Use+of+artificial+intelligence+to+predict+outcomes+of+nonextraction+treatment+of+Class+II+malocclusions.">Use of artificial intelligence to predict outcomes of nonextraction treatment of Class II malocclusions.</a> Semin Orthodontics. 27 (2), 87-95 (2021).</li><li>Bao, H., 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=Evaluating+the+accuracy+of+automated+cephalometric+analysis+based+on+artificial+intelligence.">Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence.</a> BMC Oral Health. 23 (1), 1-10 (2023).</li><li>Gillot, M., 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=Automatic+landmark+identification+in+cone-beam+computed+tomography.">Automatic landmark identification in cone-beam computed tomography.</a> Orthod Craniofac Res. , (2023).</li><li>Blum, F. 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G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Statistical+power+analyses+using+G*Power+3.1:+tests+for+correlation+and+regression+analyses.">Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.</a> Behav Res Methods. 41 (4), 1149-1160 (2009).</li><li>Landis, J. R., Koch, G. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+measurement+of+observer+agreement+for+categorical+data.">The measurement of observer agreement for categorical data.</a> Biometrics. 33, 159-174 (1977).</li><li>Uechi, J., 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=A+novel+method+for+the+3-dimensional+simulation+of+orthognathic+surgery+by+using+a+multimodal+image-fusion+technique.">A novel method for the 3-dimensional simulation of orthognathic surgery by using a multimodal image-fusion technique.</a> Am J Orthod Dentofacial Orthop. 130 (6), 786-798 (2006).</li><li>Nkenke, E., 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=Fusion+of+computed+tomography+data+and+optical+3D+images+of+the+dentition+for+streak+artifact+correction+in+the+simulation+of+orthognathic+surgery.">Fusion of computed tomography data and optical 3D images of the dentition for streak artifact correction in the simulation of orthognathic surgery.</a> Dentomaxillofac Radiol. 33 (4), 226-232 (2004).</li><li>Lagrav&#232;re, M. 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The authors declare no conflicts of interest.

Using the presented protocol, digitization of landmarks and integrating CBCT and DDI can be easily accomplished using machine-learned software. This protocol requires the following critical steps: i) reorientation of the head in the CBCT scan, ii) digitization of CBCT and DDI, and iii) merging CBCT images with the DDI. The digitization of five landmarks for the reorientation of the head is critical because it determines the 3D position of the head with reference planes in spatial areas. Three landmarks (R-/L-U6CP and R U...

Three-dimensional (3D) digital technology has broadened the scope of diagnosis and planning for orthodontic or surgical-orthodontic treatment. A virtual head constructed from a facial cone-beam computed tomography (CBCT) image can be used to evaluate dentofacial and dental abnormalities, plan orthognathic surgery, fabricate dental wafers&#160;and implant surgical guides using computer-aided design and manufacturing1,2,3,4. However, CBCT scans have a low representation of dentition, including dental morphology and interocclusal relationship, which are due to their limited resolution and streak artifacts from dental restoration or orthodontic brackets5. Therefore, the dental features have been substituted on CBCT images with digital dental images (DDI), such as scanned casts or intraoral scan images.
For reliable integration of DDI on CBCT images, numerous studies reported various methods such as the use of fiducial markers6,7, voxel-based8, and surface-based registrations (SBRs)9,10. These procedures have their methods of using extraoral markers, multiple CBCT scans, and extra process steps such as cleaning metal artifacts on CBCT images. Regarding SBR accuracy, several previous studies reported errors ranging from 0.10 to 0.43mm9,11. In addition, Zou et al. evaluated intra-/inter-observer reliability and errors between a digital engineer and an orthodontist using SBR and reported the need for clinical experience and repeated learning10.
Artificial intelligence (AI) has been used to predict treatment outcomes12 and digitize landmarks on cephalometric radiographs13 or CBCT images14,15,16, and some commercial software is currently available to assist in this process17. Accurate identification of anatomical landmarks on 3D images is challenging because of the ambiguity of flat surfaces or curved structures, areas of low density, and the wide variability of the anatomical structures.
AI-based, machine-learned automation can be applied not only for digitization but also for the integration of DDI and dentofacial CBCT. However, there is little research on the accuracy of an AI-based registration (ABR) compared to the existing surface-based method. To achieve more accurate outcomes of 3D skeletal and dental changes through bimaxillary orthognathic surgery, it is necessary to evaluate the accuracy of AI-based programs when merging CBCT and DDI. Therefore, this article presents a step-by-step protocol for digitizing and integrating CBCT and DDI with an AI-based registration (ABR) and to evaluate its reliability and reproducibility compared to that of SBR.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>G*Power&#160;</td>
			<td>Heinrich Heine Universit&#228;t, D&#971;sseldorf, Germany</td>
			<td>v. 3.1.9.7</td>
			<td>A sample size calculuation software</td>
		</tr>
		<tr>
			<td>Geomagic Qualify&#174;</td>
			<td>3D Systems, 
			Morrisville, NC, USA</td>
			<td>v 2013</td>
			<td>3D metrology feature and automation software, 
			which transform scan and probe data into 3D to be used in design, manufacturing and metrology applications&#160;</td>
		</tr>
		<tr>
			<td>KODAK 9500</td>
			<td>Carestream Health Inc., Rochester, NY, USA</td>
			<td>5159538</td>
			<td>Cone Beam Computed Tomograph (CBCT)</td>
		</tr>
		<tr>
			<td>MD-ID0300</td>
			<td>Medit Co, Seoul, South Korea 
			Seoul, Korea</td>
			<td>61010-1</td>
			<td>Desktop model scanner&#160;</td>
		</tr>
		<tr>
			<td>ON3D</td>
			<td>3D ONS Inc., 
			Seoul, Korea</td>
			<td>v 1.3.0</td>
			<td>Software for 3D CBCT evaluation; AI-based landmark identification, craniofacial and TMJ analysis, superimposition, and virtual orthognathic surgery</td>
		</tr>
		<tr>
			<td>SPSS&#160;</td>
			<td>IBM, Armonk, NY, USA</td>
			<td>v 22.0&#160;</td>
			<td>A statistic analysis program</td>
		</tr>
	</tbody>
</table>

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

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

Our research is focused on integration of CBCT and digital dental images, which is inevitable in creating a virtual head. So far, those three images are marginally using the best film method, which is surface-based. This research is to introduce a new integration method based on AI-assisted digitization and to evaluate its accuracy.Artificial intelligence has been used to predict treatment outcomes and digitize landmarks in supplementary radiographs or CPCT images. There are some commercial softwares available. This program adopts AI-assisted machine-learned automation in digitizing landmarks in CPCT and also calibrating manually picked landmarks in dental images.The internal observer, the lab team, showed significant and almost full effect ICC in each method. The main difference showed no significant between the first and second registrations in each ABR and SBR and between both methods. However, the ranges were lower with ABR than with the SBR method.The ABR protocol not only improved the accuracy, but also significantly reduced the merging time. While the SBR method took three to four minutes, the ABR program only required about 50 seconds for landmark selection, 40 seconds for DDI landmarks, and two to three seconds for CPCT and DDI merging.

Here we described the integration process of CBCT and DDI using an AI-based program. To evaluate its reliability and reproducibility, a comparative study with surface-based registration (SBR) was conducted. It was determined that a minimum sample size of ten was required after a power analysis under correlation &#961; H1 = 0.77, &#945; = 0.05, and power (1&#8722;&#946;) = 0.8018. A total of 17 sets of CBCT scans and digital dental images from orthognathic patients at Seoul National University Bund...

Watch this Scientific Journal Video about Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization at JoVE.com

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 ...

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Medicine

AI-Based Registration and Merging of Digital Dental Images for the Comparative Evaluation Against Surface-Based Registration

568 Views.  Seoul National University Bundang Hospital.  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.

Artificial Intelligence-Based Integration of Dental Images

Summary