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

Artificial Intelligence-Based Integration of Dental Images

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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

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 ...

Araştırmamız, sanal bir kafa oluşturmada kaçınılmaz olan CBCT ve dijital dental görüntülerin entegrasyonuna odaklanmıştır. Şimdiye kadar, bu üç görüntü marjinal olarak yüzey tabanlı olan en iyi film yöntemini kullanıyor. Bu araştırma, yapay zeka destekli sayısallaştırmaya dayalı yeni bir entegrasyon yöntemini tanıtmak ve doğruluğunu değerlendirmektir.Yapay zeka, tedavi sonuçlarını tahmin etmek ve ek radyografilerde veya CPCT görüntülerinde yer işaretlerini dijitalleştirmek için kullanılmıştır. Bazı ticari yazılımlar mevcuttur. Bu program, CPCT'deki yer işaretlerinin sayısallaştırılmasında ve ayrıca dental görüntülerde manuel olarak seçilen yer işaretlerinin kalibre edilmesinde yapay zeka destekli makine öğrenimi otomasyonunu benimser.İç gözlemci olan laboratuvar ekibi, her yöntemde önemli ve neredeyse tam etkili ICC gösterdi. Temel fark, her bir ABR ve SBR'de birinci ve ikinci kayıtlar arasında ve her iki yöntem arasında anlamlı bir fark göstermedi. Bununla birlikte, aralıklar ABR ile SBR yöntemine göre daha düşüktü.ABR protokolü yalnızca doğruluğu artırmakla kalmadı, aynı zamanda birleştirme süresini de önemli ölçüde azalttı. SBR yöntemi üç ila dört dakika sürerken, ABR programı yer işareti seçimi için yalnızca yaklaşık 50 saniye, DDI yer işaretleri için 40 saniye ve CPCT ve DDI birleştirme için iki ila üç saniye gerektiriyordu.