ai-based registration and merging of digital dental images for the comparative evaluation against surface-based registration

Artificial Intelligence-Based Integration of Dental Images

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.

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.

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

ai-based-registration-merging-digital-dental-images-for-comparative

Research

JoVE Journal

Medicine

69 Views.  Seoul National University Bundang Hospital. Per iniziare, avvia il software ON3D sul computer. Fare clic sull'icona di caricamento del file DICOM per importare i file. Selezionare un file DICOM e fare clic su Apri.Fare clic sull'icona di riorientamento nel pannello dei punti di riferimento. Quindi fare clic sulla tacca a V dell'osso frontale nella vista 3D. Scorri su e giù la vista sagittale per trovare il punto più anteriore.Quindi fare clic sul punto in cui la sutura frontonasale incontra le ossa nasali e frontali. Al...

Video: Registrazione e unione basate sull'intelligenza artificiale di immagini dentali digitali per la valutazione comparativa rispetto alla registrazione basata sulla superficie

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.

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.