According to this scoping review, an increasing number of studies have employed AI for various tasks related to dental crown prostheses. To align with the rapid evolution of digital technologies, the investigation focused on the most recent 14 years. The application of AI in the fabrication and evaluation of dental crowns can contribute to increased efficiency for prosthodontists, raising expectations for improved productivity. The application of AI to the following fields related to dental crown prosthesis was reviewed.
Dental crown prosthesis designed by AI
The production of crown prostheses has been separated into traditional wax-up methods and digital approaches using CAD/CAM systems. Recently, there has been a widespread adoption of CAD/CAM methodologies, particularly in conjunction with the popularization of zirconia usage. Notably, these methods report high levels of accuracy and success rates [33]. In the CAD/CAM process, there have been efforts to enhance the speed and precision of prosthesis design through the application of AI [34].
Seven studies evaluated the feasibility of AI models to design dental crown prostheses [21,22,25,28,29,31,32. Four studies used AI software [25, 28, 31, 32], while three studies [21, 22, 29] employed 3D-GAN algorithms for model training. In six studies, integration of AI into the fabrication process demonstrated higher accuracy compared with traditional CAD or manually designed methods. One study [28] reported that knowledge-based AI, compared to human-designed CAD software, exhibited a higher occlusal profile discrepancy.
Two studies [25, 31] reported high time efficiency of AI-based programs. While traditional CAD work does not involve adding and modifying wax, it requires human thinking. Both wax-up and CAD processes are heavily influenced by the accumulated experience of dental technicians [35, 36]. However, AI-based operations minimize human intervention using automated calculations. Therefore, designing with AI significantly reduces the time spent on the design process, especially in cases with extensive restoration requirements.
Among the seven studies, four used commercially produced AI software, with two of them lacking specific mention of the program's algorithm [25, 28]. The absence of detailed information on the algorithms and metrics used for training the models in these studies acts as a limitation, emphasizing the need for transparency in the application of AI in dentistry, especially with commercially available software. Additionally, studies employing software had datasets ranging from 12 to 30 cases, posing a limitation in detecting statistical significance due to the potentially insufficient sample size. Despite these limitations, AI for dental crown design has the potential to significantly increase production efficiency by saving time. Considering the performance demonstrated in aspects such as morphology, internal fit, and occlusion, there is a promising outlook for the future utilization of AI in dentistry.
Detection of dental crown finish line
Choi et al. [23] compared the accuracy of hybrid software combined with deep learning with existing traditional CAD software in detecting the crown finish line. An accurate marginal fit is crucial for preventing microgaps. This, in turn, lowers the risk of caries and ensures that the restoration retains its function [37]. Recently, finish lines have been extracted and processed manually using CAD programs, but this is a repetitive and time-consuming process [38]. In Choi et al., as a result of evaluation using Hausdorff distance and chamfer distance, the hybrid system showed statistically more accurate results. This implies that a hybrid approach, integrating both deep learning and computer-aided design methods, may allow robust and precise extraction of finish lines with minimal adjustments required.
Evaluation of crown preparation
One of the most fundamental aspects in dental prosthodontics education is understanding and practicing the principles of tooth preparation [39]. However, evaluating students' tooth preparation outcomes in dental education can lack consistency due to factors such as subjective grading scales and insufficient inter-rater agreement. This difficulty hinders the provision of ongoing and reliable feedback [40, 41].
Han et al. [27] assessed the viability of software-based automated evaluation (SAE) with AI to evaluate abutment tooth preparation for single crowns. This was done through a comparison with a human-based digitally-assisted evaluation (DAE), which showed perfect intra-rater agreement and almost perfect inter-rater agreement with SAE. The findings of this study substantiate the credibility of SAE within prosthodontics education and propose its potential clinical utility for evaluating tooth preparation
Evaluation of AI-based color matching
A crucial aspect of the dental technician's role is replicating the natural color of teeth in dental prostheses. An experienced dental technician possesses the ability to precisely assess the authentic color. However, this proves to be a challenging task for a less-experienced dental technician [42].
Ueki et al. [26] extracted 62 images of patient teeth, which were annotated by experienced dental technicians. They then used a neural network to estimate the true color. The accuracy of the first candidate's output was six of 22 (27%), considerably lower than the desired level. However, the outputs for the second and third candidates encompassed 12 (55%) and 15 (68%) of the total 22 images, respectively. This affirmed accurate classification of certain colors.
One notable limitation of this study is the relatively small size of the image dataset used. To more accurately assess the potential of AI in shade selection, a substantial amount of training data is required.
Identification of dental crown in intraoral photo
In clinical situations, it is crucial for dentists to gather intraoral information about patients—a process that demands time and effort. Additionally, the effectiveness of this procedure relies on the dentist's knowledge and experience. Consequently, there is a demand for an automated system that can rapidly assess the intraoral situation.
Takahashi et al. [24] used a deep learning object detection method to recognize dental prostheses and restorations. In their study, ‘You Only Look Once version 3’ (YOLOv3) was used for object detection because it has shown high performance in other dental deep learning studies [43, 44, 45].
A satisfactory level of performance is typically associated with an IoU exceeding 0.7 [46, 47]. In the present investigation, the IoU was 0.76. Consequently, the proficiency of this learning system was high. In assessing the accuracy of the object detection model, the mAP is employed, with values above 0.7 considered favorable in previous research [48]. The mAP achieved in the present study was 0.80, supporting the learning system's commendable performance from a mAP perspective.
Irrespective of the overall count of objects across all images, there was a tendency for higher average precision (AP) scores in cases of metallic-colored prostheses, while tooth-colored prostheses exhibited a tendency toward lower AP scores. These findings suggest that the identification was influenced by the color distinctions between the natural teeth and prostheses.
Prediction of debonding probability
CAD/CAM composite resin (CR) crowns cemented to dentin often exhibit a propensity for debonding within one year, and the reported debonding rate for CAD/CAM CR crowns cemented on implant abutments stands at 80% within one year [49]. Inadequate preparation has been identified to contribute to debonding [50, 51].
Yamaguchi et al. [30] aimed to predict the debonding probability of CAD/CAM CR crowns using scanned images of prepared models employing convolutional neural networks. The reported prediction accuracy was 98.5%. Despite the good performance, this study acknowledges a limitation in explaining the primary factor contributing to debonding, stating that it was difficult to pinpoint the main cause.