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06 June 2024: Review Articles  

The Potential of Artificial Intelligence in Prosthodontics: A Comprehensive Review

Ibrahim Saleh Aljulayfi ORCID logo1ABC*, Ali Hamoud Almatrafi2ABD, Ramzi O. Althubaitiy1ACG, Fahad Alnafisah3BCF, Khalid Alshehri3BCF, Bandar Alzahrani3BCF, Khalid Gufran ORCID logo4DEG

DOI: 10.12659/MSM.944310

Med Sci Monit 2024; 30:e944310

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Abstract

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ABSTRACT: Prosthodontics is a dental subspecialty that includes the preparation of dental prosthetics for missing or damaged teeth. It increasingly uses computer-assisted technologies for planning and preparing dental prosthetics. This study aims to present the findings from a systematic review of publications on artificial intelligence (AI) in prosthodontics to identify current trends and future opportunities. The review question was “What are the applications of AI in prosthodontics and how good is their performance in prosthodontics?” Electronic searching in the Web of Science, ScienceDirect, PubMed, and Cochrane Library was conducted. The search was limited to full text from January 2012 to January 2024. Quadas-2 was used for assessing quality and potential risk of bias for the selected studies. A total of 1925 studies were identified in the initial search. After removing the duplicates and applying exclusion criteria, a total of 30 studies were selected for this review. Results of the Quadas-2 assessment of included studies found that a total of 18.3% of studies were identified as low risk of bias studies, whereas 52.6% and 28.9% of included studies were identified as studies with high and unclear risk of bias, respectively. Although they are still developing, AI models have already shown promise in the areas of dental charting, tooth shade selection, automated restoration design, mapping the preparation finishing line, manufacturing casting optimization, predicting facial changes in patients wearing removable prostheses, and designing removable partial dentures.

Keywords: Artificial Intelligence, Deep Learning, machine learning, Prosthodontics

Introduction

Today, “Hey Siri” and “Hi Alexa” are common phrases that are widely used as an example of artificial intelligence (AI)-based technologies. In fact, AI applications could be found everywhere during our daily activities. For instance, AI is the integral structure in operating social media platforms, ordering food from delivery sites, banking services, car navigation applications, self-driving vehicles, robots, and much more. Although the beginnings of modern AI started in 1956, it was not until the 21st century that most of the AI technologies became available. When reviewing the period from 1956 to 2023, more than 94% of literature found on AI was the last decade in the field of medical sciences [1]. This recent spike in publications related to AI in different fields clearly shows the growing interest in AI applications.

AI is a technology that uses machines to establish human-like behavior [1], and it can be categorized as weak (narrow) or strong. Weak AI is designed to focus on a narrow task and to seem very intelligent at it. In contrast, strong AI is intended to be capable of any cognitive functions that a human might have and to be, in essence, no different than a real human mind. Nevertheless, current applications of AI fall under what is called weak AI, whereas strong AI applications are not yet available, except in science fiction movies. Machine learning (ML) and deep learning (DL) are common terms widely used in AI. In fact, all DL is ML, but not all ML is DL. ML is a subdomain of augmented intelligence that “learns” intrinsic statistical patterns in data to make predictions on previously unseen data [1,2]. The main purpose of ML is to discover similar patterns in new data for use in a variety of applications, such as classification, regression, and clustering [3]. DL is an ML technique that uses multi-layer mathematical operations to learn and infer from complicated input, such as pictures [1,2]. DL has been extensively used in computer-aided detection and diagnosis (CAD) in recent years. DL models, primarily convolutional neural networks (CNNs), could convert input data such as images and radiographs into outputs that are magnificently able to diagnose whether the patient has a disease or not, while gradually learning higher-level features [4].

The evolution of AI has advanced the development of human society in the current time, with dramatic revolutions shaped by theory and technique [5,6]. According to Pethani [7], AI has made substantial progress in medicine, and there is a growing body of AI research in dentistry. AI models have many implications in the dental field, for example, screening of cancerous lesions, classifications of impacted supernumerary teeth, detection of carious lesions, and tooth imputations [8,9].

AI in prosthodontics is no exception, with a more recent and narrower spike in AI prosthodontics literature representing 94% over a shorter 5-year period. Despite the recent surge in AI prosthodontics literature, there remains a notable gap in the current knowledge regarding the comprehensive assessment and evaluation of AI applications in prosthodontics, specifically in terms of their performance in achieving prosthodontic tasks. AI in prosthodontics includes but is not limited to mapping the finishing line in prepared teeth, estimating facial modifications in patients with removable prostheses, automated tooth morphology design, color selection, mimicking the manufacturing of cast process, and designing of removable partial dentures [5].

The need for this systematic review arises from the potential of AI to revolutionize the field of prosthodontics. AI has shown promise in various applications within dentistry, such as dental charting, tooth shade selection, automated restoration design, and predicting treatment outcomes. However, there is a lack of comprehensive assessment regarding the development and performance of AI models specifically designed for prosthodontic treatment purposes. Moreover, the increasing interest in utilizing AI in prosthodontics and the lack of a comprehensive assessment of AI model development and performance in this field are still lacking. Therefore, this study aims to present the findings from a systematic review of publications on AI in prosthodontics to identify current trends and future opportunities.

Material and Methods

PROTOCOL AND REGISTRATION:

This systematic review followed the preferred reporting items for systematic review and meta-analysis (PRISMA) statement [10]. Moreover, the review was registered on the website of the International Prospective Register of Systematic Reviews PROSPERO, with the ID CRD42023464381.

REVIEW QUESTION:

The review question to conduct this systematic review was “What are the applications of AI in prosthodontics and how good is their performance in prosthodontics?” Based on the study question, this review focused on the studies that measured the performance of AI, whether it is ML or DL, in the following aspects: prosthodontic diagnosis, prediction of prosthodontic treatment outcomes, and prosthodontic treatment planning. Moreover, to better outline the study, the participants, interventions, comparisons, and outcomes (PICO) format was followed (Table 1).

DATABASE SEARCH:

The electronic databases Web of Science, ScienceDirect, PubMed, and Cochrane Library were searched. Searching for relevant studies was limited to full-text papers from January 2012 to January 2024. Numerous combinations of keywords were used in the search process presented in Table 2. Mendeley (Elsevier, London, UK) reference manager was used in this study to manage search collection. It was used to filter and remove duplicates.

SELECTION CRITERIA:

Selection of the articles was based on the in vitro studies and clinical studies that assessed the application and performance of AI in prosthodontics. AI articles related to other disciplines, AI approaches initiated for other dental disciplines, for instance orthodontic, pedodontics, periodontics, and maxillofacial surgery, literature reviews, studies published prior to 2012, and full-text papers that were not accessible were excluded from this review.

SEARCH APPROACH:

Three researchers (F.A., B.A., and K.A.) independently investigated the qualified articles and screened the topics and abstracts according to the inclusion and exclusion criteria. Thereafter, the full text was further evaluated. Any discrepancies between the researchers were solved by consulting a fourth researcher (A.A.). Three investigators conducted the data charting process independently. The following data were looked at for the analogical groups of studies: aim of the study, AI application, dataset preprocessing, best applied AI model, model parameters, and model performance.

RISK OF BIAS:

Quadas-2 was used for assessing quality and potential risk of bias for the qualified studies. According to the 4 domains of Quadas-2 (patient selection, index test, reference standard, and flow and timing), patient selection should be a consecutive and random sampling of eligible patients with suspected prosthodontic needs, to avoid the potential risk of bias. To assess studies, they were categorized into 3 categories, either high, unclear, or low. If the study was considered as low in all 4 domains, it was considered to have a low risk of bias or low applicability. If one or more of the domains was considered unclear or high, the study was considered as having the potential risk of bias or was applicable to our review.

The included publications were divided in accordance with the aim and application of the AI into 3 categories as follows: prosthodontic diagnosis, prediction of prosthodontic treatment outcomes, and prosthodontic treatment planning.

STATISTICAL ANALYSIS:

Statistical analyses were conducted using the IBM SPSS statistics (version 27) for macOS (IBM Corp, Armonk, NY, USA). Cohen kappa statistics were used to identify the inter-rater agreement in the quality analysis of the selected studies. The kappa score was divided into the following categories: poor (0–0.20), fair (0.21–0.40), moderate (0.41–0.60), substantial (0.61–0.80), and near perfect (0.81–1) [11].

Results

SELECTION OF THE STUDIES:

The PRISMA guideline followed for this present review and the selection procedure of the studies are presented in Figure 1. A total of 1925 studies were identified in the initial search. Before screening, 1610 duplicates were removed. After assessment of the full text, 29 articles that met the inclusion and exclusion criteria were selected. An additional study was selected from the reference list of the screened studies. Therefore, a total of 30 studies were selected and divided into 3 outcomes: prosthodontic diagnosis, prediction of prosthodontic treatment outcome, and prosthodontic treatment planning.

DENTAL CHARTING AND PROSTHODONTIC DIAGNOSIS:

A total of 11 articles were included in which AI was used to help diagnose prosthodontic cases (Table 3). Some studies used orthopantomogram radiographs as the model inputs [4,12–15]. Three-dimensional surface scans of dental casts were also used in 1 study to measure accuracy [16], and other studies used dental photos [17–19].

One study, by Melike et al [13], aimed to assess the performance of a new DL AI model of region-based CNN when it was used to assess panoramic radiographs for prosthesis detection and diagnosis. With the exception of caries and dental calculus, the suggested AI model was deemed successful in identifying dental conditions in all panoramic radiographs. The crown, implant, and impacted tooth all had the highest sensitivity scores, measuring 0.9674, 0.9615, and 0.9658, respectively. Another study that used an ML (Apox) AI model for the diagnosis of the absence or presence of teeth showed a sensitivity of 95% and specificity of 90%, and the diagnostic effectiveness was 93%. It showed a sensitivity of 93%, specificity of 99%, and diagnostic effectiveness of 98% when used in detecting crowns on panoramic radiographs [12]. When the region-based CNN + heuristics AI model was used with an intersection over union of 0.5, it achieved 96.7% accuracy for teeth detection, and 75.4% accuracy when the intersection over union was 0.7. However, when crown and implant fixture detection was used, it showed an accuracy of only 60.9% and 40.8%, respectively, with an intersection over union of 0.5 [14]. Rohrer et al [15] tested an increasing number of tiles of the panoramic radiographs and found an increased performance of the CNN AI model. However, the increase was only significant in detecting root canal fillings when the F1 score increased from 0.33 to 0.97 by changing the tile number from 1 to 20.

From the selected papers, 3 studies used dental images as the model input. Eto et al [17] used the CNN AI model to identify the type of restoration on molars using an intraoral scanner, whether it was a crown, partial restoration, or sound tooth. The study estimated the accuracy to be around 0.95 and the precision to be 0.952. In another study, the DL method to recognize the 11 types of dental prostheses and restorations gave an accuracy of 80% for metallic prostheses and 60% for tooth-colored prostheses [19]. The CNN AI model has also been used to classify dental arches based on the status of edentulism, and the diagnostic accuracy for the maxilla and mandible were 99.5% and 99.7%, respectively. Both arches had the same recall and precision, of around 1.0 and 0.25, respectively [18].

One study looked at dental cusp detection and classification using artificial neural networks (ANN) as an AI model. The study had an accuracy of 93.5% in detecting cusps [16].

PREDICTION OF PROSTHODONTIC TREATMENT:

A total of 13 studies were included to review the outcome of prosthodontic treatments using AI (Table 4). Among the 13 studies, 6 studies used AI in the prediction of dental prosthesis treatment outcomes [20–25]. One of the studies aimed to predict facial deformation after the insertion of complete dentures using a backpropagation (BP) neural network model. The study concluded an average error rate of 22.49% and an accuracy that showed a considerable relationship [20]. Another study aimed to test the prediction of soft tissue appearance in the lower third of the face after delivering complete dentures. The mean value of accuracy was 0.769±0.205 when comparing virtual predictions with the postoperative model scan values with using the network architecture of a back propagation neural network (BPNN) [21].

Deniz S et al [22] used an ANN model to predict the surface roughness and microhardness of denture teeth and found that the experimental data were distributed along the ANN predicted line when the mean error of 7.6596 for microhardness and surface roughness were 4.0012.

Regarding predicting long-term failure, an extra tree model was used in predicting the flexural strength of computer-aided design/computer-aided manufacturing (CAD/CAM) resin composite blocks. The model showed a good ability to identify the effective components that affected the flexural strength based on the available data set [23]. In another study, a CNN AI model was used to predict the debonding of composite resin crowns. The prediction accuracy, precision, recall, and F-measure values of DL with a CNN method for the prediction of the debonding probability were 98.5%, 97.0%, 100%, and 0.985, respectively [24].

Moreover, 5 studies from the selected studies looked at shade matching and prediction [5,8,26–28]. Justiawan et al [29] used digital transformation, K-Nearest Neighbors (KNN), and neural network models to predict shades and color matching process. That study aimed to solve problems associated with human visual subjectivity. KNN algorithm in red, green, and blue characteristics achieved a testing accuracy of 92.5% within only a 0.02-s computation time. Another study used a BPNN model to compare with the traditional visual method for shade matching. Significant differences were found between the 2 color-matching systems, with the authors concluding that the AI model produced greater accuracy in color reproduction [8]. Moreover, a model called genetic algorithm-back propagation (GA+BP) was proposed for dental porcelain computer color matching and showed a more stable prediction ability of GA+BP than did a BPNN model [5]. In a different study, a digital shade-matching device was developed for dental color determination using the support vector machine (SVM) algorithm. The SVM algorithm’s cross-validation results were compared to those of other classification algorithms, including learning rate, radio frequency, and KNN. Compared to SVM, the matching rate of learning rate and radio frequency was lower. KNN and SVM produced similar matching outcomes; however, the standard deviation of KNN was larger. As a result, SVM displayed more consistent performance [26]. In the same context, a neural network model was used to automate the process of skilled dental technicians determining tooth color from pictures of patients. The study confirmed the effectiveness of the methodology proposed [27].

A study proposed using hue, saturation, and value color models for shade matching. The peak signal-to-noise ratio was used to evaluate the above model in shade matching process. The peak signal-to-noise ratio was enhanced from 97.64 to 99.93 when the fuzzy decision model was applied [28]. One study used artificial models to observe teeth prognosis. A total of 3 AI machine-learning methods were used. Among the 3 tested methods, the decision tree classifier showed the best accuracy, of 84.13%, in predicting the prognosis of teeth [25].

PROSTHODONTIC TREATMENT PLANNING AND PROSTHESIS DESIGNING:

A total of 14 studies regarding treatment planning in prosthodontics were included and are presented in Table 5. The main measurements were accuracy, area under the curve (AUC), and error rate. Most of the studies used generative adversarial networks [30–32] and knowledge-based systems [33,34]. No studies used available public dataset sources.

One study applied AI in designing removable partial dentures and used a knowledge-based clinical support system with case-based reasoning and cosine algorithm. Results showed the area under the curve-receiver operating characteristic curve (AUC-ROC) was 96% and the mean average of precision was 0.61. The normalized discounted cumulative gain had a high-performance value of 0.74 [33].

A recent study used AI for designing dental prostheses in which the researchers used a knowledge-based system for designing lithium disilicate dental crowns. The average positive profile discrepancy was significant (P=0.024) [34]. Another study used a generative adversarial network for the reconstruction of occlusal surfaces in dental prosthesis and found that the error detection was 0.114 mm. The authors concluded that the final dental crown had accurate anatomical morphology [32].

Previous research used an expert system for casting metal substructures for designing metal-ceramic crowns. The dimensional error between the manufactured cast and the metal substructure was 0.018 mm and the mean value of arithmetic roughness on the cast substructure was 1935 to 2776 μm [35]. Moreover, another study that used ANN for variegation for manufacturing of maxillofacial prostheses reported the color difference between real skin and that of ANN was 3.45±0.87 [36].

QUALITY ASSESSMENT:

Results of the Quadas-2 assessment of included studies are presented in Table 6. A total of 18.3% of studies (7/38) were identified as low-risk of bias studies [8,25,29,33,43,45,46], whereas 52.6% (20/38) and 28.9% (11/38) of included studies were identified as studies with high [12–15,17–19,20–22,26,28,30,34–38, 42,44] and unclear [4,5,16,23,24,27,31,32,39,40,46] risk of bias, respectively. Inter-rater and intra-rater reliability of the Cohen kappa were 0.8831 and 0.9232, respectively, which reflect near-perfect reliability.

Discussion

AI holds enormous promise for dental treatments since it allows for fast and precise data extraction. This would directly improve clinical service efficiency by assisting dentists’ clinical choices and decreasing the time spent interpreting clinical findings. Thus, AI could be used as a powerful decision-support tool, particularly with less experienced practitioners [17,18]. However, an assessment of the development performance of AI models and their potential impact on prosthodontic diagnosis, treatment planning, and prediction of the treatment outcome is lacking. In this systematic review, we intended to review the applications and performance of AI models designed for prosthodontic treatment purposes.

Even though AI is new in the dental field, it has been found as an efficient technology. AI has shown promising effectiveness as a charting tool and for detecting crowns and implants. On the other hand, it does not seem to be efficient yet in detecting dental caries and calculus [13]. An increasing number of tiles of the panoramic radiographs was significant only in detecting root canal fillings [15]. That could be due to the selection of a dataset (panoramic radiograph), which is a non-diagnostic tool for caries and calculus detection [12–15]. It can be inferred that panoramic radiographs with the assistance of AI are still not the ideal method for dental charting as they cannot detect all dental and periodontal conditions.

Intraoral scanners were also used in a few studies with different AI models. With the help of a CNN AI model, scanners were used to determine the type of restoration on molar teeth, whether it was a crown, partial restoration, or sound tooth, and showed remarkable precision and accuracy in identifying the different situations [17]. In another study, a DL method was used to recognize the 11 types of dental prostheses and restorations and had an accuracy of 80% for metallic prostheses and 60% for tooth-colored prostheses [19]. A CNN AI model has also been used to classify dental arches based on the status of edentulism, and the diagnostic accuracy for the maxilla and mandible were 99.5% and 99.7%, respectively. In addition, both arches had the same recall and precision, of around 1.0 and 0.25, respectively [18].

In the previous AI literature, few studies aimed to predict the after-treatment outcome of the prosthodontic treatment. One of the applications is a tool to predict soft tissue changes after complete denture placement [20,21]. Although these studies revealed the ability of AI models to predict facial soft tissue changes, the accuracy still needs to be improved before the models can be relied on as clinical guiding tools.

In the context of the physical properties, surface roughness and microhardness of denture teeth were predicted by a study using an ANN model, and it was discovered that the experimental data were distributed along the ANN predicted line [22]. The same applies to the flexural strength of CAD/CAM resin composite blocks and debonding of composite resin crowns [23,24]. Thus, AI could be a useful experiential tool for physical properties in the future if it has proven accuracy and repeatability.

To help in the shade selection process, several studies have investigated AI technologies in shade matching and selection using different methods [5,8,26,29]. In fact, AI could replace the old tools and methods, as it showed greater accuracy in color reproduction [8]. One of the useful methods for shade matching is using patient photographs by dental technicians; however, this could be affected by the subjectivity of technicians. A neural network model was used to automate the shade predicting process by expert dental technicians identifying patient photographs’ tooth colors [27]. Some AI models were also compared among themselves, and the stable prediction ability of dental porcelain color matching from GA+BP was better than that of the BPNN model [5].

One study focused on predicting the prognosis of teeth based on 17 clinical determining factors selected from the Harvard School of Dental Medicine’s comprehensive treatment planning curriculum. Although this was one of the first attempts to utilize AI in predicting prognosis, AI-based ML algorithms were found to be useful tools for assessing prognosis [25]. AI models were recruited in the process of dental prosthesis design, such as for removable partial dentures, crowns, and maxillofacial prostheses [33,34,36]. AI could design clinically acceptable fracture-resistance crowns [34]. Regarding finish line detection, 1 AI model was able to mark the margin line without manual intervention [3].

Some systematic reviews were conducted related to this topic [2,47–49]. These systematic reviews included various numbers of studies; however, except 1 study [48], none included studies conducted in 2023. In contrast, the present systematic review included most of the recent studies that met the inclusion criteria. Therefore, the present review could be considered more updated than the previously published systematic reviews. Moreover, most of the previously published systematic reviews focused only on removable prosthodontic treatment [47–49]. However, the present review focused on the overall applications of AI in prosthodontics and assessed their performance in achieving prosthodontics tasks. As AI is continuously developing and the uses of AI also improve in the different fields, it is necessary to explore the uses and performances of different AI types day by day. Hence, even though a few systematic reviews have been conducted, there is still a need for further systematic reviews on the same topic to assess the future outcomes.

Even though AI has progressed dramatically, there are still some limitations. For instance, in designing lithium disilicate dental crowns, CAD design with humans showed better physical properties than knowledge-based AI. The heterogeneity of the current study methods in the selected studies, including variations in methodologies, AI models, and datasets used, poses a challenge in directly comparing the results and drawing conclusive findings. To mitigate this issue, increasing the size of the data set could generally lead to improved performance of DL models. However, more studies should be conducted to improve the performance of AI models to enhance their abilities in achieving prosthodontic tasks of diagnosis, to improve treatment planning and dental prostheses designing and manufacturing, and to improve clinical and laboratory productivity and efficiency.

Conclusions

While still developing, AI models have already shown promise in the areas of dental charting, tooth shade selection, automated restoration design, mapping the preparation finishing line, manufacturing casting optimization, predicting facial changes in patients wearing removable prostheses, and designing removable partial dentures. However, more studies should be conducted to strengthen AI outcomes in the field of prosthodontics.

Availability of Data and Materials

The datasets used and/or analyzed during the present study are available from the corresponding author upon reasonable request.

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750