Precision medicine using patient-specific modelling: state of the art and perspectives in dental practice

DentalSegmentator: Robust open source deep learning-based CT and CBCT image segmentation

OBJECTIVES

Segmentation of anatomical structures on dento-maxillo-facial (DMF) computed tomography (CT) or cone beam computed tomography (CBCT) scans is increasingly needed in digital dentistry. The main aim of this research was to propose and evaluate a novel open source tool called DentalSegmentator for fully automatic segmentation of five anatomical structures on DMF CT and CBCT scans: maxilla/upper skull, mandible, upper teeth, lower teeth, and the mandibular canal.

METHODS

A retrospective sample of 470 CT and CBCT scans was used as a training/validation set. The performance and generalizability of the tool was evaluated by comparing segmentations provided by experts and automatic segmentations in two hold-out test datasets: an internal dataset of 133 CT and CBCT scans acquired before orthognathic surgery and an external dataset of 123 CBCT scans randomly sampled from routine examinations in 5 institutions.

RESULTS

The mean overall results in the internal test dataset (n = 133) were a Dice similarity coefficient (DSC) of 92.2 ± 6.3 % and a normalised surface distance (NSD) of 98.2 ± 2.2 %. The mean overall results on the external test dataset (n = 123) were a DSC of 94.2 ± 7.4 % and a NSD of 98.4 ± 3.6 %.

CONCLUSIONS

The results obtained from this highly diverse dataset demonstrate that this tool can provide fully automatic and robust multiclass segmentation for DMF CT and CBCT scans. To encourage the clinical deployment of DentalSegmentator, the pre-trained nnU-Net model has been made publicly available along with an extension for the 3D Slicer software.

CLINICAL SIGNIFICANCE

DentalSegmentator open source 3D Slicer extension provides a free, robust, and easy-to-use approach to obtaining patient-specific three-dimensional models from CT and CBCT scans. These models serve various purposes in a digital dentistry workflow, such as visualization, treatment planning, intervention, and follow-up.

Developing Advanced Patient-Specific In Silico Models: A New Era in Biomechanical Analysis of Tooth Autotransplantation

INTRODUCTION

As personalized medicine advances, there is an escalating need for sophisticated tools to understand complex biomechanical phenomena in clinical research. Recognizing a significant gap, this study pioneers the development of patient-specific in silico models for tooth autotransplantation (TAT), setting a new standard for predictive accuracy and reliability in evaluating TAT outcomes.

METHODS

Development of the models relied on 6 consecutive cases of young patients (mean age 11.66 years ± 0.79), all undergoing TAT procedures. The development process involved creating detailed in silico replicas of patient oral structures, focusing on transplanting upper premolars to central incisors. These models underpinned finite element analysis simulations, testing various masticatory and traumatic scenarios.

RESULTS

The models highlighted critical biomechanical insights. The finite element models indicated homogeneous stress distribution in control teeth, contrasted by shape-dependent stress patterns in transplanted teeth. The surface deviation in the postoperative year for the transplanted elements showed a mean deviation of 0.33 mm (±0.28), significantly higher than their contralateral counterparts at 0.05 mm (±0.04).

CONCLUSIONS

By developing advanced patient-specific in silico models, we are ushering in a transformative era in TAT research and practice. These models are not just analytical tools; they are predictive instruments capturing patient uniqueness, including anatomical, masticatory, and tissue variables, essential for understanding biomechanical responses in TAT. This foundational work paves the way for future studies, where applying these models to larger cohorts will further validate their predictive capabilities and influence on TAT success parameters.

Surveying the landscape of diagnostic imaging in dentistry's future: Four emerging technologies with promise

BACKGROUND

Advances in digital radiography for both intraoral and panoramic imaging and cone-beam computed tomography have led the way to an increase in diagnostic capabilities for the dental care profession. In this article, the authors provide information on 4 emerging technologies with promise.

TYPES OF STUDIES REVIEWED

The authors feature the following: artificial intelligence in the form of deep learning using convolutional neural networks, dental magnetic resonance imaging, stationary intraoral tomosynthesis, and second-generation cone-beam computed tomography sources based on carbon nanotube technology and multispectral imaging. The authors review and summarize articles featuring these technologies.

RESULTS

The history and background of these emerging technologies are previewed along with their development and potential impact on the practice of dental diagnostic imaging. The authors conclude that these emerging technologies have the potential to have a substantial influence on the practice of dentistry as these systems mature. The degree of influence most likely will vary, with artificial intelligence being the most influential of the 4.

CONCLUSIONS AND PRACTICAL IMPLICATIONS

The readers are informed about these emerging technologies and the potential effects on their practice going forward, giving them information on which to base decisions on adopting 1 or more of these technologies. The 4 technologies reviewed in this article have the potential to improve imaging diagnostics in dentistry thereby leading to better patient care and heightened professional satisfaction.

Risk assessment of inferior alveolar nerve injury after wisdom tooth removal using 3D AI-driven models: A within-patient study

OBJECTIVE

To compare a three-dimensional (3D) artificial intelligence (AI)- driven model with panoramic radiography (PANO) and cone-beam computed tomography (CBCT) in assessing the risk of inferior alveolar nerve (IAN) injury after mandibular wisdom tooth (M3M) removal through a within-patient controlled trial.

METHODS

From a database of 6,010 patients undergoing M3M surgery, 25 patients met the inclusion criteria of bilateral M3M removal with postoperative unilateral IAN injury. In this within-patient controlled trial, preoperative PANO and CBCT images were available, while 3D-AI models of the mandibular canal and teeth were generated from the CBCT images using the Virtual Patient Creator AI platform (Relu BV, Leuven, Belgium). Five examiners, who were blinded to surgical outcomes, assessed the imaging modalities and assigned scores indicating the risk level of IAN injury (high, medium, or low risk). Sensitivity, specificity, and area under receiver operating curve (AUC) for IAN risk assessment were calculated for each imaging modality.

RESULTS

For IAN injury risk assessment after M3M removal, sensitivity was 0.87 for 3D-AI, 0.89 for CBCT versus 0.73 for PANO. Furthermore, the AUC and specificity values were 0.63 and 0.39 for 3D-AI, 0.58 and 0.28 for CBCT, and 0.57 and 0.41 for PANO, respectively. There was no statistically significant difference (p>0.05) among the imaging modalities for any diagnostic parameters.

CONCLUSION

This within-patient controlled trial study revealed that risk assessment for IAN injury after M3M removal was rather similar for 3D-AI, PANO, and CBCT, with a sensitivity for injury prediction reaching up to 0.87 for 3D-AI and 0.89 for CBCT.

CLINICAL SIGNIFICANCE

This within-patient trial is pioneering in exploring the application of 3D AI-driven models for assessing IAN injury risk after M3M removal. The present results indicate that AI-powered 3D models based on CBCT might facilitate IAN risk assessment of M3M removal.

Contemporary Role and Applications of Artificial Intelligence in Dentistry

Artificial Intelligence (AI) technologies play a significant role and significantly impact various sectors, including healthcare, engineering, sciences, and smart cities. AI has the potential to improve the quality of patient care and treatment outcomes while minimizing the risk of human error. Artificial Intelligence (AI) is transforming the dental industry, just like it is revolutionizing other sectors. It is used in dentistry to diagnose dental diseases and provide treatment recommendations. Dental professionals are increasingly relying on AI technology to assist in diagnosis, clinical decision-making, treatment planning, and prognosis prediction across ten dental specialties. One of the most significant advantages of AI in dentistry is its ability to analyze vast amounts of data quickly and accurately, providing dental professionals with valuable insights to enhance their decision-making processes. The purpose of this paper is to identify the advancement of artificial intelligence algorithms that have been frequently used in dentistry and assess how well they perform in terms of diagnosis, clinical decision-making, treatment, and prognosis prediction in ten dental specialties; dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral & maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, prosthodontics, and digital dentistry in general. We will also show the pros and cons of using AI in all dental specialties in different ways. Finally, we will present the limitations of using AI in dentistry, which made it incapable of replacing dental personnel, and dentists, who should consider AI a complimentary benefit and not a threat.

Ethical considerations on artificial intelligence in dentistry: A framework and checklist

OBJECTIVE

Artificial Intelligence (AI) refers to the ability of machines to perform cognitive and intellectual human tasks. In dentistry, AI offers the potential to enhance diagnostic accuracy, improve patient outcomes and streamline workflows. The present study provides a framework and a checklist to evaluate AI applications in dentistry from this perspective.

METHODS

Lending from existing guidance documents, an initial draft of the checklist and an explanatory paper were derived and discussed among the groups members.

RESULTS

The checklist was consented to in an anonymous voting process by 29 Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health's members. Overall, 11 principles were identified (diversity, transparency, wellness, privacy protection, solidarity, equity, prudence, law and governance, sustainable development, accountability, and responsibility, respect of autonomy, decision-making).

CONCLUSIONS

Providers, patients, researchers, industry, and other stakeholders should consider these principles when developing, implementing, or receiving AI applications in dentistry.

CLINICAL SIGNIFICANCE

While AI has become increasingly commonplace in dentistry, there are ethical concerns around its usage, and users (providers, patients, and other stakeholders), as well as the industry should consider these when developing, implementing, or receiving AI applications based on comprehensive framework to address the associated ethical challenges.

Real-time simulation of the transplanted tooth using model order reduction

The biomechanics of transplanted teeth remain poorly understood due to a lack of models. In this context, finite element (FE) analysis has been used to evaluate the influence of occlusal morphology and root form on the biomechanical behavior of the transplanted tooth, but the construction of a FE model is extremely time-consuming. Model order reduction (MOR) techniques have been used in the medical field to reduce computing time, and the present study aimed to develop a reduced model of a transplanted tooth using the higher-order proper generalized decomposition method. The FE model of a previous study was used to learn von Mises root stress, and axial and lateral forces were used to simulate different occlusions between 75 and 175N. The error of the reduced model varied between 0.1% and 5.9% according to the subdomain, and was the highest for the highest lateral forces. The time for the FE simulation varied between 2.3 and 7.2 h. In comparison, the reduced model was built in 17s and interpolation of new results took approximately 2.10^-2s. The use of MOR reduced the time for delivering the root stresses by a mean 5.9 h. The biomechanical behavior of a transplanted tooth simulated by FE models was accurately captured with a significant decrease of computing time. Future studies could include using jaw tracking devices for clinical use and the development of more realistic real-time simulations of tooth autotransplantation surgery.

Stronger than Ever: Multifilament Fiberglass Posts Boost Maxillary Premolar Fracture Resistance

This paper investigates the influence of cavity configuration and post-endodontic restoration on the fracture resistance, failure mode and stress distribution of premolars by using a method of fracture failure test and finite elements analysis (FEA) coupled to Weibull analysis (WA). One hundred premolars were divided into one control group (G_contr) (n = 10) and three experimental groups, according to the post-endodontic restoration (n = 30), G₁, restored using composite, G₂, restored using single fiber post and G₃, restored using multifilament fiberglass posts (m-FGP) without post-space preparation. Each experimental group was divided into three subgroups according to the type of coronal cavity configuration (n = 10): G_1O, G_2O, and G_3O with occlusal (O) cavity configuration; G_1MO, G_2MO, and G_3MO with mesio-occlusal (MO); and G_1MOD, G_2MOD, and G_3MOD with mesio-occluso-distal (MOD). After thermomechanical aging, all the specimens were tested under compression load, and failure mode was determined. FEA and WA supplemented destructive tests. Data were statistically analyzed. Irrespective of residual tooth substance, G₁ and G₂ exhibited lower fracture resistance than G_contr (p < 0.05), whereas G₃ showed no difference compared to G_contr (p > 0.05). Regarding the type of restoration, no difference was highlighted between G_1O and G_2O, G_1MO and G_2MO, or G_1MOD and G_2MOD (p > 0.05), whereas G_3O, G_3MO, and G_3MOD exhibit higher fracture resistance (p < 0.05) than G_1O and G_2O, G_1MO and G_2MO, and G_1MOD and G_2MOD, respectively. Regarding cavity configuration: in G₁ and G_2, G_1O and G_2O exhibited higher fracture resistance than G_1MOD and G_2MOD, respectively (p < 0.05). In G₃, there was no difference among G_3O, G_3MO and G_3MOD (p > 0.05). No difference was found among the different groups and subgroups regarding the failure mode. After aging, premolars restored with multifilament fiberglass posts demonstrated fracture resistance values comparable to those of an intact tooth, irrespective of the different type of cavity configuration.

Do the Mechanical Properties of Calcium-Silicate-Based Cements Influence the Stress Distribution of Different Retrograde Cavity Preparations?

The aim of the present study was to investigate the influence of the mechanical properties of three different calcium-silicate-based cements on the stress distribution of three different retrograde cavity preparations. Biodentine™ "BD", MTA Biorep "BR", and Well-Root™ PT "WR" were used. The compression strengths of ten cylindrical samples of each material were tested. The porosity of each cement was investigated by using micro-computed X-ray tomography. Finite element analysis (FEA) was used to simulate three retrograde conical cavity preparations with an apical diameter of 1 mm (Tip I), 1.4 mm (Tip II), and 1.8 mm (Tip III) after an apical 3 mm resection. BR demonstrated the lowest compression strength values (17.6 ± 5.5 MPa) and porosity percentages (0.57 ± 0.14%) compared to BD (80 ± 17 MPa-1.22 ± 0.31%) and WR (90 ± 22 MPa-1.93 ± 0.12%) (p < 0.05). FEA demonstrated that the larger cavity preparation demonstrated higher stress distribution in the root whereas stiffer cement demonstrated lower stress in the root but higher stress in the material. We can conclude that a respected root end preparation associated with cement with good stiffness could offer optimal endodontic microsurgery. Further studies are needed to define the adapted cavity diameter and cement stiffness in order to have optimal mechanical resistance with less stress distribution in the root.

Determination of the compressive modulus of elasticity of periodontal ligament derived from human first premolars

Purpose

There are two commonly cited modulus of elasticity of the human periodontal ligament (E_PDL), i.e., 6.89 ✕ 10^-5 GPa (E1) and 6.89 ✕ 10^-2 GPa (E2), which are exactly 1000-fold different from each other. This study aims to clarify the ambiguity of the two E_PDL used for simulations and determine a more accurate E_PDL value of human first premolars using experimental and simulation approaches.

Methods

Numerical simulations using finite element analysis were performed to analyze PDL deformation under an average Asian occlusal force. To confirm the results, simple and multi-component, true-scale 3D models of a human first premolar were used in the simulations. Finally, a compression test using a universal testing machine on PDL specimens was conducted to identify the compressive E_PDL of human first premolars.

Results

The simulation results from both models revealed that E1 was inaccurate, because it resulted in excessive PDL deformation under the average occlusal force, which should not occur during mastication. Although the E2 did not lead to excessive PDL deformation, it was obtained by an error in unit conversion with no scientific backing. In contrast, the compression test results indicated that the compressive E_PDL was 9.64 ✕ 10^-4 GPa (E3). In the simulation, E3 did not cause excessive PDL deformation.

Conclusion

The simulation results demonstrated that both commonly cited E_PDL values (E1 and E2) were incorrect. Based on the experimental and simulation results, the average compressive E_PDL of 9.64 ✕ 10^-4 GPa is proposed as a more accurate value for human first premolars.

Clinical significance

The proposed more accurate E_PDL would contribute to more precise and reliable FEA simulation results and provide a better understanding of the stress distribution and deformation of dental materials, which will be beneficial to precision dentistry, orthodontics and restoration designs.

Artificial intelligence for sustainable oral healthcare

OBJECTIVES

Oral health is grounded in the United National (UN) 2030 Agenda for Sustainable Developement and its 17 Goals (SDGs), in particular SDG 3 (Ensure healthy lives and promote well-being for all at all ages). The World Health Organization (WHO) Global Strategy on Oral Health calls for prioritizing environmentally sustainable and less invasive oral health care, and planetary health. Artificial Intelligence (AI) has the potential to power the next generation of oral health services and care, however its relationship with the broader UN and WHO concepts of sustainability remains poorly defined and articulated. We review the double-edged relationships between AI and oral health, to suggest actions that promote a sustainable deployment of AI for oral health.

DATA

Concepts regarding AI, sustainability and sustainable development were identified and defined. A review of several double-edged relationship between AI and SDGs were exposed for the field of Oral Health.

SOURCES

Medline and international declarations of the WHO, the UN and the World Dental Federation (FDI) were screened.

STUDY SELECTION

One the one hand, AI may reduce transportation, optimize care delivery (SDG 3 "Good Health and Well-Being", SDG 13 "Climate Action"), and increase accessibility of services and reduce inequality (SDG 10 "Reduced Inequalities", SDG 4 "Quality Education"). On the other hand, the deployment, implementation and maintenance of AI require significant resources (SDG 12 "Responsible Consumption and Production"), and costs for AI may aggravate inequalities. Also, AI may be biased, reinforcing inequalities (SDG 10) and discrimination (SDG 5), and may violate principles of security, privacy and confidentiality of personal information (SDG 16).

CONCLUSIONS

Systematic assessment of the positive impact and adverse effects of AI on sustainable oral health may help to foster the former and curb the latter based on evidence.

CLINICAL SIGNIFICANCE

If sustainability imperatives are actively taken into consideration, the community of oral health professionals should then employ AI for improving effectiveness, efficiency, and safety of oral healthcare; strengthen oral health surveillance; foster education and accessibility of care; ensure fairness, transparency and governance of AI for oral health; develop legislation and infrastructure to expand the use of digital health technologies including AI.

Multifactorial Analysis of Endodontic Microsurgery Using Finite Element Models

Background: The present study aimed to classify the relative contributions of four biomechanical factors—the root-end filling material, the apical preparation, the root resection length, and the bone height—on the root stresses of the resected premolar. Methods: A design of experiments approach based on a defined subset of factor combinations was conducted to calculate the influence of each factor and their interactions. Sixteen finite element models were created and analyzed using the von Mises stress criterion. The robustness of the design of experiments was evaluated with nine supplementary models. Results: The current study showed that the factors preparation and bone height had a high influence on root stresses. However, it also revealed that nearly half of the biomechanical impact was missed without considering interactions between factors, particularly between resection and preparation. Conclusions: Design of experiments appears to be a valuable strategy to classify the contributions of biomechanical factors related to endodontics. Imagining all possible interactions and their clinical impact is difficult and can require relying on one’s own experience. This study proposed a statistical method to quantify the mechanical risk when planning apicoectomy. A perspective could be to integrate the equation defined herein in future software to support decision-making.