Precision dentistry—what it is, where it fails (yet), and how to get there

Implementing a Secondary Database as a Teaching Tool to Improve Genomic Literacy Among Dental Students

BACKGROUND

Recent advancements in precision medicine and precision dentistry have necessitated genomic literacy in healthcare professionals. Both the knowledge of genetics and data in primary biological databases are rapidly expanding beyond what is presented in textbooks. Dental students are often unfamiliar with the growing field of biological data and the tools used to analyse and interpret genetic information.

APPROACH

To improve genomic literacy among dental students, we incorporated 'Bioinformatics for Dentistry', a dental-specific secondary database, as a teaching tool in the first year of the Doctor of Dental Surgery (DDS) program. This study aims to explore students' perspectives on using a secondary database as a tool for teaching and learning.

EVALUATION

A convergent, parallel mixed-method study was conducted to explore student perception of the database as a teaching tool. Qualitative and quantitative data were collected from students' reflection assignments and surveys. Descriptive statistics and manifest content analysis were applied to analyse the survey data and reflection assignments, respectively. All (100%) students (n = 32) completed the assignment with reflective answers; 38% (n = 12) of the class completed the voluntary survey. Survey participants indicated that 'Bioinformatics for Dentistry' was easy to navigate and helpful for learning the genetics of tooth development. Codes from qualitative data were grouped into three categories, representing the benefit of the secondary database attributed by the students.

IMPLICATIONS

Dental students positively valued the use of 'Bioinformatics for Dentistry' to learn the genetics of tooth development. This secondary database can improve genomic literacy to meet the challenge of the postgenomic era.

Aptamers in dentistry: diagnosis, therapeutics, and future perspectives

Oral health is essential to general health. The diagnosis of dental diseases and treatment planning of dental care need to be straightforward and accurate. Recent studies have reported the use of aptamers in dentistry to achieve a simple diagnosis and facilitate therapy. Aptamers comprise nucleic acid sequences that possess a strong affinity for their target. Synthesized chemically, aptamers have several advantages, including smaller size, higher stability, and lower immunogenicity compared with monoclonal antibodies. They can be used to detect biomarkers in saliva and the presence of various pathogens, or can be used as a targeted drug delivery system for disease treatment. This review highlights current research on aptamers for dental care, especially the recent progress in oral disease diagnosis and therapeutics. The challenges and unresolved problems faced by the clinical use of aptamers are also discussed. In the future, the clinical applications of aptamers will be further extended to include, for example, dental indications and regenerative dentistry.

Prediction models for the complication incidence and survival rate of dental implants-a systematic review and critical appraisal

PURPOSE

This systematic review aims to assess the performance, methodological quality and reporting transparency in prediction models for the dental implant's complications and survival rates.

METHODS

A literature search was conducted in PubMed, Web of Science, and Embase databases. Peer-reviewed studies that developed prediction models for dental implant's complications and survival rate were included. Two reviewers independently evaluated the risk of bias and reporting quality using the PROBAST and TRIPOD guidelines. The performance of the models were also compared in this study. The review followed the PRISMA guidelines and was registered with PROSPERO (CRD42019122274).

RESULTS

The initial screening yielded 1769 publications, from which 14 studies featuring 43 models were selected. Four of the 14 studies predicted peri-implantitis as the most common outcome. Three studies predicted the marginal bone loss, two predicted suppuration of peri-implant tissue. The remaining five models predicted the implant loss, osseointergration or other complication. Common predictors included implant position, length, patient age, and a history of periodontitis. Sixteen models showed good to excellent discrimination (AUROC >0.8), but only three had undergone external validation. A significant number of models lacked model presentation. Most studies had a high or unclear risk of bias, primarily due to methodological limitation. The included studies conformed to 18-27 TRIPOD checklist items.

CONCLUSIONS

The current prediction models for dental implant complications and survival rate have limited methodological quality and external validity. There is a need for enhanced reliability, generalizability, and clinical applicability in future models.

Advanced Imaging in Dental Research: From Gene Mapping to AI Global Data

Advances in imaging technologies combined with artificial intelligence (AI) are transforming dental, oral, and craniofacial research. This editorial highlights breakthroughs ranging from gene expression mapping to visualizing the availability of global AI data, providing new insights into biological complexity and clinical applications.

Artificial intelligence and personalized diagnostics in periodontology: A narrative review

Periodontal diseases pose a significant global health burden, requiring early detection and personalized treatment approaches. Traditional diagnostic approaches in periodontology often rely on a "one size fits all" approach, which may overlook the unique variations in disease progression and response to treatment among individuals. This narrative review explores the role of artificial intelligence (AI) and personalized diagnostics in periodontology, emphasizing the potential for tailored diagnostic strategies to enhance precision medicine in periodontal care. The review begins by elucidating the limitations of conventional diagnostic techniques. Subsequently, it delves into the application of AI models in analyzing diverse data sets, such as clinical records, imaging, and molecular information, and its role in periodontal training. Furthermore, the review also discusses the role of research community and policymakers in integrating personalized diagnostics in periodontal care. Challenges and ethical considerations associated with adopting AI-based personalized diagnostic tools are also explored, emphasizing the need for transparent algorithms, data safety and privacy, ongoing multidisciplinary collaboration, and patient involvement. In conclusion, this narrative review underscores the transformative potential of AI in advancing periodontal diagnostics toward a personalized paradigm, and their integration into clinical practice holds the promise of ushering in a new era of precision medicine for periodontal care.

Artificial Intelligence in Orthodontics: Critical Review

With increasing digitalization in orthodontics, certain orthodontic manufacturing processes such as the fabrication of indirect bonding trays, aligner production, or wire bending can be automated. However, orthodontic treatment planning and evaluation remains a specialist's task and responsibility. As the prediction of growth in orthodontic patients and response to orthodontic treatment is inherently complex and individual, orthodontists make use of features gathered from longitudinal, multimodal, and standardized orthodontic data sets. Currently, these data sets are used by the orthodontist to make informed, rule-based treatment decisions. In research, artificial intelligence (AI) has been successfully applied to assist orthodontists with the extraction of relevant data from such data sets. Here, AI has been applied for the analysis of clinical imagery, such as automated landmark detection in lateral cephalograms but also for evaluation of intraoral scans or photographic data. Furthermore, AI is applied to help orthodontists with decision support for treatment decisions such as the need for orthognathic surgery or for orthodontic tooth extractions. One major challenge in current AI research in orthodontics is the limited generalizability, as most studies use unicentric data with high risks of bias. Moreover, comparing AI across different studies and tasks is virtually impossible as both outcomes and outcome metrics vary widely, and underlying data sets are not standardized. Notably, only few AI applications in orthodontics have reached full clinical maturity and regulatory approval, and researchers in the field are tasked with tackling real-world evaluation and implementation of AI into the orthodontic workflow.

Breakthroughs in Oral and Maxillofacial Surgery

In the field of oral and maxillofacial surgery, continuous advances have ushered in a new era of innovation, profoundly influencing this branch of medicine [...].

A review on artificial intelligence for the diagnosis of fractures in facial trauma imaging

Patients with facial trauma may suffer from injuries such as broken bones, bleeding, swelling, bruising, lacerations, burns, and deformity in the face. Common causes of facial-bone fractures are the results of road accidents, violence, and sports injuries. Surgery is needed if the trauma patient would be deprived of normal functioning or subject to facial deformity based on findings from radiology. Although the image reading by radiologists is useful for evaluating suspected facial fractures, there are certain challenges in human-based diagnostics. Artificial intelligence (AI) is making a quantum leap in radiology, producing significant improvements of reports and workflows. Here, an updated literature review is presented on the impact of AI in facial trauma with a special reference to fracture detection in radiology. The purpose is to gain insights into the current development and demand for future research in facial trauma. This review also discusses limitations to be overcome and current important issues for investigation in order to make AI applications to the trauma more effective and realistic in practical settings. The publications selected for review were based on their clinical significance, journal metrics, and journal indexing.

Features and networks of the mandible on computed tomography

The mandible or lower jaw is the largest and hardest bone in the human facial skeleton. Fractures of the mandible are reported to be a common facial trauma in emergency medicine and gaining insights into mandibular morphology in different facial types can be helpful for trauma treatment. Furthermore, features of the mandible play an important role in forensics and anthropology for identifying gender and individuals. Thus, discovering hidden information of the mandible can benefit interdisciplinary research. Here, for the first time, a method of artificial intelligence-based nonlinear dynamics and network analysis are used for discovering dissimilar and similar radiographic features of mandibles between male and female subjects. Using a public dataset of 10 computed tomography scans of mandibles, the results suggest a difference in the distribution of spatial autocorrelation between genders, uniqueness in network topologies among individuals and shared values in recurrence quantification.

Wearables for personalized monitoring of masticatory muscle activity - opportunities, challenges, and the future

Wearable devices are worn on or remain in close proximity of the human body. The use of wearable devices specific to the orofacial region is steadily increasing. Orofacial applications of wearable devices include supplementing diagnosis, tracking treatment progress, monitoring patient compliance, and understanding oral parafunctional behaviours. In this short communication, the role of wearable devices in advancing personalized dental medicine are highlighted with a specific focus on masticatory muscle activity monitoring in naturalistic settings. Additionally, challenges, opportunities, as well as future research areas for successful use of wearable devices for precise, personalized care of muscle disorders are discussed.

Deep learning: A primer for dentists and dental researchers

OBJECTIVES

Despite deep learning's wide adoption in dental artificial intelligence (AI) research, researchers from other dental fields and, more so, dental professionals may find it challenging to understand and interpret deep learning studies, their employed methods, and outcomes. The objective of this primer is to explain the basic concept of deep learning. It will lay out the commonly used terms, and describe different deep learning approaches, their methods, and outcomes.

METHODS

Our research is based on the latest review studies, medical primers, as well as the state-of-the-art research on AI and deep learning, which have been gathered in the current study.

RESULTS

In this study, a basic understanding of deep learning models and various approaches to deep learning is presented. An overview of data management strategies for deep learning projects is presented, including data collection, data curation, data annotation, and data preprocessing. Additionally, we provided a step-by-step guide for completing a real-world project.

CONCLUSION

Researchers and clinicians can benefit from this study by gaining insight into deep learning. It can be used to critically appraise existing work or plan new deep learning projects.

CLINICAL SIGNIFICANCE

This study may be useful to dental researchers and professionals who are assessing and appraising deep learning studies within the field of dentistry.

Personalized Oral and Dental Care

Recent advances in genomics, data analytics technologies, and biotechnology have been unprecedented, ushering in a new era of healthcare in which interventions are increasingly tailored to individual patients [...].

Integrating P4 Medicine in Teledentistry and M-Health in Oral, Dental, and Periodontal Care

Given that dental practice is currently based on the "average" patient, providing therapeutic and rehabilitative interventions rather than preventive measures [...].

Personalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging

Personalized medicine refers to the tailoring of diagnostics and therapeutics to individuals based on one's biological, social, and behavioral characteristics. While personalized dental medicine is still far from being a reality, advanced artificial intelligence (AI) technologies with improved data analytic approaches are expected to integrate diverse data from the individual, setting, and system levels, which may facilitate a deeper understanding of the interaction of these multilevel data and therefore bring us closer to more personalized, predictive, preventive, and participatory dentistry, also known as P4 dentistry. In the field of dentomaxillofacial imaging, a wide range of AI applications, including several commercially available software options, have been proposed to assist dentists in the diagnosis and treatment planning of various dentomaxillofacial diseases, with performance similar or even superior to that of specialists. Notably, the impact of these dental AI applications on treatment decision, clinical and patient-reported outcomes, and cost-effectiveness has so far been assessed sparsely. Such information should be further investigated in future studies to provide patients, providers, and healthcare organizers a clearer picture of the true usefulness of AI in daily dental practice.

Anesthetic-, irrigation- and pain-free dentistry? The case for a femtosecond laser enabled intraoral robotic device

By leveraging ultrashort pulse laser and micro-electromechanical systems (MEMS) technologies, we are developing a miniaturized intraoral dental robotic device that clamps onto teeth, is remotely controlled, and equipped with a focusing and scanning system to perform efficient, fast, and ultra-precise laser treatments of teeth and dental restorative materials. The device will be supported by a real-time monitoring system for visualization and diagnostic analysis with appropriate digital controls. It will liberate dentists from repetitive manual operations, physical strain and proximity to the patient's oro-pharyngal area that potentially contains infectious agents. The technology will provide patients with high-accuracy, minimally invasive and pain-free treatment. Unlike conventional lasers, femtosecond lasers can ablate all materials without generating heat, thus negating the need for water irrigation, allowing for a clear field of view, and lowering cross-infection hazards. Additionally, dentists can check, analyze, and perform precise cutting of tooth structure with automatic correction, reducing human error. Performing early-stage diagnosis and intervention remotely will be possible through units installed at schools, rural health centers and aged care facilities. Not only can the combination of femtosecond lasers, robotics and MEMS provide practical solutions to dentistry's enduring issues by allowing more precise, efficient, and predictable treatment, but it will also lead to improving the overall access to oral healthcare for communities at large.

Gingival shape analysis using surface curvature estimation of the intraoral scans

Background

Despite many advances in dentistry, no objective and quantitative method is available to evaluate gingival shape. The surface curvature of the optical scans represents an unexploited possibility. The present study aimed to test surface curvature estimation of intraoral scans for objective evaluation of gingival shape.

Methods

The method consists of four main steps, i.e., optical scanning, surface curvature estimation, region of interest (ROI) definition, and gingival shape analysis. Six different curvature measures and three different diameters were tested for surface curvature estimation on central (n = 78) and interdental ROI (n = 88) of patients with advanced periodontitis to quantify gingiva with a novel gingival shape parameter (GS). The reproducibility was evaluated by repeating the method on two consecutive intraoral scans obtained with a scan-rescan process of the same patient at the same time point (n = 8).

Results

Minimum and mean curvature measures computed at 2 mm diameter seem optimal GS to quantify shape at central and interdental ROI, respectively. The mean (and standard deviation) of the GS was 0.33 ± 0.07 and 0.19 ± 0.09 for central ROI using minimum, and interdental ROI using mean curvature measure, respectively, computed at a diameter of 2 mm. The method’s reproducibility evaluated on scan-rescan models for the above-mentioned ROI and curvature measures was 0.02 and 0.01, respectively.

Conclusions

Surface curvature estimation of the intraoral optical scans presents a precise and highly reproducible method for the objective gingival shape quantification enabling the detection of subtle changes. A careful selection of parameters for surface curvature estimation and curvature measures is required.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-022-02322-y.

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

The dental practice has largely evolved in the last 50 years following a better understanding of the biomechanical behaviour of teeth and its supporting structures, as well as developments in the fields of imaging and biomaterials. However, many patients still encounter treatment failures; this is related to the complex nature of evaluating the biomechanical aspects of each clinical situation due to the numerous patient-specific parameters, such as occlusion and root anatomy. In parallel, the advent of cone beam computed tomography enabled researchers in the field of odontology as well as clinicians to gather and model patient data with sufficient accuracy using image processing and finite element technologies. These developments gave rise to a new precision medicine concept that proposes to individually assess anatomical and biomechanical characteristics and adapt treatment options accordingly. While this approach is already applied in maxillofacial surgery, its implementation in dentistry is still restricted. However, recent advancements in artificial intelligence make it possible to automate several parts of the laborious modelling task, bringing such user-assisted decision-support tools closer to both clinicians and researchers. Therefore, the present narrative review aimed to present and discuss the current literature investigating patient-specific modelling in dentistry, its state-of-the-art applications, and research perspectives.