Data Dentistry: How Data Are Changing Clinical Care and Research

Abstract

Data are a key resource for modern societies and expected to improve quality, accessibility, affordability, safety, and equity of health care. Dental care and research are currently transforming into what we term data dentistry, with 3 main applications: 1) medical data analysis uses deep learning, allowing one to master unprecedented amounts of data (language, speech, imagery) and put them to productive use. 2) Data-enriched clinical care integrates data from individual (e.g., demographic, social, clinical and omics data, consumer data), setting (e.g., geospatial, environmental, provider-related data), and systems level (payer or regulatory data to characterize input, throughput, output, and outcomes of health care) to provide a comprehensive and continuous real-time assessment of biologic perturbations, individual behaviors, and context. Such care may contribute to a deeper understanding of health and disease and a more precise, personalized, predictive, and preventive care. 3) Data for research include open research data and data sharing, allowing one to appraise, benchmark, pool, replicate, and reuse data. Concerns and confidence into data-driven applications, stakeholders’ and system’s capabilities, and lack of data standardization and harmonization currently limit the development and implementation of data dentistry. Aspects of bias and data-user interaction require attention. Action items for the dental community circle around increasing data availability, refinement, and usage; demonstrating safety, value, and usefulness of applications; educating the dental workforce and consumers; providing performant and standardized infrastructure and processes; and incentivizing and adopting open data and data sharing.

Augmented, Virtual and Mixed Reality in Dentistry: A Narrative Review on the Existing Platforms and Future Challenges

“…All the above studies proved the application of AI in the current dental field to diagnose and make prognoses through extrication of useful information from large amounts of medical records [65]. Again, data mining analysis performed on a bulk of restorative data of patients revealed that differences in the material of dental restorations serve as important factors determining the lifespan of a restoration [66]. Currently, studies applying machine learning based on artificial neural networks to dental treatment through analysis of dental magnetic resonance imaging, computed tomography, and cephalometric radiography are actively underway.…”

Dentistry and Interoperability

“…Yet, many such EHR systems promote free-text data entry, rather than a structured form facilitating stakeholders to store similar data in numerous locations, thereby making the data inconsistent and useless (Song et al 2013). These data in these isolated information systems need to be cracked and made available through secure access for reuse (Schwendicke and Krois 2022). Dentistry lags in the adoption of health information technology (IT) systems, but the initial move toward the structured and secure digitization is to procure an electronic dental record 1100175J DRXXX10.1177/00220345221100175Journal of Dental ResearchDentistry and Interoperability…”

“…All these newer technologies are being successfully utilized in astronomy, retail markets, automobiles, social media, web search engines, and even politics ( Murdoch and Detsky 2013 ). The costs of using and storing data are reducing, and it is considered an inexhaustible resource ( Schwendicke and Krois 2022 ). Estimates indicate that health care data will soon attain the levels of zettabytes and even yottabytes ( Glick 2015 ).…”

Deep Learning–Based Prediction of the 3D Postorthodontic Facial Changes

“…Artificial intelligence (AI) and machine learning have been introduced in dentistry, inducing a drastic change in the digital workflow (Schwendicke et al 2020; Mörch et al 2021; Schwendicke and Krois 2022). Artificial neural networks for image analysis and generation, including convolutional neural networks (CNNs), generative adversarial networks (GANs), and conditional GAN (cGAN) (Mirza and Osindero 2014; Goodfellow et al 2017; Isola et al 2017), have been proposed to support clinicians in an efficient, accurate, and reliable diagnosis (Casalegno et al 2019; Kim et al 2020; Yu et al 2020; Wang et al 2021).…”