About this Research Topic
In dentistry, digital twins are virtual duplicates of patients' oral structures and conditions, crafted using advanced imaging and modelling technologies. These digital replicas empower dentists to acquire a thorough and detailed comprehension of a patient's dental anatomy, facilitating more precise diagnosis, treatment planning, and individualized care. Generated through diverse imaging methods like CBCT, intraoral, and digital scans, digital twins allow dentists and researchers to simulate various treatment scenarios, evaluate the impact of interventions, and communicate efficiently with patients regarding their oral health. This pioneering technology not only heightens accuracy in dental procedures but also fosters patient engagement and well-informed decision-making, ultimately contributing to enhanced overall dental healthcare outcomes.
Digital twin modelling in dentistry aims to create virtual replicas of patients' oral structures and conditions, allowing for precise simulations and analysis. The primary goal is to enhance clinical practice by providing dentists with comprehensive insights into patient-specific dental anatomy, treatment planning, and outcomes prediction. By integrating digital imaging technologies such as intraoral scanners and cone beam computed tomography (CBCT), dentists can generate accurate 3D models of teeth and surrounding tissues and potentially integrate them in more complex biomechanical models. This can enable practitioners to simulate various treatment scenarios, optimize procedures, and anticipate potential challenges before implementing interventions in real clinical settings. Ultimately, digital twin modelling empowers dentists to deliver more personalized and efficient care, leading to improved patient outcomes and satisfaction in the day-to-day clinical practice as well as in research and development settings.
This Research Topic encompasses digital dentistry and its subfields, including
• Develop material models for oral tissues to capture nonlinear mechanical behavior under various loading conditions.
• Integrate computational models with data from in vitro studies, clinical observations, and advanced imaging techniques for validation and predictive capabilities.
• Customize treatment planning and predict outcomes by integrating patient-specific data into biomechanical models.
• Explore novel simulation techniques (e.g., fluid-structure interaction, multi-physics) for investigating complex phenomena in oral biomechanics.
• Enhance computational efficiency and scalability of modeling platforms for real-time simulations and high-throughput analyses. Integrate model order reduction techniques and AI algorithms (e.g., machine learning, deep learning) for computational efficiency, predictive accuracy, and automation in oral biomechanical simulations.
Digital twin modelling in dentistry aims to create virtual replicas of patients' oral structures and conditions, allowing for precise simulations and analysis. The primary goal is to enhance clinical practice by providing dentists with comprehensive insights into patient-specific dental anatomy, treatment planning, and outcomes prediction. By integrating digital imaging technologies such as intraoral scanners and cone beam computed tomography (CBCT), dentists can generate accurate 3D models of teeth and surrounding tissues and potentially integrate them in more complex biomechanical models. This can enable practitioners to simulate various treatment scenarios, optimize procedures, and anticipate potential challenges before implementing interventions in real clinical settings. Ultimately, digital twin modelling empowers dentists to deliver more personalized and efficient care, leading to improved patient outcomes and satisfaction in the day-to-day clinical practice as well as in research and development settings.
This Research Topic encompasses digital dentistry and its subfields, including
• Develop material models for oral tissues to capture nonlinear mechanical behavior under various loading conditions.
• Integrate computational models with data from in vitro studies, clinical observations, and advanced imaging techniques for validation and predictive capabilities.
• Customize treatment planning and predict outcomes by integrating patient-specific data into biomechanical models.
• Explore novel simulation techniques (e.g., fluid-structure interaction, multi-physics) for investigating complex phenomena in oral biomechanics.
• Enhance computational efficiency and scalability of modeling platforms for real-time simulations and high-throughput analyses. Integrate model order reduction techniques and AI algorithms (e.g., machine learning, deep learning) for computational efficiency, predictive accuracy, and automation in oral biomechanical simulations.
Keywords: Digital twins, dentistry, digital dentistry, biomechanics, in-silico modeling, patient-specific modeling
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