Opportunities through the Bitonte College of Dentistry

Ehsanul Hoque Apu, Ph.D. and Nazeeba Siddika, BDS, MPH, Ph.D. – 1

Surrogate Modeling for Patient-Specific Craniofacial Reconstruction: Bridging the Gap Between High-Fidelity FEA and Real-Time Prediction

Patient-specific finite element analysis (FEA) has emerged as the gold standard for
characterizing the heterogeneous and anisotropic mechanical response of craniofacial
structures. However, the computational burden associated with high-fidelity simulations
derived from multi-scale imaging modalities such as Cone Beam CT (CBCT) and Micro-CT— remains a significant barrier to routine clinical integration. To resolve the latency between accurate biomechanical assessment and the real-time demands of surgical planning, the field is increasingly pivoting toward surrogate modeling. This review critically examines the state-of-the-art in surrogate modeling architectures applied to craniofacial biomechanics. We delineate the end-to-end computational pipeline, traversing from image segmentation and mesh generation (e.g., 3D Slicer) to high-fidelity physics-based solution schemes (e.g., MOOSE, Abaqus). The review categorizes current strategies into data-driven machine learning approaches (e.g., Deep Neural Networks, Gaussian Processes) and projection based Reduced Order Models (e.g., POD-Galerkin), with specific focus on emerging hybrid paradigms like Physics-Informed  Neural Networks (PINNs). We evaluate the fidelitye Viciency trade-oV inherent in these models, particularly regarding their capacity to handle complex topology changes during osteotomy and reconstruction. Furthermore, we discuss the pivotal role of open-source frameworks in democratizing access to these technologies and facilitating reproducible workflows for generating predictive Digital Twins. We conclude that combining high-quality validation with reliable surrogate inference is essential for the development of the next generation of clinical decision support systems.

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Ehsanul Hoque Apu, Ph.D. and Nazeeba Siddika, BDS, MPH, Ph.D. – 2

Toward the Dental Digital Twin: Integrating Image-Based Finite Element Modeling and Machine Learning for Predicting Peri-Implant Disease Progression.

Failure of dental implant failure is a complex process caused by the interplay of mechanical stresses and biological issues such as peri-implantitis. While conventional diagnostics mainly respond to problems, computer modeling provides a proactive way to predict failure mechanisms. Nonetheless, traditional Finite Element Analysis (FEA) has primarily been confined to static, linear-elastic models, which do not adequately capture the evolving, time-dependent nature of oral health conditions. This review explores the latest computational strategies for predicting implant failures, highlighting the evolution from basic models to sophisticated, dynamic simulations, with particular emphasis on the progression from medical imaging to predictive analytics. It details the utilization of Imageto- Mesh pipelines, multiphysics simulations, and surrogate modeling techniques. The review also explains how CBCT and Micro-CT scans—via tools such as 3D Slicer—are employed to develop patient-specific tissue models and describes the application of opensource platforms like MOOSE to integrate mechanical stress calculations with biological processes, thus simulating bone remodeling over time. Additionally, it underscores the role of Machine Learning in expediting complex FEA computations, thereby enabling near real time predictive capabilities. The concept of “Virtual Laboratory” is introduced, wherein parameters are optimized and analyzed through computational simulations. Special attention is given to open-source workflows that link physical models to data-driven algorithms, using Python and MATLAB. The integration of high-quality imaging, multiphysics FEA, and artificial intelligence is propelling the development of the Dental Digital Twin—a comprehensive digital model of a patient’s oral health status. The authors believe that next generation models will not only show current stress distributions but also forecast progressive bone loss, thereby enhancing surgical planning and proactive intervention.

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Ehsanul Hoque Apu, Ph.D. and Nazeeba Siddika, BDS, MPH, Ph.D. – 3

Computational Strategies for Real-Time Biomechanical Prediction in Craniofacial Injuries Using Physics- Informed Neural Networks: A Mini-Review.

Traditional assessment of complex craniofacial trauma lacks the quantitative accuracy needed for optimal treatment planning. While the Finite Element Method (FEM) provides high-fidelity biomechanical analysis, its computational cost limits clinical use in time sensitive situations. Physics-based machine learning overcomes this by creating eEicient surrogate models that incorporate the governing laws of mechanics, allowing near real-time prediction of fracture patterns and post-operative outcomes with high physical accuracy. This paper discusses key computational methods for combining FEM and physics-informed machine learning (ML), presenting a schematic for a clinical workflow and examining the current limitations of this approach. This work emphasizes a crucial step toward translating advanced computational tools from research into personalized, real-time surgical planning.

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Ehsanul Hoque Apu, Ph.D. and Nazeeba Siddika, BDS, MPH, Ph.D. – 4

Preclinical Imaging of Temporomandibular Joint Microarchitecture

The temporomandibular joint (TMJ) is a complex load-bearing articulation comprising the mandibular condyle, temporal bone, and an interposed fibrocartilaginous disc. Its intricate microstructure and biomechanical demands make it particularly susceptible to temporomandibular disorders (TMDs), which aCect a significant portion of the population and often lead to chronic orofacial pain and functional limitations. Despite decades of research, the internal architecture of the TMJ—especially the disc and osteochondral interface—remains insuCiciently characterized due to its deep anatomical location and heterogeneous tissue composition. Recent advances in high resolution imaging modalities, particularly micro-computed tomography (microCT), have enabled detailed three-dimensional (3D) visualization of mineralized and soft tissues within the TMJ. When integrated with complementary techniques such as high-resolution magnetic resonance imaging (MRI) and histological validation, these approaches oCer unprecedented insights into the spatial organization, mineral density, and structural integrity of the joint. This review highlights the potential of preclinical imaging to  quantitatively assess TMJ microstructure, paving the way for improved understanding  of TMD pathophysiology and the development of targeted therapeutic strategies.

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David Kay, Ph.D.

Tooth shape across ontogeny in crocodylian species with varying degrees of heterodonty

In some vertebrates with socketed dentitions, alveoli (sockets) and tooth cusps are unusually related. For example, tooth cusp offset in some muroid rodents is the result of tooth bud-jaw interactions: parallel or offset cusps can be induced to vary developmentally based on experimentally manipulated lateral alveolar thickness in mice and voles. This research emphasized the jaw and teeth as a directly integrated developmental unit. Previously, I have also shown that tooth crown shape and alveolar shape are significantly related in adult Alligator mississippiensis, suggesting that a similar mechanically constraining mechanism found in rodents may extend to crocodylians as well. Further, crocodylians are uniquely polyphyodont with tooth sockets formed iteratively during ontogeny, implicating this integration as a means of generating heterodont or homodont dentition over the course of their lifespan. Preliminary investigations into alveolar mechanical constraint driving heterodonty in crocodylians suggests that iteratively growing alveolar septa generate a mesiodistal constraint to each developing crown and alveolar walls generate a buccolingual constraint. While this implies the alveoli mechanically shape teeth to produce heterodonty in crocodylians, the study did not take into account a quantification of tooth shape itself. This current project aims to investigate this by measuring tooth shape across ontogeny in crocodylian species with relatively more heterodont (differently shaped teeth) or homodont (similar shaped teeth) dentitions, by testing Alligator mississipiensis and Crocodylus acutus, respectively using a 3D digital anatomical approach.

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Nazeeba Siddika, BDS, MPH, Ph.D. and Ehsanul Hoque Apu, Ph.D. – 1

Airborne Contaminants in Dentistry: A Comprehensive Review of Microbial, Chemical, and Environmental Emissions During Clinical Procedures

Aerosol generation in dental settings has long been recognized as a central
concern for occupational and patient safety. Dental procedures involving high-speed handpieces, ultrasonic scalers, air–water syringes, and air-polishing devices produce complex mixtures of airborne contaminants, including microbial bioaerosols, chemical emissions, and particulate pollutants. These aerosols consist of droplets and particles of varying sizes that can remain suspended in the air, disperse throughout the operatory, and be inhaled by dental personnel or patients.

The COVID-19 pandemic amplified global attention to dental aerosols, highlighting the potential role of airborne transmission in clinical environments. Although numerous studies have investigated aerosol generation during specific dental procedures, findings remain highly variable due to methodological inconsistencies, differences in measurement technologies, and heterogeneity across clinical environments. Furthermore, recent investigations indicate that aerosols generated in dentistry may contain not only microorganisms from the patient’s oral cavity but also particles derived from dental materials, waterline systems, cleaning agents, and environmental dust. These components contribute to a complex exposure mixture with uncertain health implications.

Many investigations address only microbial contamination or aerosol quantity, neglecting the chemical and particulate components that also contribute to exposure. Despite the substantial interest in aerosol-related risks, a comprehensive understanding of the sources, composition, and behavior of aerosols and pollutants generated during dental procedures is still lacking. This gap hinders the development of evidence-based guidelines to optimize ventilation, personal protective equipment (PPE), material selection, and procedural workflows. A rigorous synthesis of the available evidence is therefore needed to support future occupational safety recommendations and infection-control policies.

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Nazeeba Siddika, BDS, MPH, Ph.D. and Ehsanul Hoque Apu, Ph.D. – 2

Knowledge, Attitude and Practice on Dental Care, and Oral Health Status among Pregnant Women in Bangladesh

Oral health is a critical component of overall well-being, yet pregnant women face heightened risks of gingivitis, periodontitis, and dental caries due to hormonal fluctuations, dietary changes, altered saliva composition, and decreased immune function. These oral conditions have been associated with adverse pregnancy outcomes, including low birth weight and preterm delivery. Despite the recognized importance of maternal oral health, many pregnant women, specifically from Low- and Middle-Income Countries (LMICs) like Bangladesh avoid dental care because of socioeconomic and cultural barriers, limited knowledge, and misconceptions about the safety of dental procedures during pregnancy. Healthcare providers including dentists, midwives, and prenatal care professionals also frequently lack adequate training and confidence in providing or recommending dental care during pregnancy, resulting in missed opportunities for early intervention. The World Health Organization advocates for integrated health service delivery, especially in resource-constrained settings, yet collaboration between medical and dental professionals remains insufficient. This gap underscores the need for strengthened interdisciplinary approaches, improved provider education, and increased awareness among pregnant women. Addressing these challenges is essential for reducing preventable oral diseases in pregnancy and lowering the risk of early childhood caries linked to maternal transmission of cariogenic bacteria.

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