Articles - Forum Implantologicum

Article

Feature Article

Apr 23, 2025 // 16 min read

Personalized Oral Implantology - a Definition and Outlook on Future Potential

Sašo Ivanovski

Rafael Pereira da Mata Santos

Himanshu Arora

DOI: 10.3290/iti.fi.45753

digital workflow omics personalized dentistry artificial intelligence 3D printing

Abstract

Background: Personalized medicine (PM) seeks to customize diagnostics and therapeutics based on a thorough understanding of individual characteristics, including genotypic, phenotypic, and behavioral aspects. This approach strives to create timely and effective treatment and prevention measures. Advancements in technology and data-driven systems in healthcare are key to progress in enabling this personalized approach. Recent advancements are enhancing implantology by individualizing patient care and therapies. These include artificial intelligence (AI), OMICS (the full set of biological molecules in an individual), computer-assisted implant surgery (CAIS), three-dimensional (3D) printing and bioprinting.

Objectives: This narrative review explores current aspects of personalized implantology and its clinical implementation.

Conclusion: While it may be a distant reality, personalized implantology holds great potential. Once feasible, it will improve disease prediction and enable targeted prevention and therapies, ultimately enhancing patient outcomes and minimizing side effects.

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Introduction

Personalized Medicine (PM) is a concept focused on customizing treatment strategies to suit each patient's unique requirements. This approach requires a thorough analysis of complex biological systems and individual phenotypic and behavioral data, incorporating both biological and clinical information. The main goal is to provide timely, effective, and safe diagnoses and treatments while implementing preventive measures tailored to a patient’s presenting condition and disease management. This comprehensive integration aims to deliver optimal preventive and therapeutic care. PM shares some elements with the P4 medicine concept, which stands for a more precise and personalized but also preventive and participatory approach towards healthcare (European Council 2015; Joda & Zitzmann 2022; Bartold & Ivanovski 2022; Schwendicke & Krois 2022) (Fig. 1).

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Fig. 1: P4 Medicine is a holistic model that combines predictive, preventive, personalized, and participatory elements

PM began to take shape in the 1990s, driven by advancements in genomic research and data processing technologies (Colins & Fink 1995). Although there have been initial advancements such as the use of omics technology to identify disease-associated genes and therapeutic targets (Hood 2013; Pirmohamed 2023), the implementation of PM, particularly in the dental field, is still in its conceptualization and requires much development.

Dentistry is currently in an era of stratifying patients into risk groups by risk assessment. By evaluating risk factors, dental professionals can better tailor preventive measures and treatment plans to enhance patient outcomes. This approach inherently assigns the same treatment to entire groups, thereby limiting the capacity for personalized care (Schwendicke & Krois 2022; Hung et al. 2023). Understanding factors that contribute to disease pathogenesis, progression and management at an individual level is essential for personalized dentistry. This helps predict responses to etiological factors, treatment effectiveness, and the likelihood of disease development. A significant advancement in personalized care will be the identification of reliable, clinically validated biomarkers associated with specific diseases. These biomarkers can help guide therapeutic targets, reduce diagnostic errors, and monitor treatment response. Furthermore, when combined with epidemiological and outcome studies, they can support the evaluation of intervention effectiveness and allow for the adaptation of procedures to meet the unique requirements of each patient (Kornman & Duff 2012; Ballman 2015).

Implant dentistry, whether in clinical or research settings, has the potential to incorporate various technological and computational innovations, such as artificial intelligence (AI), three-dimensional (3D) printing, virtual surgical and prosthetic planning, and omics (defined as the characterization and quantification of the full set of biological molecules in an organism). These advancements show promising potential for the future implementation of personalized implantology. This article focuses on the current aspects of personalized implantology and explores its prospects.

Fig. 1: P4 Medicine is a holistic model that combines predictive, preventive, personalized, and participatory elements

Fig. 2: Implant Disease Risk Assessment (IDRA) functional diagram. Reproduced with permission from Heitz-Mayfield et al. (2020): “Each vector represents one risk parameter with an area of relative risk. Low risk is represented by the area within the center of the octagon (green shading), moderate risk is represented by the area of the octagon between the first and second ring in bold (yellow shading), and high risk is represented by the area outside the second bold ring of the octagon (red shading).”

Fig. 3: Peri-implant microenvironment. The illustration contrasts a healthy peri-implant environment (left) with peri-implantitis (right), depicting immune cells, cytokines, bacteria, and Ti particles. Figure reproduced from Huang et al. (2024), Role of immune dysregulation in peri-implantitis. Frontiers in Immunology, under the Creative Commons Attribution 4.0 International (CC BY 4.0).

Fig. 4: Illustration of AI and its subsets: Machine learning and deep learning

Fig. 5: Illustration of the parameters used to evaluate implant placement accuracy, comparing the planned implant position with the actual position achieved

Fig. 6: sCAIS workflow: The process begins with data acquisition (CBCT and surface scan), which are aligned to form a virtual coordinated system. The implant position is then planned based on the ideal prosthetic position and the patient’s anatomy. Subsequently, the surgical template is designed and sent for 3D printing

Fig. 7: The free drilling distance (FDD) is the linear measurement from the bottom of the guided sleeve to the bottom of the osteotomy

Fig. 8: sCAIS clinical case: (a) Occlusal view of the edentulous ridge. (b) Surgical template fitting test. (c) Occlusal view of the ridge after flap reflection. (d) Guided bone drilling. (e) Parallel pins indicating the position and depth of the osteotomies. (f) Implant placement according to the planned position. (g) Guided bone regeneration using a mixture of xenogenous and autologous bone particles. (h) Application of collagen membranes. (i) Tension-free flap closure and suturing

Fig. 9: CAD-CAM custom healing abutment: Clinical case demonstrating the use of CAD-CAM custom healing abutment of early (Type 2) placement in the anterior maxilla. (a) Pre-operative presentation, (b, c) Digitally designed and fabricated custom healing abutment, (d) Guided implant placement, (e) Custom healing abutment helping in the positioning of the hard-tissue grafting material, (f) Soft tissue closure around the custom healing abutment

Fig. 10: 3D printing workflow. Based on Bartnikowski et al. (2020)

Fig. 11: Ridge augmentation using 3D-printed Ti mesh clinical case (Courtesy of Dr. Jamil Alayan)

Fig. 12: Autologous bone graft harvest from the mandibular ramus region. The use of autologous grafts presents challenges, including limited availability, potential morbidity, rapid resorption, and complications related to the donor site. (a) Exposed bone following full-thickness flap reflection. (b) Bone incision for the removal of the bone block. (c) Harvested bone block. Courtesy of Dr. Rafael Lazarin

Fig. 13: A clinical case of jawbone defect treated with resorbable personalized implant scaffold: A 3D-printed personalized scaffold using medical-grade polycaprolactone, functionalized with autogenous platelet concentrate and bone particulate was utilized for the regeneration of an anterior maxillary defect. The patient presented with maxillary alveolar ridge resorption at missing teeth 11 and 21, resulting in insufficient bone for dental implant placement and reconstruction (a). Cone beam computed tomography (CBCT) scans were performed to assess the presenting bone anatomy (b). The personalized scaffold (c), manufactured through 3D printing, featured a porous framework to support and guide bone formation, fixation perforations, and an internal compartment for the delivery of injectable platelet rich fibrin (iPRF) and bone particulate. During the procedure, the bone defect was surgically accessed, and the personalized scaffold was placed to check the fit (d). Biofunctionalization was imparted via bone particles and iPRF (e, f). The scaffold was fixed over the defect. Favorable healing of the implanted 3D scaffold was observed, with no signs of inflammation or rejection of the scaffold after 6 months (h). Post-healing CBCT of the regenerated alveolar ridge showed a significant increase in width and volume (i, j, indicated by arrows), sufficient for subsequent dental implant insertion and dental reconstruction