Artificial Intelligence-Enhanced Three-Dimensional Reconstruction of Ridge Deficiencies

Overview

n the current study, AI-assisted 3D ridge augmentation of combined vertical and horizontal deficient ridges utilizing allogenic dentin block versus allogenic dentin shell graft will be assessed.

Severe periodontitis, trauma, and long-term edentulism can cause significant resorptive alterations of the alveolar ridge, leading to severe bone defects in the esthetic zone. Careful hard and soft tissue evaluation prior to implant surgery is important to restore proper function and esthetics. Horizontal, vertical, or combined ridge defects after extraction significantly influence dental implant placement and its long-term stability, as well as the final esthetics outcome. Under natural conditions following a tooth extraction, the alveolar bone around the empty socket would undergo resorption and the alveolar ridge would recede in height and width. Besides leading to aesthetic and functional issues, the reduced bone volume would also cause insufficient support to the dental implant, causing the implant to become loose and unstable. For patients facing such issues, alveolar ridge augmentation is necessary to regenerate the bone tissue and restore the alveolar ridge so as to ensure the long-term stability of the implant. Although a large number of alloplastic, allogenic or xenogenic bone substitute materials are available for reconstruction of the alveolar crest, the use of autogenous bone is still considered the gold standard. Autogenous bone has excellent osteoinductive, osteoconductive and osteogenetic characteristics; immunological reactions or the transmission of diseases can be safely avoided, and predictable augmentation results can be obtained. For several years, the use of dentin as an alternative autogenous material for alveolar crest reconstruction and the grafting of bone deficits has been described and investigated in animal experiments and clinical studies. Dentin is a suitable grafting material because it is very similar to bone in its organic and inorganic composition. Similar to the alveolar bone, about 90% of the organic substance of dentin consists of type I collagen. Also, osteogenetically relevant structural proteins, such as osteocalcin, osteonectin, phosphoprotein and sialoprotein, can be found in dentin. Moreover, dentin contains osteogenetically active factors, including bone morphogenetic protein 2 (BMP-2), tissue growth factor-ß (TGF-ß) and insulin- like growth factor-2 (IGF-2). As in alveolar bone, the inorganic components of dentin consist of various calcium phosphates such as hydroxylapatite, ß-tricalcium phosphate, octacalcium phosphate and amorphous calcium phosphate. Compared with autogenous bone, the use of dentin offers the advantage of avoiding the harvesting procedure and the possible resulting donor site morbidity with promising clinical and histological results owing to its inherent osteoinductive and osteoconductive capacity. In comparison to autogenous bone block graft, dentin grafts also show significantly less resorption. However, autogenous dentin graft has limitations despite its proven bone-formation capacity: dependence of the quantity on the number of teeth indicated for extraction and the condition of the extracted teeth, lack of a standard method to process autogenous dentin graft, and patient preference. Therefore, the application of dentin graft material from other individuals- allogeneic dentin graft-has been considered as an alternative autogenous dentin graft. Allo-dentin graft was conceptualized from the demineralized bone matrix, which was largely developed and defined for the bone induction principle, which states that a protein macromolecule in dentin and bone induces the differentiation of mesenchymal cells into osteoblasts; this was postulated by Urist in 1965. The Allo-dentin graft is a refined allograft that has osteoinductivity and has been clinically used since the 1980s. Very few studies have investigated the application of Allo-dentin graft as bone substitutes for bone graft surgery. The narrative review of revealed that Allo-dentin graft has demonstrated a great potential for osteoinductivity in extraskeletal sites, maximum clinical safety and efficacy without antigenicity nor provoked immunologic reactions. Cone beam computed tomography (CBCT) imaging developments went hand in hand with the increasing use of 3D imaging applications for presurgical planning and transfer of oral implant treatment. The main reasons for the triumph of CBCT are its capabilities of volumetric jaw bone imaging at reasonable costs and doses, with a relative advantage of having a compact, affordable, and nearby equipment. For the clinicians involved in implant rehabilitation, the power of a dental 3D dataset is not only situated in the diagnostic field, but also in the potential of gathering integrated patient information for presurgical and treatment applications related to oral implant placement. Nowadays, rapid advances of digital technology and computer-aided design/computer-aided manufacturing (CAD/CAM) systems are indeed creating challenging opportunities for diagnosis, surgical implant planning and delivery of implant-supported prostheses. Segmenting organs and tissues to create 3-dimensional models is a useful technique for diagnosis, surgical planning, and simulation. This technique is also applicable to the planning of 3D-ridge augmentation, which plays a significant part in oral and maxillofacial surgery. Segmentation enables accurate measurement of the ridge volume, providing clinicians with quantitative insight into changes caused by cysts and tumors. In addition, accurately segmenting provides valuable guidance for clinicians when planning ridge augmentation during implant surgery. Although manual segmentation can be used for this purpose, it is time-consuming and dependent on the practitioner's experience due to high inter and intra-observer variability on CBCT images. While artificial intelligence (AI) segmentation is generally more successful and easier to perform than manual segmentation. In the current study, AI-assisted 3D ridge augmentation of combined vertical and horizontal deficient ridges utilizing allogenic dentin block versus allogenic dentin shell graft will be assessed.

Conditions

Interventions

Eligibility

Locations (2)

Outcomes