Welcome to this new blog on full arch dental implant therapy !
Often the decision on prosthetic design and approach for dental implant therapy can be a challenge to determine. Patients either present to our practice with extensive decay or severe periodontal disease such that teeth can no longer be maintained. Rather than a cookie cutter approach to full arch, in this series we will try to outline a decision tree algorithm on planning these cases to ensure not only implant osseointegration is successful but ensure long term aesthetic success of the case.
Decision Tree Algorithm
The etiology of tooth loss is important to understand and is broken down into two categories in the decision tree algorithm for full arch implant therapy: periodontal disease/edentulous and decay/wear. Critical analysis of the starting condition of each potential full arch candidate involves radiographs, CBCT, Intraoral scan or PVS impressions in addition to high quality photographs at rest and in full smile. The photos taken are especially critical to avoid transition line issues due to high lip line patients (Figure 1)
Gingival Display
Lip line assessment can be classified as either low or high. In cases in which the gingival display is high (Figure 2), bone reduction or alveoplasty is critical to the success of the full arch implant therapy patient from a functional and esthetic perspective. Another indication for bone reduction is insufficient prosthetic space for the restorative material chosen. Typically, a hybrid prosthesis requires 12-15mm of space to accommodate the denture tooth, acrylic and framework. Monolithic zirconia will require 11-14mm of prosthetic space. There are many ways to achieve this bone reduction including burrs, piezo devices and saws.
Proper planning of these cases , including the correct alveoplasty can lead to an aesthetic outcome. Shown below (figure 3) is a case with periodontal disease and excessive maxillary display treated with a full arch approach at CIDN LAB. Stay tuned for PART 2 where we will dive deeper into our decision tree algorithm.
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