(77) Digital assessment with machine learning of immediate single-tooth replacement with acellular dermal matrix and sponge-like ossifying block graft on sloped platform-switching implant: two case reports

Introduction: The present study aims to describe a minimally invasive technique that combines a flapless immediate sloped platform-switching implant placement with an acellular dermal matrix (ADM) placed to contain a sponge-like ossifying block in the implant-socket gap and further augment the gingival thickness facial to the sloped implant shoulder. Additionally, the three-dimensional soft tissue quantitative analysis is performed under the digital models applied with machine learning algorithms.

Methods: Two patents are included in this case report. CBCT was taken before the surgery to verify the condition of the surgical site. Implants with sloped platform were placed immediately after tooth extraction. Sponge-like ossifying block, which contains hydroxyapatite and sugar cross-linked collagen matrix, was utilized to fill the implant-socket gap up to the height of the facial plate. The ADM allogeneic graft was placed in the space between the healing abutment and the gingiva. CBCT was taken again 2 years after the surgery. Dimensional data of the soft tissue around the implant site was collected by digitally quantitative analysis, which was applied with the machine learning algorithm.

Results: Digital quantitative analysis of the soft tissue thickness over implant platform level was performed by superimposing the digital impression to the CBCT. The linear measurements were applied to the soft tissue from the implant platform level to the peri-implant mucosa surface. The soft tissue thickness presented horizontally 3.15 mm and vertically 3.8 mm for the first case and horizontally 4.7mm and vertically 4.2 mm for the second case.

Conclusions: Flapless immediate placement of sloping implants combined with ADM marginal soft tissue grafting and sponge-like ossifying block gap grafting may maintain proper soft and hard tissue volume around the implant and lead to predictable esthetic outcomes. The digital quantitative assessment with machine learning algorithm is proved to be a viable technique for comparing the tissue volume before and after the treatment.

Lead Author's Contributions to the Research Project. : The lead author was responsible for machine learning model of the digital quantitative assessment, and composition of the abstract