The Emergence Clone concept. Optimizing the implant emergence profile using artificial segmentation

A correct emergence profile is fundamental for achieving a natural result to transition from an implant with a circular neck to a cervical area that replicates a natural tooth. The shape of the critical contour must allow the reproduction of the emergence profile from the natural tooth: its design is linked to the desired tooth shape and gingival architecture. However, the absence of horizontal and vertical soft tissue references in the anterior edentulous area may present a clinical challenge in terms of both surgery and prosthetics. Digital workflows are an active trend in dentistry. The generation of 3D models is fundamental to make an accurate diagnosis, create a virtual plan, and predict the outcome. Segmentation is the isolation and individualization of the anatomical structure from one initial CBCT scan. A new artificial intelligence-convolutional neural network system was developed to automatically segment CBCT images. In delayed implant placement, the peri-implant soft tissue is progressively shaped through sequential modification of the critical contour. By copy-pasting the segmented root and through a mirror effect, the dental technician will be able to copy the natural critical contour of the contralateral tooth and replicate it exactly. CAD/CAM fabricated temporization using new artificial intelligence application enables consistent results in the reproduction of the natural critical contour in implant restoration. This innovative technique thus allows for the recreation with mirror effect of soft tissue architecture and a natural tooth contour using artificial intelligence-convolutional neural network. It facilitates the fabrication of the temporary tooth in cases of delayed implant placement in the anterior sector. No adjustment is required since the shape is mimetic to the contralateral natural tooth.