AI-assisted Workflow to Optimize Immediate Implant Drilling Protocol with a SocketFit Static Surgical Guide: A Case Report.

Abstract

The incorporation of artificial intelligence (AI) into digital technology has profoundly enhanced the field of dental implantology in all phases of treatment from diagnosis through planning, surgery and restoration. With specific attention to planning and surgery, static computer-aided implant surgery (sCAIS) has become a widely accepted workflow by streamlining implant placement and restoration. However, during the placement of immediate implants, users of sCAIS can often experience specific limitations due to the anatomical complexity of post-extraction sockets, including their morphology, angulation, and the heterogeneity of surrounding bone density in relation to the planned implant position. These clinical factors can adversely influence the trajectory and stability of the surgical drill leading to its deflection resulting in deviations from the planned implant path. Such inaccuracies can lead to suboptimal implant positioning, compromising primary stability, esthetic outcomes, and ultimately, long-term clinical success. This article introduces an AI-assisted modification to the traditional sCAIS guide design workflow by introducing a pilot drill surgical guide (SocketFit Guide (SFG)) to minimize these risks when working with immediate implants. The design of the SFG incorporates the use of AI-driven virtual segmentation of anatomical structures during the digital planning phase. Through virtual tooth extraction, the AI algorithm accurately delineates the alveolar socket boundaries, enabling the design of the SFG with an extension and more apically positioned guide sleeve. Moving the pilot drill closer in proximity to the planned osteotomy site allows for more control over the drill trajectory minimizing deflection.