Artificial Intelligence-Guided Versus Manual CBCT Planning for Immediate Implant Placement

This study evaluates whether artificial intelligence (AI)-based analysis of cone-beam computed tomography (CBCT) scans can support clinical decision-making for immediate dental implant placement in molar extraction sites. When a molar tooth is removed, placing a dental implant immediately may reduce treatment time and preserve surrounding bone. However, immediate implant placement is not always possible and depends on the anatomy of the extraction socket, particularly the interradicular septum (the bone between the roots). CBCT imaging is routinely used to assess this anatomy before surgery. Traditionally, radiologists manually evaluate these scans. Recently, AI-based tools have been developed to automatically analyze CBCT images. In this randomized controlled trial, patients requiring molar extraction and potential immediate implant placement will be assigned to one of two planning approaches: AI-guided CBCT assessment or conventional manual CBCT assessment. The operating surgeon will use the assigned planning report to guide treatment decisions. The primary outcome of the study is the feasibility of immediate implant placement, defined as successful implant placement with achievement of primary stability during surgery. Secondary outcomes include surgical time, need for changes to the treatment plan, and implant stability measurements. The goal of this study is to determine whether AI-assisted CBCT analysis performs similarly to, or improves upon, conventional manual radiologic assessment in supporting safe and effective immediate implant placement.

This study is a prospective, parallel-arm, randomized controlled clinical trial designed to evaluate the clinical impact of artificial intelligence (AI)-based CBCT analysis on decision-making for immediate implant placement in molar extraction sites. Following eligibility confirmation and informed consent, participants requiring molar extraction with potential immediate implant placement will undergo standardized preoperative cone-beam computed tomography (CBCT) imaging. Participants will be randomly allocated in a 1:1 ratio to one of two planning workflows: AI-guided planning arm: CBCT scans will be analyzed using a pre-specified, locked AI-based segmentation and socket assessment model. The AI system will quantify interradicular septum dimensions and generate a feasibility classification based on predefined anatomical criteria. Manual planning arm: CBCT scans will undergo conventional manual segmentation and assessment by an experienced radiologist using the same predefined anatomical criteria for feasibility determination. In both arms, feasibility recommendations will be based on identical, prospectively defined decision thresholds to ensure comparability between planning methods. The operating surgeon will receive only the planning report corresponding to the assigned allocation. All surgeries will be performed according to a standardized surgical protocol. The primary outcome is intraoperative feasibility of immediate implant placement, defined as successful implant placement in the extraction socket with achievement of primary stability according to prespecified stability criteria documented in the operative record. Cases in which implant placement is not performed or is aborted due to inability to achieve adequate primary stability will be classified as non-feasible. Secondary outcomes include operative time, need for intraoperative modification of the treatment plan, insertion torque values, and any intraoperative complications. Outcome assessment will be performed by an independent assessor blinded to allocation. AI analysis will be conducted using a locked model without post hoc modification. The study aims to determine whether AI-guided CBCT planning is non-inferior or superior to conventional manual CBCT assessment in supporting immediate implant placement decisions.