Artificial Intelligence-Guided Versus Manual CBCT Planning for Immediate Implant Placement
Trial statusNot yet recruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18-99
SponsorShalash Dental education
About this trial
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.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
Adults≥18 years are able to provide informed consent. Indicated for the extraction of a molar tooth (maxillary or mandibular) with planned implant-supported rehabilitation.
Candidate for immediate implant placement attempt based on preoperative clinical evaluation and CBCT availability.
Preoperative CBCT will be acquired using the study imaging protocol within a defined window (e.g., ≤4 weeks before surgery).
Adequate oral hygiene and periodontal condition compatible with implant surgery (e.g., treated/stable periodontal status per clinician judgement).
Disqualifiers
Pregnancy or breastfeeding (due to imaging/surgical considerations).
Uncontrolled systemic disease or medical contraindication to oral surgery/implant placement (e.g., uncontrolled diabetes, immunosuppression as judged by clinician).
History of head and neck radiotherapy in the implant region. Use of medications associated with compromised bone healing where immediate implant placement is not advised (e.g., high-dose antiresorptives/IV bisphosphonates; you can specify per your clinic policy).
Acute uncontrolled infection at the site requiring staged management (e.g., spreading cellulitis/abscess) or other condition precluding immediate placement per surgeon judgement.
Trial design
Design model
Parallel
Treatments tested in this trial
AI assisted CBCt
DeviceThe intervention consists of a fully automated, deep learning-based CBCT analysis pipeline designed for extraction socket segmentation and quantitative interradicular septum assessment. The AI system utilizes a pre-trained convolutional neural network architecture to perform voxel-level segmentation of the extraction socket and surrounding alveolar structures on CBCT datasets. Following segmentation, the model automatically quantifies predefined anatomical parameters, including interradicular septum width at standardized reference levels and socket morphology classification. These measurements are generated using algorithmically defined geometric landmarks, ensuring consistent spatial reference across cases. Feasibility for immediate implant placement is determined using a prespecified, protocol-defined decision rule applied to AI-derived quantitative parameters.
Manual CBCT segmentation
Procedure/SurgeryThe control intervention consists of conventional radiologic evaluation of CBCT datasets using manual segmentation and operator-driven anatomical assessment. CBCT scans will be reviewed by an experienced oral and maxillofacial radiologist using standard imaging software. Interradicular septum dimensions will be determined through manual identification of anatomical landmarks and measurement using software-based calipers at predefined reference levels. Socket morphology classification will be assigned based on visual interpretation and application of the same predefined anatomical criteria specified in the study protocol. Feasibility for immediate implant placement will be determined by applying the protocol-defined decision thresholds to manually obtained measurements. All measurements and classifications will be documented in a structured planning report provided to the operating surgeon. Unlike the AI-guided intervention, this workflow relies on manual landmark identification and ope
Treatment groups
80 Participants
are divided into 2 treatment groupsGroup A: AI assisted CBCTExperimental treatment 1 intervention
Group B: Manual CBCT segmentedActive comparator 1 intervention
Trial outcomes
Primary outcomes
1
Immediate implant feasability
Proportion of sites with successful immediate implant placement with primary stability at the index surgery (feasible vs not feasible).
Time frame
During the implant surgery-intra operative after flap elevationSecondary outcomes
1
Planning time (minutes)
Time to generate the report (AI processing time vs manual segmentation/assessment time).
Time frame
Preoperative before the surgerySponsors and contacts
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Source ClinicalTrials.gov
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