An interventional study of AI assisted CBCt and Manual CBCT segmentation in Dental Implant, Immediate Implant and Guided Bone Regeneration, sponsored by Shalash Dental education. Completed at 1 site in Egypt. Open to participants aged 18 Years to 99 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-09-30.
Sponsored by Shalash Dental education · Not applicable, Interventional, and Treatment
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.
Shalash Dental education is the lead sponsor of 2 studies on the registry; none are open to participants now.
Counted across the registry records on this site, refreshed daily.
Exclusion Criteria:
Participants randomized to this arm will undergo preoperative cone-beam computed tomography (CBCT) imaging as part of standard diagnostic workup for molar extraction with potential immediate implant placement. CBCT datasets will be processed using a pre-specified, locked artificial intelligence (AI)-based segmentation and socket assessment model. The AI system will automatically segment the extraction socket anatomy, quantify interradicular septum dimensions, and generate a structured feasibility assessment according to predefined anatomical decision thresholds established in the study protocol. A standardized AI-generated planning report, including septum measurements and feasibility classification, will be provided to the operating surgeon prior to surgery. The surgeon will use this report to guide preoperative planning and intraoperative decision-making. Surgical procedures will be performed according to a standardized surgical protocol. Immediate implant placement will be attempte
Device: AI assisted CBCt
Participants randomized to this arm will undergo preoperative CBCT imaging as part of the standard diagnostic workup for molar extraction with potential immediate implant placement. CBCT datasets will be evaluated using conventional manual segmentation and radiologic assessment performed by an experienced radiologist. Interradicular septum dimensions and socket morphology will be assessed according to predefined anatomical criteria specified in the study protocol. Feasibility for immediate implant placement will be determined using the same prospectively defined decision thresholds applied in the intervention arm. A standardized manual planning report, including septum measurements and feasibility classification, will be provided to the operating surgeon before surgery. The surgeon will use this report to guide preoperative planning and intraoperative decision-making. Surgical procedures will be performed according to the same standardized surgical protocol
Procedure: Manual CBCT segmentation
The 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.
The 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
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 elevation
Planning time (minutes)
Time to generate the report (AI processing time vs manual segmentation/assessment time).
Time frame: Preoperative before the surgery
Plan to share: Undecided
No publications or documents are linked to this record.
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
This study is completed, as verified in Mar 2026. You cannot join it, but the record below documents what was studied.
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