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

Summary

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.

Conditions

• Dental Implant
• Immediate Implant
• Guided Bone Regeneration

Interventions

DEVICE

AI assisted CBCt

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.

PROCEDURE

Manual CBCT segmentation

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

Sponsors & Collaborators

• Shalash Dental education

  lead OTHER

Principal Investigators

• Mahmoud Shalash, PhD · Shalash Implant education

Study Design

Allocation

RANDOMIZED

Purpose

TREATMENT

Masking

DOUBLE

Model

PARALLEL

Eligibility

Min Age

18 Years

Max Age

99 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2026-03-03

Primary Completion

2026-04-02

Completion

2026-05-02

Countries

• Egypt

Study Locations