AI-Assisted Implant Planning Using CBCT Data (AIP-CBCT)

May 13, 2026 updated by: St. Petersburg State Pavlov Medical University

Retrospective Reader Study of AI-Assisted Implant Planning Using Cone-Beam Computed Tomography Data in Edentulous Patients

This retrospective observational reader study will evaluate artificial intelligence (AI)-assisted implant planning using anonymized cone-beam computed tomography (CBCT) datasets from patients with complete edentulism or a clinically equivalent edentulous condition. AI-generated implant plans will be compared with expert reference plans created by clinicians using the same CBCT data. The study will assess the clinical acceptability of AI-generated implant plans, geometric agreement with expert plans, anatomical safety, workflow time, and agreement between expert reviewers where applicable. The study uses previously acquired anonymized imaging data and does not involve patient recruitment, treatment allocation, additional imaging, clinical intervention, or prospective follow-up.

Study Overview

Status

Active, not recruiting

Conditions

Intervention / Treatment

Detailed Description

This study is designed as a retrospective non-randomized comparative reader study. Anonymized CBCT datasets acquired during routine clinical care will be used for implant planning assessment. For each eligible case, expert clinicians will create reference implant plans without access to AI-generated plans. The AI system will generate implant planning outputs from the same CBCT datasets, and expert clinicians will review the AI-generated plans using a standardized assessment approach. The main evaluation will compare AI-generated plans with expert reference plans within the same case. Outcomes will include clinical acceptability of the AI-generated plan, geometric agreement between AI-generated and expert plans, anatomical safety relative to relevant risk structures, time required for expert planning versus AI-plan review and correction, and inter-reader agreement where applicable. The study does not test an autonomous AI decision-making system. The AI workflow is evaluated as a clinical decision-support tool, and all AI-generated plans are subject to expert clinician review. No new imaging examinations, treatment allocation, patient intervention, or prospective clinical outcome assessment will be performed.

Study Type

Observational

Enrollment (Estimated)

100

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Russia
    • Sankt-Peterburg
      • Saint Petersburg, Sankt-Peterburg, Russia, 197022
        • Pavlov First Saint Petersburg State Medical University

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

The study population will consist of anonymized CBCT cases from edentulous patients, or patients with a clinically equivalent edentulous condition, who underwent CBCT imaging during routine clinical care for implant prosthodontic planning. No new patient recruitment, additional imaging, treatment allocation, or patient intervention will be performed.

Description

Inclusion Criteria:

  • Anonymized CBCT dataset from a patient with complete edentulism or a clinically equivalent edentulous condition requiring implant prosthodontic planning.
  • CBCT imaging acquired during routine clinical care.
  • Sufficient field of view to assess the jaws and relevant anatomical landmarks for implant planning.
  • Image quality sufficient for anatomical assessment, segmentation, and implant planning.
  • Technical suitability of the CBCT dataset for expert reference planning and AI-assisted implant planning.

Exclusion Criteria:

  • Severe motion artifacts or metal artifacts preventing reliable anatomical assessment.
  • Incomplete field of view preventing assessment of the intended implant planning region.
  • Corrupted, incomplete, duplicate, or unreadable DICOM data.
  • Technical limitations preventing expert reference planning or AI-assisted implant planning.
  • Missing data required for assessment of the primary outcome.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Retrospective CBCT Planning Cases
Anonymized cone-beam computed tomography (CBCT) cases from patients with complete edentulism or a clinically equivalent edentulous condition who underwent CBCT imaging for implant planning during routine clinical care. Each case will be evaluated using expert reference planning and AI-assisted implant planning with expert review.
Other: AI-Assisted Implant Planning Workflow
AI-assisted implant planning workflow applied to anonymized CBCT datasets. The workflow generates implant planning outputs for expert review and comparison with expert reference plans. It is evaluated as a clinical decision-support workflow and does not involve patient treatment, additional imaging, or autonomous clinical decision-making.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Clinical acceptability of AI-generated implant plans
Time Frame: Baseline
Proportion of AI-generated implant plans rated by expert clinicians as accepted without modification, accepted after minor modification, accepted after major modification, or rejected.
Baseline

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Geometric agreement between AI-generated and expert reference implant plans
Time Frame: Baseline
Geometric agreement will be assessed for matched implants using entry-point deviation, apical deviation, and angular deviation between AI-generated and expert reference implant positions.
Baseline
Anatomical safety of AI-generated implant plans
Time Frame: Baseline
Anatomical safety will be assessed using minimum distances from planned implants to relevant anatomical risk structures and the presence or absence of predefined safe-margin violations.
Baseline
Workflow time for AI-assisted planning review compared with expert planning
Time Frame: Baseline
Time required for independent expert implant planning will be compared with the time required for expert review and correction of AI-generated implant plans.
Baseline
Inter-reader agreement for clinical acceptability ratings
Time Frame: Baseline
Agreement between expert clinicians will be assessed for clinical acceptability ratings of AI-generated implant plans where more than one expert evaluates the same cases.
Baseline

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

St. Petersburg State Pavlov Medical University

Investigators

  • Principal Investigator: Roman A Rozov, MD, DSc, St. Petersburg State Pavlov Medical University
  • Study Director: Karina Sh Oisieva, DDS, MSc, Saint Petersburg State University, Russia

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

February 16, 2026

Primary Completion (Estimated)

July 30, 2026

Study Completion (Estimated)

October 30, 2026

Study Registration Dates

First Submitted

May 8, 2026

First Submitted That Met QC Criteria

May 13, 2026

First Posted (Actual)

May 19, 2026

Study Record Updates

Last Update Posted (Actual)

May 19, 2026

Last Update Submitted That Met QC Criteria

May 13, 2026

Last Verified

May 1, 2026

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • LEC-05-26-N

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

Individual participant data will not be shared because the study uses retrospective anonymized medical imaging datasets. CBCT/DICOM data may contain potentially re-identifiable information and cannot be publicly shared. Aggregated results will be reported in publications.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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