AI-Assisted Implant Planning Using CBCT Data

NCT07597785 | Active Not Recruiting

• 1 other identifier: LEC-05-26-N
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Edentulism in Lower Jaw; Dental Implant

Keywords

Cone-Beam Computed Tomography; Artificial Intelligence; Dental Implant Planning; Implant Prosthodontics

Outcome Measures

Primary Outcomes (1)

• Clinical acceptability of AI-generated implant plans

  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 Outcomes (4)

• Geometric agreement between AI-generated and expert reference implant plans

  Baseline

• Anatomical safety of AI-generated implant plans

  Baseline

• Workflow time for AI-assisted planning review compared with expert planning

  Baseline

• Inter-reader agreement for clinical acceptability ratings

  Baseline

Study Arms (1)

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

Interventions

AI-Assisted Implant Planning Workflow OTHER

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.

Retrospective CBCT Planning Cases

Eligibility Criteria

• Age: 65 Years - 85 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• St. Petersburg State Pavlov Medical University: lead

Study Sites (1)

Pavlov First Saint Petersburg State Medical University

Saint Petersburg, Sankt-Peterburg, 197022, Russia

Location

Study Officials

• Roman A Rozov, MD, DSc

  St. Petersburg State Pavlov Medical University

  PRINCIPAL INVESTIGATOR

• Karina Sh Oisieva, DDS, MSc

  Saint Petersburg State University, Russia

  STUDY DIRECTOR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 8, 2026
• First Posted: May 19, 2026
• Study Start: February 16, 2026
• Primary Completion (Estimated): July 30, 2026
• Study Completion (Estimated): October 30, 2026
• Last Updated: May 19, 2026

Record last verified: 2026-05

Data Sharing

• IPD Sharing: Will not share

Locations

RU (1)