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NCT Number: NCT07597785

AI-Assisted Implant Planning Using CBCT Data

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.

Active, Not Recruiting

This study is active but is not currently recruiting participants.

Key information

Conditions

Edentulism in Lower Jaw Dental Implant

Age range

65 year–85 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Pavlov First Saint Petersburg State Medical University

Saint Petersburg, Sankt-Peterburg, 197022, Russia

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

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.

Primary outcomes

  1. 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.

Secondary outcomes

  1. 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.

  2. 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.

  3. 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.

  4. 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.

Sponsors and collaborators

Lead sponsor

St. Petersburg State Pavlov Medical University

Other

Registry information

Official study title

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

Acronym: AIP-CBCT

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
May 19, 2026
Registry last updated
May 19, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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