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AI-Assisted Implant Planning Using CBCT Data
Sponsor: St. Petersburg State Pavlov Medical University
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.
Official title: Retrospective Reader Study of AI-Assisted Implant Planning Using Cone-Beam Computed Tomography Data in Edentulous Patients
Key Details
Gender
All
Age Range
65 Years - 85 Years
Study Type
OBSERVATIONAL
Enrollment
100
Start Date
2026-02-16
Completion Date
2026-10-30
Last Updated
2026-05-19
Healthy Volunteers
No
Conditions
Interventions
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.
Locations (1)
Pavlov First Saint Petersburg State Medical University
Saint Petersburg, Sankt-Peterburg, Russia