Dental Implant, Edentulism in Lower Jaw
Conditions
Keywords
Cone-Beam Computed Tomography, Artificial Intelligence, Dental Implant Planning, Implant Prosthodontics
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.
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.
Interventions
OTHERAI-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.
Sponsors
St. Petersburg State Pavlov Medical University
Study design
Observational model
CASE_ONLY
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
65 Years to 85 Years
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Clinical acceptability of AI-generated implant plans | 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
| Measure | Time frame | Description |
|---|---|---|
| Geometric agreement between AI-generated and expert reference implant plans | 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. |
| Anatomical safety of AI-generated implant plans | 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. |
| Workflow time for AI-assisted planning review compared with expert planning | 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. |
| Inter-reader agreement for clinical acceptability ratings | 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. |
Countries
Russia
Contacts
PRINCIPAL_INVESTIGATORRoman A Rozov, MD, DSc
St. Petersburg State Pavlov Medical University
STUDY_DIRECTORKarina Sh Oisieva, DDS, MSc
Saint Petersburg State University, Russia
Outcome results
None listed