Edentulism in Lower JawAIP-CBCTNCT07597785
AI-Assisted Implant Planning Using CBCT Data
An observational study of Edentulism in Lower Jaw and Dental Implant, run by St. Petersburg State Pavlov Medical University at 1 site in Russia.
Trial 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. Read more about this study
- Who is it for?
- Ages 65 to 85 years
- Conditions the study is for
- Edentulism in Lower Jaw, Dental Implant
- What is being tested?
- AI-Assisted Implant Planning Workflow
- How many people
- 100 participants
- Where
- 1 siteRussia
- Study dates
- 16 Feb 2026to 30 Oct 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 65 to 85 years
- All sexes
- No healthy volunteers
- Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
Russia1 site
Pavlov First Saint Petersburg State Medical University
Saint Petersburg, Sankt-Peterburg, 197022
About this study
Official title: 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.
Read the 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.
- Conditions
- Edentulism in Lower Jaw, Dental Implant
- Being tested
- AI-Assisted Implant Planning Workflow
- Keywords
- Cone-Beam Computed Tomography, Artificial Intelligence, Dental Implant Planning, Implant Prosthodontics
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Assisted Implant Planning WorkflowOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: AI-Assisted Implant Planning Workflow |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Baseline
Secondary outcomes (4)
Geometric agreement between AI-generated and expert reference implant plans
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.
Measured: Baseline
Anatomical safety of AI-generated implant plans
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.
Measured: Baseline
Workflow time for AI-assisted planning review compared with expert planning
Time required for independent expert implant planning will be compared with the time required for expert review and correction of AI-generated implant plans.
Measured: Baseline
Inter-reader agreement for clinical acceptability ratings
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.
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 May 2026
- Study start16 Feb 2026
- Primary completion30 Jul 2026 (estimated)
- Study completion30 Oct 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Edentulism in Lower JawStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Retrospective
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- St. Petersburg State Pavlov Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Roman A Rozov, MD, DSc, Principal investigator, St. Petersburg State Pavlov Medical UniversityKarina Sh Oisieva, DDS, MSc, Study director, Saint Petersburg State University, Russia
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07597785
- Sponsor's protocol ID
- LEC-05-26-N
- Acronym
- AIP-CBCT
- Status last verified
- May 2026
- First submitted
- 8 May 2026
- Last updated
- 19 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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.
Study at a glance
From the registry record, last updated by the sponsor on 19 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Retrospective Reader Study of AI-Assisted Implant Planning Using Cone-Beam Computed Tomography Data in Edentulous Patients
- ClinicalTrials.gov ID
- NCT07597785
- Acronym / sponsor ID
- AIP-CBCT / LEC-05-26-N
- Conditions
- Edentulism in Lower Jaw, Dental Implant
- Keywords
- Cone-Beam Computed Tomography, Artificial Intelligence, Dental Implant Planning, Implant Prosthodontics
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- St. Petersburg State Pavlov Medical UniversityUniversity, hospital or other
- Study start
- 16 Feb 2026 (actual)
- Primary completion
- 30 Jul 2026 (estimated)
- Study completion
- 30 Oct 2026 (estimated)
- First posted
- 19 May 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Retrospective CBCT Planning Cases | – | Other: AI-Assisted Implant Planning WorkflowAnonymized 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. |
Who may qualify
- Ages
- 65 to 85 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Older adult
- 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.
Key inclusion criteria (5 listed)
- 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.
Key exclusion criteria (5 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-Assisted Implant Planning WorkflowAI-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. | Other | Retrospective CBCT Planning Cases |
Primary outcome
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.
4 secondary outcomes
Geometric agreement between AI-generated and expert reference implant plans
Time frame: Baseline
Anatomical safety of AI-generated implant plans
Time frame: Baseline
Workflow time for AI-assisted planning review compared with expert planning
Time frame: Baseline
Inter-reader agreement for clinical acceptability ratings
Time frame: Baseline
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 May 2026.
- Individual participant dataSharing planned: No. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Roman A Rozov, MD, DSc principal investigator
St. Petersburg State Pavlov Medical University
Karina Sh Oisieva, DDS, MSc study director
Saint Petersburg State University, Russia
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Edentulism in Lower Jaw and Dental Implant, run by St. Petersburg State Pavlov Medical University at 1 site in Russia.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 19 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Russia. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Clinical acceptability of AI-generated implant plans (time frame: Baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 May 2026; AzTrials last refreshed its copy on 26 Oct 2026.