AI-Assisted Implant Planning Using CBCT Data (AIP-CBCT)
13 maj 2026 uppdaterad av: St. Petersburg State Pavlov Medical University
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.
Studieöversikt
Status
Aktiv, inte rekryterande
Betingelser
Intervention / Behandling
Detaljerad beskrivning
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.
Studietyp
Observationell
Inskrivning (Beräknad)
100
Kontakter och platser
Det här avsnittet innehåller kontaktuppgifter för dem som genomför studien och information om var denna studie genomförs.
Studieorter
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Sankt-Peterburg
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Saint Petersburg, Sankt-Peterburg, Ryssland, 197022
- Pavlov First Saint Petersburg State Medical University
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Deltagandekriterier
Forskare letar efter personer som passar en viss beskrivning, så kallade behörighetskriterier. Några exempel på dessa kriterier är en persons allmänna hälsotillstånd eller tidigare behandlingar.
Urvalskriterier
Åldrar som är berättigade till studier
- Äldre vuxen
Tar emot friska volontärer
Nej
Testmetod
Icke-sannolikhetsprov
Studera befolkning
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.
Beskrivning
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.
Studieplan
Det här avsnittet ger detaljer om studieplanen, inklusive hur studien är utformad och vad studien mäter.
Hur är studien utformad?
Designdetaljer
Kohorter och interventioner
Grupp / Kohort |
Intervention / Behandling |
|---|---|
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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.
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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.
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Vad mäter studien?
Primära resultatmått
Resultatmått |
Åtgärdsbeskrivning |
Tidsram |
|---|---|---|
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Clinical acceptability of AI-generated implant plans
Tidsram: Baseline
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Proportion of AI-generated implant plans rated by expert clinicians as accepted without modification, accepted after minor modification, accepted after major modification, or rejected.
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Baseline
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Sekundära resultatmått
Resultatmått |
Åtgärdsbeskrivning |
Tidsram |
|---|---|---|
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Geometric agreement between AI-generated and expert reference implant plans
Tidsram: Baseline
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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.
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Baseline
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Anatomical safety of AI-generated implant plans
Tidsram: Baseline
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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.
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Baseline
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Workflow time for AI-assisted planning review compared with expert planning
Tidsram: Baseline
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Time required for independent expert implant planning will be compared with the time required for expert review and correction of AI-generated implant plans.
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Baseline
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Inter-reader agreement for clinical acceptability ratings
Tidsram: Baseline
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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.
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Baseline
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Samarbetspartners och utredare
Det är här du hittar personer och organisationer som är involverade i denna studie.
Utredare
- Huvudutredare: Roman A Rozov, MD, DSc, St. Petersburg State Pavlov Medical University
- Studierektor: Karina Sh Oisieva, DDS, MSc, Saint Petersburg State University, Russia
Studieavstämningsdatum
Dessa datum spårar framstegen för inlämningar av studieposter och sammanfattande resultat till ClinicalTrials.gov. Studieposter och rapporterade resultat granskas av National Library of Medicine (NLM) för att säkerställa att de uppfyller specifika kvalitetskontrollstandarder innan de publiceras på den offentliga webbplatsen.
Studera stora datum
Studiestart (Faktisk)
16 februari 2026
Primärt slutförande (Beräknad)
30 juli 2026
Avslutad studie (Beräknad)
30 oktober 2026
Studieregistreringsdatum
Först inskickad
8 maj 2026
Först inskickad som uppfyllde QC-kriterierna
13 maj 2026
Första postat (Faktisk)
19 maj 2026
Uppdateringar av studier
Senaste uppdatering publicerad (Faktisk)
19 maj 2026
Senaste inskickade uppdateringen som uppfyllde QC-kriterierna
13 maj 2026
Senast verifierad
1 maj 2026
Mer information
Termer relaterade till denna studie
Nyckelord
Andra studie-ID-nummer
- LEC-05-26-N
Plan för individuella deltagardata (IPD)
Planerar du att dela individuella deltagardata (IPD)?
NEJ
IPD-planbeskrivning
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.
Läkemedels- och apparatinformation, studiedokument
Studerar en amerikansk FDA-reglerad läkemedelsprodukt
Nej
Studerar en amerikansk FDA-reglerad produktprodukt
Nej
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