AI-Assisted Implant Planning Using CBCT Data (AIP-CBCT)
13. mai 2026 oppdatert 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.
Studieoversikt
Status
Aktiv, ikke rekrutterende
Intervensjon / Behandling
Detaljert beskrivelse
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.
Studietype
Observasjonsmessig
Registrering (Antatt)
100
Kontakter og plasseringer
Denne delen inneholder kontaktinformasjon for de som utfører studien, og informasjon om hvor denne studien blir utført.
Studiesteder
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Sankt-Peterburg
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Saint Petersburg, Sankt-Peterburg, Russland, 197022
- Pavlov First Saint Petersburg State Medical University
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Deltakelseskriterier
Forskere ser etter personer som passer til en bestemt beskrivelse, kalt kvalifikasjonskriterier. Noen eksempler på disse kriteriene er en persons generelle helsetilstand eller tidligere behandlinger.
Kvalifikasjonskriterier
Alder som er kvalifisert for studier
- Eldre voksen
Tar imot friske frivillige
Nei
Prøvetakingsmetode
Ikke-sannsynlighetsprøve
Studiepopulasjon
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.
Beskrivelse
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
Denne delen gir detaljer om studieplanen, inkludert hvordan studien er utformet og hva studien måler.
Hvordan er studiet utformet?
Designdetaljer
Kohorter og intervensjoner
Gruppe / Kohort |
Intervensjon / 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.
|
Hva måler studien?
Primære resultatmål
Resultatmål |
Tiltaksbeskrivelse |
Tidsramme |
|---|---|---|
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Clinical acceptability of AI-generated implant plans
Tidsramme: 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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Sekundære resultatmål
Resultatmål |
Tiltaksbeskrivelse |
Tidsramme |
|---|---|---|
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Geometric agreement between AI-generated and expert reference implant plans
Tidsramme: 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
Tidsramme: 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
Tidsramme: 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
Tidsramme: 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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Samarbeidspartnere og etterforskere
Det er her du vil finne personer og organisasjoner som er involvert i denne studien.
Etterforskere
- Hovedetterforsker: Roman A Rozov, MD, DSc, St. Petersburg State Pavlov Medical University
- Studieleder: Karina Sh Oisieva, DDS, MSc, Saint Petersburg State University, Russia
Studierekorddatoer
Disse datoene sporer fremdriften for innsending av studieposter og sammendragsresultater til ClinicalTrials.gov. Studieposter og rapporterte resultater gjennomgås av National Library of Medicine (NLM) for å sikre at de oppfyller spesifikke kvalitetskontrollstandarder før de legges ut på det offentlige nettstedet.
Studer hoveddatoer
Studiestart (Faktiske)
16. februar 2026
Primær fullføring (Antatt)
30. juli 2026
Studiet fullført (Antatt)
30. oktober 2026
Datoer for studieregistrering
Først innsendt
8. mai 2026
Først innsendt som oppfylte QC-kriteriene
13. mai 2026
Først lagt ut (Faktiske)
19. mai 2026
Oppdateringer av studieposter
Sist oppdatering lagt ut (Faktiske)
19. mai 2026
Siste oppdatering sendt inn som oppfylte QC-kriteriene
13. mai 2026
Sist bekreftet
1. mai 2026
Mer informasjon
Begreper knyttet til denne studien
Nøkkelord
Andre studie-ID-numre
- LEC-05-26-N
Plan for individuelle deltakerdata (IPD)
Planlegger du å dele individuelle deltakerdata (IPD)?
NEI
IPD-planbeskrivelse
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.
Legemiddel- og utstyrsinformasjon, studiedokumenter
Studerer et amerikansk FDA-regulert medikamentprodukt
Nei
Studerer et amerikansk FDA-regulert enhetsprodukt
Nei
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