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AI-Assisted Implant Planning Using CBCT Data (AIP-CBCT)

2026年5月13日 更新者：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.

研究概览

地位

主动，不招人

条件

干预/治疗

详细说明

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.

研究类型

观察性的

注册 (估计的)

100

联系人和位置

本节提供了进行研究的人员的详细联系信息，以及有关进行该研究的地点的信息。

学习地点

  • 俄罗斯
    • Sankt-Peterburg
      • Saint Petersburg、Sankt-Peterburg、俄罗斯、197022
        • Pavlov First Saint Petersburg State Medical University

参与标准

研究人员寻找符合特定描述的人，称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。

资格标准

适合学习的年龄

  • 年长者

接受健康志愿者

不

取样方法

非概率样本

研究人群

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.

描述

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.

学习计划

本节提供研究计划的详细信息，包括研究的设计方式和研究的衡量标准。

研究是如何设计的？

设计细节

队列和干预

团体/队列
干预/治疗
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.
其他：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.

研究衡量的是什么？

主要结果指标

结果测量
措施说明
大体时间
Clinical acceptability of AI-generated implant plans
大体时间：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.
Baseline

次要结果测量

结果测量
措施说明
大体时间
Geometric agreement between AI-generated and expert reference implant plans
大体时间：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.
Baseline
Anatomical safety of AI-generated implant plans
大体时间：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.
Baseline
Workflow time for AI-assisted planning review compared with expert planning
大体时间：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.
Baseline
Inter-reader agreement for clinical acceptability ratings
大体时间：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.
Baseline

合作者和调查者

在这里您可以找到参与这项研究的人员和组织。

赞助

St. Petersburg State Pavlov Medical University

调查人员

  • 首席研究员：Roman A Rozov, MD, DSc、St. Petersburg State Pavlov Medical University
  • 研究主任：Karina Sh Oisieva, DDS, MSc、Saint Petersburg State University, Russia

研究记录日期

这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查，以确保它们在发布到公共网站之前符合特定的质量控制标准。

研究主要日期

学习开始 (实际的)

2026年2月16日

初级完成 (估计的)

2026年7月30日

研究完成 (估计的)

2026年10月30日

研究注册日期

首次提交

2026年5月8日

首先提交符合 QC 标准的

2026年5月13日

首次发布 (实际的)

2026年5月19日

研究记录更新

最后更新发布 (实际的)

2026年5月19日

上次提交的符合 QC 标准的更新

2026年5月13日

最后验证

2026年5月1日

更多信息

与本研究相关的术语

关键字

其他研究编号

  • LEC-05-26-N

计划个人参与者数据 (IPD)

计划共享个人参与者数据 (IPD)？

不

IPD 计划说明

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.

药物和器械信息、研究文件

研究美国 FDA 监管的药品

不

研究美国 FDA 监管的设备产品

不

此信息直接从 clinicaltrials.gov 网站检索，没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求，请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改，我们的网站上也会自动更新.

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