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.

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.

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.

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.

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.