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Comparative Evaluation of Implant Planning Software

NCT06693635 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 1

Last updated 2025-01-01

No results posted yet for this study

Summary

Artificial intelligence (AI) is increasingly being integrated into dental implant planning, revolutionizing the way clinicians approach treatment. AI-driven software can enhance the accuracy of implant placement by analyzing complex data sets, including bone density, anatomical structures, and patient-specific factors. This technology enables the creation of precise 3D models and surgical guides, facilitating more predictable and personalized treatment outcomes. Additionally, AI algorithms can automate tasks such as segmentation, matching digital impressions with CBCT scans, and even suggesting optimal implant positions. The integration of AI in implant planning not only improves clinical efficiency but also contributes to better patient outcomes by reducing surgical risks and enhancing the overall success of implant procedures.

Conditions

  • Edentulous Jaw
  • Edentulous Alveolar Ridge
  • Edentulous Mouth
  • Partial-edentulism

Interventions

DIAGNOSTIC_TEST

Implant planning

The implant planning will be carried out using various software programs. The AI assistance provided by each software will be evaluated.

Sponsors & Collaborators

  • University of Bari Aldo Moro

    lead OTHER

Principal Investigators

  • Giuseppe D'Albis, Dr · University of Bari Aldo Moro

  • Massimo Corsalini, Prof · University of Bari Aldo Moro

  • Saverio Capodiferro, Prof · University of Bari Aldo Moro

Eligibility

Min Age
18 Years
Max Age
70 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-11-01
Primary Completion
2024-11-01
Completion
2024-11-01

Countries

  • Italy

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06693635 on ClinicalTrials.gov