Accuracy and Time Efficiency of Artificial Intelligence-Driven Tooth Segmentation on CBCT Images: A Validation Study Using Two Implant Planning Software Programs.

Journal: Clinical oral implants research
Published Date: Jul 18, 2025

Abstract

OBJECTIVES: To assess the accuracy and time efficiency of manual versus artificial intelligence (AI)-driven tooth segmentation on cone-beam computed tomography (CBCT) images, using AI tools integrated within implant planning software, and to evaluate the impact of artifacts, dental arch, tooth type, and region.

Authors

  • Panagiotis Ntovas
    Department of Prosthodontics, School of Dental Medicine, Tufts University School of Dental Medicine, Boston, Massachusetts, USA.
  • Piyarat Sirirattanagool
    Department of Prosthodontics, School of Dental Medicine, Tufts University, Boston, Massachusetts, USA.
  • Praewvanit Asavanamuang
    Department of Prosthodontics, School of Dental Medicine, Tufts University, Boston, Massachusetts, USA.
  • Shruti Jain
    Department of Anesthesiology, School of Medical Sciences & Research, Sharda University, Greater Noida.
  • Lorenzo Tavelli
    Division of Periodontology, Department of Oral Medicine, Infection, and Immunity, Harvard School of Dental Medicine, Boston, Massachusetts, USA.
  • Marta Revilla-León
    Affiliate Assistant Professor, Graduate Prosthodontics, Department of Restorative Dentistry, School of Dentistry, University of Washington, Seattle, Wash and Faculty and Director of Research and Digital Dentistry, Kois Center, Seattle, Wash; Adjunct Professor, Department of Prosthodontics, School of Dental Medicine, Tufts University, Boston, MA.
  • Maria Eliza Galarraga-Vinueza
    Department of Oral Medicine, Infection, and Immunity Division of Periodontology, Harvard School of Dental Medicine, Boston, Massachusetts, USA.

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