The detection of apical radiolucencies in periapical radiographs: A comparison between an artificial intelligence platform and expert endodontists with CBCT serving as the diagnostic benchmark.

Journal: International endodontic journal
Published Date: May 3, 2025

Abstract

AIM: Accurate detection of periapical radiolucent lesions (PARLs) is crucial for endodontic diagnosis. While cone beam computed tomography (CBCT) is considered the radiographic gold standard for detecting PARLs in non-root filled teeth, its use is often limited by cost and radiation exposure. Artificial Intelligence (AI)-based radiographic analysis has shown the potential to enhance the diagnostic capability of periapical (PA) radiographs, but its accuracy, compared with CBCT, needs further evaluation. The aim of this paper is to assess the diagnostic accuracy of Diagnocat, a commercial AI-driven platform in detecting PARLs on PA radiographs of teeth diagnosed with irreversible pulpitis or pulp necrosis and scheduled for primary root canal treatment, using CBCT as the reference standard, and to compare Diagnocat's performance with that of experienced clinicians.

Authors

  • Marwa Allihaibi
    Department of Endodontics, Faculty of Dentistry, Taif University, Taif, Saudi Arabia.
  • Garrit Koller
    Department of Endodontics, Centre for Oral, Clinical and Translational Sciences, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK.
  • Francesco Mannocci
    Department of Endodontics, Centre for Oral, Clinical and Translational Sciences, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK.

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