AI-Assisted Diagnostics in Dentistry: an Eye-Tracking Study on User Behavior

• Laura Winterhalter ^[1] ; Florian Kofler ^[2] ; Dragan-Alexander Ströbele ^[1] ; Ahmed Othman ^[1] ; Constantin von See ^[1]
  1. [1] Danube Private University

     Danube Private University

     Krems an der Donau, Austria

     
  2. [2] Helmholtz AI, Helmholtz Munich, Neuherberg, Germany; Department of Computer Science, TUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany; Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
• Localización: Journal of Clinical and Experimental Dentistry, ISSN-e 1989-5488, Vol. 16, Nº. 5 (May), 2024, págs. 547-555
• Idioma: inglés
• Enlaces
  • Texto completo
• Resumen

  • Artificial Intelligence (AI) has increasingly been integrated into dental practices, notably in radiographic imaging like Orthopantomograms (OPGs), transforming diagnostic protocols. Eye tracking technology offers a method to understand how dentists’ visual attention may differ between conventional and AI-assisted diagnostics, but its integration into daily clinical practice is challenged by the cost and complexity of traditional systems.

    Thirty experienced practitioners and dental students participated to evaluate the effectiveness of two low-budget eye-tracking systems, including the Peye Tracker (Eye Tracking Systems LTD, Southsea, UK) and Webgazer.js (Brown University, Providence, Rhode Island) in a clinical setting to assess their utility in capturing dentists’ visual engagement with OPGs. The hardware and software setup, environmental conditions, and the process for eye-tracking data collection and analysis are illustrated.

    The study found significant differences in eye-tracking accuracy between the two systems, with Webgazer.js showing higher accuracy compared to Peye Tracker (p<0.001). Additionally, the influence of visual aids (glasses vs. contact lenses) on the performance of eye-tracking systems revealed significant differences for both Peye Tracker (p<0.05) and Webgazer.js (p<0.05).

    Low-budget eye-tracking devices present challenges in achieving the desired accuracy for analyzing dentists’ visual attention in clinical practice, highlighting the need for continued innovation and improvement in this technology.