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

Laura Winterhalter; Florian Kofler; Dragan-Alexander Ströbele; Ahmed Othman; Constantin von See

doi:10.4317/jced.61491

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

J Clin Exp Dent. 2024 May 1;16(5):e547-e555. doi: 10.4317/jced.61491. eCollection 2024 May.

Authors

Laura Winterhalter¹, Florian Kofler^{2

3

4

5}, Dragan-Alexander Ströbele⁶, Ahmed Othman⁷, Constantin von See⁸

Affiliations

¹ Undergraduate dental student, Danube Private University, Krems, Austria.
² Helmholtz AI, Helmholtz Munich, Neuherberg, Germany.
³ Department of Computer Science, TUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
⁴ TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany.
⁵ Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
⁶ Assistant Professor in the digital technologies in dentistry and CAD/CAM department- Danube Private University- Austria.
⁷ Assistant professor and senior orthodontist in the digital technologies in dentistry and CAD/CAM department- Danube Private University- Austria.
⁸ Director of the digital technologies in dentistry and CAD/CAM department- Danube Private University- Austria.

Abstract

Background: 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.

Material and methods: 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.

Results: 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).

Conclusions: 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. Key words:Artificial intelligence, Eye-tracking device, low-budget, dentistry.