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Benedikt Sagl
Benedikt Sagl, PhDHead of Competence Center Artificial Intelligence in Dentistry

University Clinic of Dentistry
Position: Research Associate (Postdoc)

ORCID: 0000-0002-6739-4222
benedikt.sagl@meduniwien.ac.at

Keywords

Artificial Intelligence; Computer Simulation; Dentistry; Magnetic Resonance Imaging

Research group(s)

  • Competence Center Artificial Intelligence in Dentistry
    Head: Benedikt Sagl
    Research Area: The Competence Center Artificial Intelligence in Dentistry focuses on the development and application of cutting-edge artificial intelligence tools tailored towards dental applications. An emphasis is put on translational research to ensure the highest scientific quality as well as clinical relevance.
    Members:

Research interests

I am a Group Leader at the University Clinic of Dentistry of the Medical University of Vienna. My research focuses on developing AI tools specifically for dental applications, with a strong emphasis on clinical relevance. My work centers on applying AI to medical imaging, processing dental intraoral scans with graph convolutional networks, and integrating AI with biomechanical simulations to explore sex differences in temporomandibular disorders. Ongoing projects include automated segmentation, periodontal health grading, dental prosthetic design optimization, and predictive modeling for temporomandibular disorders. Our access to a robust computing cluster and a vast clinical database allows us to train complex models and test them in real-world dental practice. Moreover, I have extensively worked on computational modeling of joint systems, with a focus on hybrid multibody- finite element modeling.

Selected publications

  1. Sagl, B., Dickerson, C.R. and Stavness, I. (2019) ‘Fast Forward-Dynamics Tracking Simulation: Application to Upper Limb and Shoulder Modeling’, IEEE Transactions on Biomedical Engineering, 66(2), pp. 335–342. Available at: http://dx.doi.org/10.1109/tbme.2018.2838020.
  2. Sagl, B. et al. (2019) ‘A Dynamic Jaw Model With a Finite-Element Temporomandibular Joint’, Frontiers in Physiology, 10. Available at: http://dx.doi.org/10.3389/fphys.2019.01156.
  3. Sagl, B. et al. (2024) ‘The effect of bolus properties on muscle activation patterns and TMJ loading during unilateral chewing’, Journal of the Mechanical Behavior of Biomedical Materials, 151, p. 106401. Available at: https://doi.org/10.1016/j.jmbbm.2024.106401.
  4. Sagl, B. et al. (2022) ‘Effect of facet inclination and location on TMJ loading during bruxism: An in-silico study’, Journal of Advanced Research, 35, pp. 25–32. Available at: http://dx.doi.org/10.1016/j.jare.2021.04.009.
  5. Sun, S. et al. (2024) ‘Explainable Deep Learning and Biomechanical Modeling for TMJ Disorder Morphological Risk Factors’, JCI Insight [Preprint]. Available at: https://doi.org/10.1172/jci.insight.178578.