Artificial intelligence and multimodal dental data
We apply and evaluate machine-learning methods for dental imaging, surface data, functional measurements, clinical information, and multimodal datasets.
Competence Center Artificial Intelligence in Dentistry
AI-Dent Vienna
Connecting clinical questions with computational approaches in dentistry.
We bring methodological and clinical perspectives together within one interdisciplinary team. Our research uses, adapts, and further develops approaches in artificial intelligence, computational modeling, and digital dentistry in response to clinically relevant questions.
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Research overview
Methodological research guided by clinical relevance.
Strong methodological research and strong clinical research often develop in separate environments. Our ambition is to connect these perspectives within one team and move directly between clinical questions, quantitative analysis, and computational investigation.
Based at the University Clinic of Dentistry, we combine access to clinical expertise and data with experience in AI, biomechanics, imaging, and digital measurement. Across all three research areas, we emphasize scientific rigor, reproducibility, and relevance to dental research and care.
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Research areas
Three complementary areas of research.
Patient-specific computational biomechanics
We combine biomechanical simulation and data-driven modeling to study anatomy, joint loading, motor control, and treatment effects.
Digital dentistry and quantitative evaluation
We develop quantitative approaches for evaluating dental structures, movements, materials, restorations, and treatment performance.
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Selected research
Current funded projects.
Patient-specific modeling of TMJ sexual dimorphism biomechanics
Patient-specific computational models, machine learning, and motor-control simulation are used to investigate how anatomical sex differences influence temporomandibular joint loading and TMD risk.
TMD TRACE
Explainable artificial intelligence is being developed to classify and interpret mandibular kinematics from jaw-motion data, supporting TMD research and future clinical decision-making.
Long-term performance of monolithic zirconia restorations
This ten-year clinical study evaluates custom-made monolithic restorations manufactured from Zolid Gen-X (4Y-TZP) and Zolid Bion (4Y-/5Y-TZP) across multiple dental indications. Complementary micro-CT and materials-testing studies investigate their structural and mechanical performance.
Open-source research Selected code and research tools are available on GitHub.
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Publications
Selected recent publications.
Recent work from the group and its collaborators across computational modeling, imaging, and digital dentistry.
202620262026
View all publications Towards patient-specific optimization for mandibular reconstruction planning based on predicted bone-union propensity
Aftabi H, Lloyd JE, Ding A, Sagl B, Prisman E, Hodgson A, Fels S · Medical Image Analysis · accepted
A computational framework for simulating patient-specific TMJ biomechanics using a combined multibody dynamics and finite element approach
Ahmadi F, Sun S, Zhao J, Chen J, Wilson MB, Damon B, Wu Y, Almpani K, Chung R, Jani P, Chen P, Slate EH, Lee JS, Sagl B, Yao H · Annals of Biomedical Engineering
Milled surgical guides vs. freehand placement of orthodontic implants: a preclinical micro-CT study
Schwärzler A, Domic D, Panwinkler S, Chitan P, Sagl B, Jonke E · Frontiers in Oral Health · 7:1834583
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People
Our group.
We bring together researchers with backgrounds in dentistry, biomedical engineering, physics, artificial intelligence, and data science.
Benedikt Sagl
Group Head
Thomas Holzinger
PhD Student
Healthcare and Rehabilitation Technology
Valentin Yugay
PhD Student
Dentistry
Can Camuz
PhD Student
Data Science
Chen Lu
PhD Student
Dentistry
Shivam Singh
PhD Student
Data Science
Yeji Kim
PhD Student
Artificial Intelligence Applications and Innovation
Theresa Mayer
PhD Student
Physics
Patrick Chitan
PhD Student
Dentistry
Christian Hauer
MSc Student
Biomedical Engineering
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Latest news
News from the group.
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Collaborations
Research partners.
Our work is supported by academic and industry collaborations.
Prof. Hai Yao
Clemson University and Medical University of South Carolina
Prof. Ian Stavness
University of Saskatchewan
Prof. Sidney Fels
University of British Columbia
Prof. Dario Cazzola
University of Bath
Amann Girrbach
Austria
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Opportunities
Interested in joining us?
We do not currently have a funded position advertised, but welcome inquiries from prospective doctoral and postdoctoral researchers whose interests closely align with our research areas. Formally advertised positions will be posted here.
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Contact
AI-Dent Vienna
Competence Center Artificial Intelligence in Dentistry
University Clinic of Dentistry · Medical University of Vienna
Sensengasse 2a · 1090 Vienna · Austria