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Balazs Feher
Dr. Balazs FeherJunior Scientist

University Clinic of Dentistry
Position: PHD Student

ORCID: 0000-0003-4386-6237


Artificial Intelligence; Biology; Data Interpretation, Statistical; Models, Statistical; Surgery, Oral

Research group(s)

Research interests

Balazs Feher is involved in preclinical and clinical research projects at the University Clinic of Dentistry and Vienna General Hospital. He is currently studying towards a Ph.D. on research grants issued by the ITI Foundation and the Osteology Foundation (Supervisors: Prof. Reinhard Gruber, Prof. Ulrike Kuchler, Prof. Johannes Grillari).

His preclinical research focuses on osteocytic signaling in bone regeneration. Osteocytes are key players in bone modeling/remodeling. Through elaborate signaling, osteocytes play a role in bone resorption and regeneration. However, several aspects of osteocytic signaling remain elusive. Prof. Gruber and Dr. Feher investigate osteocytic signaling using conditional knockout models in cooperation with the Center for Biomedical Research (Head: Prof. Bruno Podesser).

His clinical research under the supervision of Prof. Ulrike Kuchler focuses on data science in oral surgery. In an extensive collaborative effort with the Center for Medical Statistics, Informatics and Intelligent Systems (Head: Prof. Martin Posch), novel research methods are applied to the field of dental surgery, ranging from advanced statistical modeling for surgical risk prediction to artificial intelligence and convolutional neural networks for oral cyst/tumor diagnosis.

Techniques, methods & infrastructure

In the above preclinical projects, Prof. Gruber and Dr. Feher use osteocyte specific conditional knockouts. In all experimental models, micro-computed tomography, histology, and histomorphometry are used in cooperation with the Core Facility Hard Tissue and Biomaterial Research (Head: Dr. Stefan Tangl).

Under the guidance of Prof. Kuchler, Dr. Feher, in cooperation with Prof. Florian Frommlet, Prof. Georg Heinze, and Stefan Lettner, use large datasets and advanced analytics to estimate biological processes like postoperative cyst regeneration and predict surgical risk after procedures like wisdom tooth removal or dental implant placement. Moreover, deep learning, specifically convolutional neural networks are applied to digital radiographic imaging data to automatize the diagnosis of oral cysts and tumors.


  • Osteocytic RANKL expression in bone graft consolidation (2020)
    Source of Funding: Osteology Foundation, Young Researcher Grant
    Principal Investigator
  • Disabling osteocyte apoptosis in mice: Impact on calvarial bone regeneration (2019)
    Source of Funding: International Team for Implantology Foundation, Small Research Grant
    Principal Investigator

Selected publications

  1. Feher B, Frommlet F, Lettner S, Gruber R, Nemeth LE, Ulm C, Kuchler U. A volumetric prediction model for postoperative cyst shrinkage. Clinical Oral Investigations. 2021. (accepted)
  2. Feher B*, Apaza Alccayhuaman KA*, Strauss FJ, Lee JS, Tangl S, Kuchler U, Gruber R. Osteoconductive properties of flipped bilayer collagen membranes in rat calvarial defects. International Journal of Implant Dentistry. 2021. (accepted)
  3. Feher, B. et al., 2020. An advanced prediction model for postoperative complications and early implant failure. Clinical Oral Implants Research, 31(10), pp.928–935. Available at:
  4. Feher, B. et al., 2020. Angular changes in implants placed in the anterior maxillae of adults: a cephalometric pilot study. Clinical Oral Investigations, 25(3), pp.1375–1381. Available at: