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Balazs Feher
Priv.-Doz. Dr. Balazs Feher, Ph.D.Senior Scientist

University Clinic of Dentistry
Position: Lecturer

ORCID: 0000-0003-4386-6237


Artificial Intelligence; Biostatistics; Bone Regeneration; Guided Tissue Regeneration; Oral Medicine; Oral Surgical Procedures

Research group(s)

Research interests

Balazs Feher conducts research at the Medical University of Vienna as well as the Harvard School of Dental Medicine. He is also a contributor to the ITU/WHO Focus Group on Artificial Intelligence for Health as well as a member of the European Association for Osseointegration's Junior Committee. In 2018, he was awarded the European Prize for Research in Implant Dentistry.

His preclinical research focuses on osteocytic signaling in bone regeneration. Osteocytes play a crucial role in bone resorption and regeneration. However, several aspects of their elaborate signaling remain elusive. Prof. Gruber and Dr. Feher investigate osteocytic signaling using Cre/loxP knockout models in cooperation with the Center for Biomedical Research.

His clinical research focuses on data science in oral surgery. In extensive collaborative efforts with multiple universities around the world, Prof. Kuchler and Dr. Feher apply novel research methods to the field of dental surgery, ranging from advanced statistical modeling for surgical risk prediction to artificial intelligence and convolutional neural networks for automated diagnosis.

Techniques, methods & infrastructure

In their preclinical projects, Prof. Gruber and Dr. Feher use Cre/lox knockout models to selectively disable parts of the osteocytic signaling pathway. For analysis, in vivo and ex vivo micro-computed tomography, histology, and histomorphometry are used in cooperation with the Core Facility Hard Tissue and Biomaterial Research.

In their clinical projects, Prof. Kuchler and Dr. Feher 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 used in cooperation with the Charité in Berlin as well as the Harvard School of Dental Medicine in Boston.


  • 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. et al. (2023) ‘The effect of osteocyte‐derived RANKL on bone graft remodeling: An in vivo experimental study’, Clinical Oral Implants Research, 34(12), pp. 1417–1427. Available at:
  2. Feher, B. et al. (2022) ‘Emulating Clinical Diagnostic Reasoning for Jaw Cysts with Machine Learning’, Diagnostics, 12(8), p. 1968. Available at:
  3. Feher, B. et al. (2021) ‘Prediction of post-traumatic neuropathy following impacted mandibular third molar removal’, Journal of Dentistry, 115, p. 103838. Available at:
  4. Feher, B. et al. (2021) ‘A volumetric prediction model for postoperative cyst shrinkage’, Clinical Oral Investigations, 25(11), pp. 6093–6099. Available at:
  5. 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: