Artificial Intelligence; Biology; Data Interpretation, Statistical; Models, Statistical; Surgery, Oral
Balazs Feher conducts research at Vienna General Hospital and the University Clinic of Dentistry. He is currently studying towards a Ph.D. in Endocrinology and Metabolism on competitive grants issued by the International Team for Implantology Foundation and the Osteology Foundation (Supervisors: Prof. Reinhard Gruber, Prof. Ulrike Kuchler, Prof. Johannes Grillari). He is 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. 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 focuses on data science in oral surgery. In extensive collaborative efforts with various data science institutions, Prof. Kuchler and Dr. Feher apply novel research methodsto 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 their 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).
In their clinical projects, Prof. Kuchler and 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
- Disabling osteocyte apoptosis in mice: Impact on calvarial bone regeneration (2019)
Source of Funding: International Team for Implantology Foundation, Small Research Grant
- Feher B, Frommlet F, Gruber R, Hirtler L, Ulm C, Kuchler U. Resonance frequency analysis of implants placed in condensed bone. Clinical Oral Implants Research. 2021 Jul 7 accepted.
- Feher B, Frommlet F, Lettner S, Gruber R, Nemeth LE, Ulm C, Kuchler U. A volumetric prediction model for postoperative cyst shrinkage. Clin Oral Investig. 2021 Apr 20.
- Feher B*, Apaza Alccayhuaman KA*, Strauss FJ, Lee JS, Tangl S, Kuchler U, Gruber R. Osteoconductive properties of upside-down bilayer collagen membranes in rat calvarial defects. Int J Implant Dent. 2021 Jun 7.
- Feher B, Lettner S, Heinze G, Karg F, Ulm C, Gruber R, Kuchler U. An advanced prediction model for postoperative complications and early implant failure. Clin Oral Implants Res. 2020 Oct;31(10):928-935.
- Feher B, Gruber R, Gahleitner A, Celar A, Necsea PL, Ulm C, Kuchler U. Angular changes in implants placed in the anterior maxillae of adults: a cephalometric pilot study. Clin Oral Investig. 2021 Mar;25(3):1375-1381.