Synergy between human expertise and artificial intelligence for better health care.
My research aims to harness the full potential of data science and artificial intelligence in oral medicine, while also advancing our understanding of the cellular and molecular processes underlying oral health and disease.
A computer vision architecture that estimates periodontal stability at the tooth and patient level.
This work uses a combined object detection and image classification architecture on intraoral radiographs to estimate periodontal stability, which traditionally requires both clinical and radiographic examination, from radiographic signal alone. The model uses single tooth-level results to estimate periodontal stability at the patient level.
A machine learning model that diagnoses jaw cysts and determines whether a tooth is involved.
In this international collaboration between twenty researchers from ten countries, we developed a machine learning model that diagnoses jaw cysts and classifies them into odontogenic (i.e., a tooth is involved) and non-odontogenic lesions, thereby aiding the surgical treatment planning process. The model achieved higher diagnostic performance than the human control group.
Advanced predictive modeling that improves the outcomes of wisdom tooth surgery.
Postoperative neuropathy is one of the most severe complications of wisdom tooth removal, a routine procedure in oral surgery. In this work, we developed the first prediction model that not only uses advanced penalized regression to identify risk factors but provides individual odds that a given patient will experience postoperative neuropathy.
“… a dedicated young professional with a strong focus on oral regeneration.”
— Prof. Reinhard Gruber