Growth and development of the dentofacial region is a key factor in good oral health. We investigate clinical application of novel surgical techniques to correct congenital or acquired deformities of the facial skeleton. We strive to obtain high quality of treatment with minimal risk of sequelae to surgery.

We operate a clinic for orofacial anomalies and our research domains focus on growth and progress of abnormal development, specifically in juvenile arthritis and other growth disturbances implying diagnosis, basic etiology, morbidity, imaging, timing and effect of orthopedic treatment and other interventions.

Various autoimmune and degenerative conditions can adversely affect facial growth and function. Our research activities focus on diagnosis, pathophysiology, and development of interdisciplinary management strategies for conditions like juvenile arthritis and idiopathic condylar resorption.

My research involves the use of novel technology in the clinical workflow, with a major focus on artificial intelligence (AI) through deep learning. Applications include image enhancement, automated image processing (e.g. segmentation), lesion detection, and risk assessment for treatment planning.

My research primarily focuses on the design of signal processing and deep learning techniques to process images and time series data, with applications in e.g. noise removal, artefact correction and image classification.

My research on Unilateral Cranial Microsomia, a craniofacial deformity, focuses on enhancing 3D visualisation and measurements to improve diagnostics and clinical decision-making. A method using mesh alignment and distance colour maps enables dynamic, high-resolution evaluation of treatment effects.

My research explores how virtual and mixed reality (VR/MR) can enhance dental and oral surgery education. These tools create realistic, interactive environments where dental students can practice skills and prepare for real-world procedures.

In my research, I apply different deep learning techniques to synthetically produce dental radiographs (or specific features thereof). This synthetic data can augment the training of Artificial intelligence (AI) models for tasks like segmentation and lesion detection.

Dentofacial deformities significantly impact occlusion and overall jaw function in affected patients. Our research focuses on using 3D technologies and robotics to improve surgical accuracy in the treatment of these patients. Our goal is to enhance outcomes tailored to each patient’s unique needs.

My research leverages deep learning to enhance cone-beam computed tomography (CBCT). It aims to improve image quality, reduce noise and artifacts, lower radiation dose, and optimize diagnostic accuracy for conditions like vertical root fractures or the evaluation of root canal anatomy.

My research focuses on advancing orthodontics and imaging, using magnetic resonance imaging (MRI) as a radiation-free diagnostic tool. We aim at integrating artificial intelligence and machine learning for enhancing precision in diagnosis and optimizing treatment planning in orthodontics using MRI.

New products, materials, and technology have the potential to transform the orthodontic workflow. My research focuses on developing and testing innovations with clinical applicability in orthodontics, with a particular emphasis on artificial intelligence.

My research explores imaging technologies – incl. dental-dedicated magnetic resonance imaging, intraoral scanning, cone beam and micro computed tomography - combined with biological evaluation to assess tissue regeneration related to oral surgery aiming to improve implant treatment decision-making.