Prof. Dr.-Ing. Yan Xia
W2-Professor
Einrichtungen
Kieferorthopädie
Lebenslauf, Veröffentlichungen und Auszeichnungen
Wissenschaftliche und edukative Tätigkeiten
I am a W2 Professor of Artificial Intelligence in Orthodontics, working at the intersection of computer vision, machine learning, and medical image analysis. My research spans the development and evaluation of state-of-the-art ML/DL methods for medical image generation, semantic and instance segmentation, image registration, disease detection and classification, and treatment-response prediction.
My current focus is AI for orthodontics and dentistry, designing clinically reliable AI algorithms that integrate dental imaging with patient data to improve diagnosis, risk assessment, and treatment planning. I work across the full pipeline, from robust representation learning and automated measurement to outcome prediction, and aim to translate AI advances into practical clinical tools.
My team has been working on several research directions to unlock the next wave of AI in orthodontics and dentistry:
•Large-scale self-supervised and foundation-model pretraining: learning strong representations from unlabeled cephalograms, panoramic, CBCT, and intraoral scans to reduce annotation burden and improve robustness across devices and clinics.
•Multi-task and multi-modal learning: unified models that jointly perform landmarking, segmentation, abnormality detection, and measurement, integrating images with EHR/clinical notes, and 3D oral scans, to improve generalization and clinical utility.
•Anatomy- and physics-informed networks: embedding cephalometric and craniofacial constraints (geometry, symmetry, topological consistency, tooth-jaw relationships) into model architectures and loss functions to enforce biologically plausible predictions.
•Longitudinal modeling and treatment-response prediction: learning from time-series records (pre-, mid-, post-treatment) to forecast growth trajectories, anchorage loss risk, root resorption risk, and likely occlusal outcomes under different treatment options.
•3D image analysis such as robust tooth/airway/TMJ segmentation, cephalometry in 3D, automated surgical/orthognathic planning support, and registration between 2D/3D modalities for comprehensive assessment.
Introduction to Artificial Intelligence in Medicine
Our course “Introduction to Artificial Intelligence in Medicine” is funded by the VHB under the special initiative “KI-Kompetenzen im Studium.” The course integrates technical and clinical perspectives to help students work effectively with AI in healthcare. It covers core concepts, applications, ethics, and future trends, focusing on AI literacy and clinical relevance rather than building algorithms from scratch. Students will learn how AI systems process data, where AI is used in medicine, current limitations, and key ethical and regulatory issues. They will also gain skills to evaluate AI literature, perform exploratory data analysis, train standard models, assess performance and feasibility, and collaborate with AI developers to identify practical opportunities.
