Quantitative Imaging Biomarkers for Predictive Healthcare

The Quantitative Imaging Biomarkers for Predictive Healthcare Focus Group explores the potential of personalized and predictive medicine through applications of radiological imaging and artificial intelligence. Leveraging large-scale datasets from multiple sources, the team works to extract meaningful biomarkers beyond traditional anatomical assessments.

The group's research centers on developing AI-driven organ segmentation models, with an additional focus on body composition analysis. Their work aims to establish new indicators of overall health and aging by creating organ-specific biological age prediction algorithms and quantifying various aspects of body composition. By integrating imaging biomarkers with clinical and genetic data, they aim to improve risk assessment and validate organ age as a predictor for various diseases and treatment outcomes.

The group is led by Albrecht Struppler Clinician Scientist Fellow Lisa Adams, and works closely with cooperation partners Julia Schnabel (Computational Imaging and AI in Medicine, TUM), Daniel Rückert (Artificial Intelligence in Healthcare and Medicine, TUM), and Keno Bressem (German Heart Center, TUM). This multidisciplinary collaboration brings together expertise in radiology and computer science to advance research in quantitative imaging and predictive healthcare.