Image-guided scoliosis management – Focuses on treatment management and monitoring techniques for scoliosis, scaling from experimental proof-of-concept up to actual clinical implementation (TRL3–TRL9).
AI-driven image synthesis for spine regeneration – Focuses on MRI-to-CT conversion technology to safely guide the growth and regeneration of the juvenile scoliotic spine (TRL3–TRL9).
Liver cancer detection and quantification – Focuses on developing machine learning techniques to identify and measure liver cancer and metastases (TRL3–TRL9).
Pre-surgery hologram generation – Focuses on generating 3D hologram images of brain tumors to improve patient consultation and surgery planning (TRL3–TRL9).
Paediatric sarcoma data harmonization – Focuses on the AI-driven harmonization and segmentation of multicenter MRI data to support clinical decision-making in international trials (TRL3–TRL9).
Vertebral collapse prediction – Focuses on using artificial intelligence to predict vertebral collapse in patients suffering from multiple myeloma (TRL3–TRL9).
Orthopedic MRI diagnostics – Focuses on AI techniques for the diagnosis, monitoring, and treatment planning of various orthopedic conditions (TRL3–TRL9).
Real-time radiotherapy reconstruction – Focuses on deep learning models for real-time image reconstruction and motion estimation to enable MRI-guided radiotherapy for abdominal, thoracic, and cardiac tumors (TRL3–TRL9).
Organ motion determination – Focuses on using machine learning techniques to calculate and track internal organ motion during interventions (TRL3–TRL9).
Wearable signal hemodynamics – Focuses on machine learning algorithms that determine critical hemodynamic parameters directly from wearable device signals (TRL3–TRL9).
Data-driven tissue mechanics – Focuses on discovering data-driven models for tissue mechanics by utilizing MRI data (TRL3–TRL9).
Deep learning radiation dose planning – Focuses on using deep learning to optimize and automate radiation dose planning workflows (TRL3–TRL9).
