KrauthammerLab
University of Zurich & University Hospital Zurich
AI-assisted diagnosis in rheumatology
We are proud to be part of the UZH University Research Priority Programs (URPP) where we together with the Schwank lab investigate the …
Since the early days of computing, healthcare professionals have dreamt of using the vast storage and processing powers of computers to …
Helping reduce idle time in the USZ Radiology department
This study investigates the use of cfDNA sequencing to monitor tumor dynamics in patients undergoing high-dose radiotherapy, revealing correlations between genetic alterations and clinical outcomes.
We developed machine learning models, PRIDICT2.0 and ePRIDICT, to predict prime editing efficiency, offering a robust tool for optimizing genome editing strategies across diverse chromatin contexts.
The first objective of this study was to implement and assess the performance and reliability of a vision transformer (ViT)-based deep-learning model, an ‘off-the-shelf’ artificial intelligence solution, for identifying distinct signs of microangiopathy in nailfold capilloroscopy (NFC) images of patients with SSc. The second objective was to compare the ViT’s analysis performance with that of practising rheumatologists.