KrauthammerLab
University of Zurich & University Hospital Zurich
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AI-assisted diagnosis in rheumatology
Today, in every aspect of our lives, everything we do leaves a digital footprint. Health-care institutions are also increasingly …
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 …
An introduction to long-read sequencing.
Helping reduce idle time in the USZ Radiology department
Comparing neural-networks versus logistic regression for predicting readmission.
Assessing the quality of online health information with AI.
Cancer on the cell level.
Novel computational method for drug-drug interaction predictions which are an important consideration for patient treatment.
Using deep learning for automatically generated medical reports describing radiological images.
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.