David Golan
Co-Founder, Viz.ai
"Viz derived great value from Labelata's work. It allowed us to jumpstart a project much faster than we would have otherwise and probably shaved 1-2 months off our delivery timeline which is significant. Not only was the speed and quality of the segmentations impressive, we particularly liked the ease of communication with the Labelata team."
Ender Konukoglu
Professor of Biomedical Image Computing at ETH-Zurich
"To advance cutting-edge research in the field of medical image analysis, very specific data sets are required that demand significant expertise to generate. Labelata was able to provide this service and delivered datasets curated to our needs. The proactive and flexible mindset of Labelata facilitated the process greatly. We were very happy with the results both in terms of how fast they were produced and their quality"
Jonas Widmer
Head of Spine Biomechanics, Balgrist University Hospital & CTO at Moving Spine AG
"The quality of Labelata's segmentations has been very good and its team has been excellent at adapting to different patient pathologies and image qualities. In particular, the streamlined process and timely execution of Labelata's 24 hour segmentation service gives me confidence to integrate Labelata into clinical applications and help scale up and commercialise our research"
Benjamin Odry
Chief AI Officer, Covera Health
"Labelata's Musculoskeletal experts very clearly understood our labeling needs and we were able to use their expertise for the benefit of our projects."
CASE STUDIES
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How to scale up and commercialise research and development
Labelata has supported the Spine Biomechanics Group to publish multiple clinical studies and provided the comfort and reliability to scale their products. They have now created a spin-off to commercialise their research.
Only available in English
How to develop FDA-cleared algorithms to analyse medical imaging data
Viz.ai was able to launch the Left Ventricle/Right Ventricle ratio algorithm (an FDA approved AI algorithm). Labelata's segmentations of the heart helped Viz.ai develop an algorithm that automatically calculates the ratio between the two ventricles, a critical measurement to evaluate the severity of pulmonary embolism.
Only available in English
How to establish ground truth for Medical AI models?
Labelata's team of highly skilled musculoskeletal experts helped Covera Health to produce ground truth. Using this training data, Covera was able to build AI models to develop software providing quality insights on Musculoskeletal ('MSK') radiology.
Only available in English