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Enabling medical research and AI through high quality data labelling

Medical professional working with multiple monitors displaying medical imaging data

CLIENTS

Balgrist University Hospital
Viz.ai
Covera Health

UNIVERSITY OF ZURICH

ETH COMPUTER VISION LAB

SERVICES

Labelata specialises in supporting academics, firms and hospitals with Medical research and AI projects

Medical Segmentation Examples

DATA LABELLING

We ensure quality and protect your data

Provide manual segmentation and labelling of any anatomical and/or pathological structures across all imaging modalities (e.g. MRI, CT, US etc.)

Medical Professional reviewing scans

CONSULTING &
QUALITY ASSURANCE

Technical advice & expertise from experienced radiologists and pathologists

Our network of radiologists with experience in AI projects provide quality assurance and can offer help and guidance to plan your project

Medical imaging equipment

DATA ACQUISITION

Acquire training data

We acquire the training data you need to perform research and development

Common Problems with
Medical Data Labelling

Prototyping icon

Prototyping new ideas is slow

Scaling icon

Scaling up products is difficult

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Expensive regulatory approvals

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Unreliable quality

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Slow and difficult communication

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Inability to deal with complexity

Labelata Logo
Network icon

Network of trained medical experts

high quality & reliable data

Scalable icon

Easily scalable

from small to big projects

Quality icon

Robust quality assurance

helps regulatory approvals

Expertise icon

Expertise

in complex 3D segmentations

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Seamless communication

projects managed by medical experts

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Highly flexible

fast adaptability & labels on demand

CASE STUDIES

Balgrist University Hospital

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.

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Only available in English

Viz.ai

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.

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Only available in English

Covera Health

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.

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Only available in English