Stay Informed: Transforming Radiology with Structured Reporting and Data-Driven Approaches

Dive into our activities, projects, and product updates. Catch on the latest industry news and learn who we are as a company and as a team.

Creating evidence to tackle COVID-19

Our COVID-19 reading template is in use for two weeks, and its usage is multiplying. We are grateful for the close cooperation and feedback from existing and new mint Lesion™ users.

Our medical team went through all the available publications and created a reading template that can be used with ease by readers in your hospital or research center. As new papers and guidelines become available, we will update the reading template, retaining the structured data already collected.

mint Lesion™ extracts all primary data - including radiomics features - from images, and immediately links any measured value to its context and further related data of clinical significance. Together with clinical endpoints, the mineable data is ready for real-time analytics and AI.

Let mint Lesion™ assist you in generating the data and creating the evidence that will enable us to tackle COVID-19 jointly.

Request a demo

Related Resources

Related Resources

Radiologist using for medical image analysis

Advancing Real-World Federated Learning in Radiology

Federated Learning (FL) enables collaborative model training without data centralization – a crucial aspect for radiological image analysis where…

Schematic visualization of the federated learning study and its data infrastructure

RACOON: A Guide to Bridging the Gap Between Simulated and Real-World Federated Learning Research

Deep learning (DL) has become an important part of radiological image analysis. To train these deep-learning models, access to large and diverse…

This image shows a scan of pericardial effusion

Unveiling the Predictive Power of Pericardial Effusion in COVID-19 Outcomes

The COVID-19 pandemic wreaked havoc on healthcare systems worldwide, requiring a comprehensive understanding of disease progression for optimal…