The neuroradiological assessment of magnetic resonance imaging follow-ups for diseases such as multiple sclerosis or brain metastasis is extremely tedious and error prone.

 

     We developed an intelligent automation software that sets a new standard in the quality of follow-up reporting. The application, shaped by thousands of hours in the reading room, is tailored to fit the clinical workflow, increase productivity and reduce the number of errors. It integrates deep learning methods to automate most tedious tasks, such as lesion annotation, anatomical lesion position detection, lesion volume measurement, and report generation.

 

A powerful, intuitive user interface permits an efficient quality check by the radiologist, resulting in a high-quality report with systematic quantitative lesion load assessment.

 

​