Algorithm for the identification of treatment options: Implementation of a prototypical workflow process for the identification of epilepsy lesions requiring treatment using MRI data.
Neuroradiological sectional imaging (computed tomography; magnetic resonance imaging, MRI) of the brain is of central diagnostic importance in numerous neurological diseases. The outcome directly impacts therapeutic decisions and indirectly influences the course of disease. However, the range of epileptogenic lesions is not commonly known among radiologists, which can lead to missed treatment opportunities. Doctors who perform neuroradiological cross-sectional imaging (usually radiologists) are reaching the limits of their ability to do this job as their workload increases and examination procedures become more complex. Therefore, it is hoped that AI-supported evaluation methods will be able to support the detection of therapy-relevant findings in cross-sectional imaging.
The pilot scheme aims to achieve AI-supported MRI diagnoses and assess how radiologists and neurologists incorporate the findings into their care process. This will be tested in two studies (retrospective and prospective) for its practical feasibility and acceptance.
- Procedure model for interaction at human-AI-human interfaces in workflow (IAW)
- Interview and conversation guidelines for involving employees, works and personnel councils, and other co-determination bodies (GA RUB/IGM)
- Role training for AI developers and AI users along the competence dimensions (IAW)