Artificial intelligence in scans for deciding quickly on stroke treatment

In stroke, fast diagnosis and treatment is key to prevent oxygen shortage from causing irreversible cell death. CT imaging has been the predominant method of choice for diagnosis. The AMC and Nico-lab have developed Artificial Intelligence (AI) methods for automated CT image analysis tasks to improve the stroke workup in clinical practice. As an alternative to CT, MR imaging gives an unprecedented view on the stroke brain, holding a strong promise for improving diagnosis and treatment selection. The development of AI methods for MR images will enhance the decision making process for stroke patients. The aim is to develop stroke treatment decision support by AI for analysis of MR images, in which Amsterdam UMC joins forces with Nico-lab and Cloud Technology Solutions using the Google Cloud platform.

Stroke is a major cardiovascular disease with severe impact on quality-of-life and huge costs for society of €45B for an expected 1.2 million patients in the EU in 2025. The introduction of intra-arterial treatment in 2015 has revolutionized stroke care, with positive outcome in 30-70% of patients.

AI-supported decision making in medical imaging traverses over a cascade of connected tasks which all need to be trained on large datasets. By simultaneously rather than independently learning these tasks, an increase in efficiency of 60-70% can be achieved, holding a great promise for stroke treatment decision support.

Ten times accelerated MRI imaging and robust AI analysis will be developed and validated in the North-Holland, the Netherlands environment to demonstrate a short door-to-treatment time for patients. Multitask learning will be developed on the current task portfolio for CT-analysis and develop a multitask network for MRI. Distributed high performance will be demonstrated computing in the cloud will reduce analysis time to 3 minutes without a loss in accuracy. By improving and extending AI-based treatment decision support for stroke patients, this project will reduce the burden on decreasing healthcare personnel numbers and economic costs for society.