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Conference ·
Using artificial intelligence and multimodal imaging to understand multiple sclerosis
How data-driven imaging can connect multiple sclerosis heterogeneity with prognosis and clinically meaningful subtypes.
2nd Inflammation & Imaging Symposium · Multiscale Imaging Centre, Münster, Germany
- Delivered
- Event
- 2nd Inflammation & Imaging Symposium
- Place
- Multiscale Imaging Centre, Münster, Germany
- Format
- Conference
- Source deck
- 26 slides
From the slides
Deck notes
Presented at the Multiscale Imaging Centre, this talk brought together artificial intelligence and multimodal imaging to examine heterogeneity in multiple sclerosis. The emphasis was on disease classification, prognosis and robust translation.
The question
Can patterns across imaging modalities provide a more useful account of multiple sclerosis than conventional clinical categories?
Deck outline
- The problem of disease heterogeneity
- Limits of clinical phenotype labels
- Regional atrophy and imaging-derived patterns
- Data-driven multiple sclerosis subtypes
- Prognosis and phenotypic conversion
- Algorithmic bias, robustness and translation
Take-away
Data-driven imaging patterns offer a route from heterogeneous scans to more informative disease subtypes and individual prognosis.
