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Post-Trial Services

Trial Enrichment

Machine learning assisted clinical trials by choosing patients at higher risk of disability progression or specific biological profiles to choose the best treatment for patients.

Causal Modelling

Understanding mechanism of action of treatments in different disorders by applying machine learning to clinical trials.

Data-Driven Patient Staging

Stratifying patients into groups according to the stage of the disease progression, as an important step toward individualised decision making in research studies, clinical trials, and in the near future, clinical practice.

Biomarker Integration

Multimodal machine learning models, integrating MRI, blood biomarkers, and other emerging biomarkers to provide holistic picture of multiple sclerosis and neurodegenerative disorders.

Machine Learning Prognosticating

Using routinely available data to detect high risk patients who may benefit from stronger treatments or clinical trials of high efficacy treatments, while minimising adverse events for those with milder disease course.

Brain Age Prediction

Providing the expected chronological age of MRI scans that may enable treatment response prediction, and prediction of disability.

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