Funded by NWO

The Future of Neuromuscular Diagnostics

ARISE-NMD develops advanced Artificial Intelligence for the analysis of electromyography (EMG) and builds the world's largest annotated EMG database.

Discover the Project
EMG Examination in progress

Current Practice

Needle electromyography (nEMG) is currently a manual, labor-intensive process reliant on visual interpretation.

Precision Diagnosis through AI

The ARISE-NMD project combines clinical expertise with advanced Machine Learning. Our goal is to eliminate subjectivity from the diagnosis and set a new standard for objective analysis.

Diagnostic Accuracy

Increasing sensitivity and reducing inter-rater variability through standardized AI models.

Efficiency

Reducing the time needed for expert evaluation and accelerating the diagnostic path for patients.

Clinical Biomarker

Creating objective biomarkers suitable for use in clinical trials for new therapies.

Our Mission

We are building a future where data and intelligence come together to transform patient care.

AI Platform

Development of a fully automated ML platform that can interpret nEMG with an accuracy comparable to or better than human experts.

Open Access Database

Creation of a large-scale, freely accessible, and annotated database with over 20,000 nEMG fragments for global research.

International Impact

Collaboration with a global consortium of 20 nEMG experts to validate the model and set the standard for international diagnostics.

The Consortium

ARISE-NMD is a collaboration between leading academic centers and industrial partners.

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Principal Investigators

Dr. M.R. Tannemaat

Dr. M.R. Tannemaat

LUMC

Clinical Neurophysiologist

Dr. C. Verhamme

Dr. C. Verhamme

Amsterdam UMC

Clinical Neurophysiologist

Dr. A. Kononova

Dr. A. Kononova

LIACS

Computer Science

Prof. Dr. H. Marquering

Prof. Dr. H. Marquering

Amsterdam UMC

Translational AI

Prof. Dr. T.H.W. Bäck

Prof. Dr. T.H.W. Bäck

LIACS

Natural Computing

PhD Candidates

Janne Luijten

Janne Luijten

LUMC / Amsterdam UMC

Technical Medicine

Mathieu Cherpitel

Mathieu Cherpitel

LIACS

Computer Science

Daniël van de Pavert

Daniël van de Pavert

Amsterdam UMC

Deep Learning