Collaborative study in deep learning for predicting
disease activity in multiple sclerosis (deepMS)
Claudia Chien, Fabian Eitel, Alexander Brandt, Friedemann Paul,
Experimental and Clinical Research Center, Max Delbrück Center for Molecular Medicine & Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin,
Humboldt-Universität zu Berlin, and Berlin Institute of Health
NeuroCure Clinical Research Center, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health
Department of Psychiatry and Psychotherapy, Berstein Center for Computational Neuroscience, Berlin Center for Advanced Neuroimaging, Charité-Universitätsmedizin Berlin,
Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health
Department of Neurology, University of California Irvine, Irvine, CA, USA
Department of Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health
Objective and Aims:
Collecting and procuring an MRI and clinical data repository from centres around the world for collaborative, expert
scientic contributions in predicting disease activity in MS patients. With this data repository we aim to develop new
MRI-derived clinically applicable biomarkers using a combination of convolutional neural networks (CNNs) and
Results (from pilot study, see reference (1)):
- a classication accuracy of 87%, when ne-tuned from
Alzheimer's Disease and transferred to MS data
- the algorithm independently learned patterns in MRIs
to separate MS patients from healthy participants
- visualisation analysis revealed that CNNs focused not
only on lesion voxels, but also incorporates information
from lesion location, as well as non-lesional white and
grey matter areas, such as the thalamus.
Initial results show immense promise in using CNNs to
automatically evaluate brain MRIs in MS patients. In this
collaborative study, we aim to extend our methods now
for the more complex task of predicting individualized
disease activity and prognosis (Figure 1). For robust,
reliable, and clinically relevant future applications,
further data contribution is required. Together with
neurologists, neuroradiologists, and researchers, the
data will be analysed in a collaborative, expert-led, and
1. Eitel F, Soehler E, Bellmann-Strobl J, Brandt AU, Ruprecht K,
Giess RM, et al. Uncovering convolutional neural network
decisions for diagnosing multiple sclerosis on conventional
MRI using layer-wise relevance propagation. arXiv:190408771
[cs] [Internet]. 2019 Sep 5, accepted in NeuroImage: Clinical
This project is supported by the Deutsche Multiple Sklerose
Bundesverband e.V., Deutsche Forschungsgemeinschaft
(389563835) and Manfred and Ursula Müller-Stiftung.
- 3D T1-, T2-
type & date,
Clinical & Radiological
Figure 1. Overview of the methods and aims of deepMS.
Conicts of Interest:
CC has nothing to disclose.
FE has nothing to disclose.
AUB is cofounder and holds shares of Motognosis GmbH and Nocturne GmbH. He is
named as inventor on several patent applications describing multiple sclerosis serum
biomarkers, perceptive visual computing-based motion analysis and retinal image ana-
lysis; none of this is related to the present abstract.
FP has received research support from Bayer, Novartis, Biogen Idec, Teva, Sano-
Aventis/Genzyme, Merck Serono, Alexion, Chugai, Arthur Arnstein Foundation Berlin,
Guthy Jackson Charitable Foundation and the US National Multiple Sclerosis Society;
has received travel funding and/or speaker honoraria from Bayer, Novartis, Biogen Idec,
Teva, Sano- Aventis/Genzyme and Merck Serono; and has consulted for Sano Genzy-
me, Biogen Idec and MedImmune; none of this is related to the present abstract.
KR has nothing to disclose.
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