— A deep learning-based approach for detection of neurological disease patterns using a draw-a-shape test


Interested in machine learning, and analysing digital health datasets to uncover hidden disease progression? Want to make a tangible impact on the lives of people living with neurological diseases? Then help us apply deep learning methods to develop better digital measurements of impairment!


Apply until April 18, 2021 / Xplorers Camp on May 3, 2021 

Question to be solved

Can we use deep learning methods to derive digital measures that capture information about neurological impairments?

General Background

Roche is at the forefront of the digital health space, with regular deployments of digital health solutions in clinical studies of neurological diseases (Lipsmeier et al., 2018, Midaglia et al. 2018). One of the flagship tests for assessing fine motor control is the Draw-a-Shape test (Creagh et al. 2020), which involves tracing pre-determined shapes on a smartphone screen. Processing of the resulting touch data allows us to determine features that are associated with upper limb motor impairment and disease progression in neurological diseases.
The aim of this challenge is to determine whether machine learning approaches can be used to detect disease patterns that have not been captured by the existing Draw-a-Shape features. Both generative and discriminative modelling could be used. Note that the touch data can be thought of as a temporal sequence of inputs.

Data Types & Technologies

The input data will consist of touch traces of pre-defined shapes on a smartphone screen. Each test consists of the following shapes: Line (top to bottom), line (bottom to top), square, circle, figure eight, spiral. The data is collected during clinical studies of people with neurological disorders. Each participant performs the test daily for the duration of the study. The following are some approaches that could be considered for this challenge, but we are open to other solutions:

  • Neural decomposition methods (Märtens & Yau, 2020).
  • Classifier predicting clinical measures (such as tests of hand function).
  • Sequence processing models such as LSTM networks (Hochreiter & Schmidhuber, 1997), Temporal-Convolutional Networks (Bai et al., 2018), and Transformers (Vaswani et al., 2017).

Supporting Material or Links

  • Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271.
  • Baker et al. Digital health: Smartphone-based monitoring of multiple sclerosis using Floodlight. https://www.nature.com/articles/d42473-019-00412-0 [Accessed 20 November 2020].
  • Creagh, A. P., Simillion, C., Scotland, A., Lipsmeier, F., Bernasconi, C., Belachew, S.,van Beek, Baker, J., et al. (2020). Smartphone-based remote assessment of upper extremity function for multiple sclerosis using the Draw a Shape Test. Physiological Measurement, 41(5), 054002.
  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
  • Lipsmeier, F. et al. (2018). Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson’s disease clinical trial. Mov Disord. 33(8):1287-1297.
  • Märtens, K., & Yau, C. (2020). Neural Decomposition: Functional ANOVA with Variational Autoencoders. In S. Chiappa & R. Calandra (Eds.), Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (Vol. 108, pp. 2917–2927). PMLR.
  • Midaglia et al. (2019). Adherence and Satisfaction of Smartphone- and Smartwatch-Based Remote Active Testing and Passive Monitoring in People With Multiple Sclerosis: Nonrandomized Interventional Feasibility Study. J Med Internet Res 21(8):e14863.
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008).

Needed Skills

  • Good communication skills
  • Solid Python skills
  • Experience with deep learning frameworks, e.g. PyTorch, TensorFlow
  • Understanding of the statistical underpinnings of machine learning methods


Frank Dondelinger
Data Analysis Lead MS, Digital Biomarkers, pREDi

Marcin Elantkowski
Principal Associate Data Analyst, Digital Biomarkers, pREDi

Form of Cooperation

Internship, 3 months. Preference full-time, part-time possible.

How to present your Idea

Preferred presentation format: 3-5 slides. Other forms of presentation are possible if they serve a purpose. Knowledge of Python and machine learning will be checked during the pitch sessions.

By sending this to us via the submit button you agree to the following:


  • you confirm that you are the author of the submission and entitled to dispose of rights of use and exploitation of the contents of your submission, and that you have not yet granted any rights of use and exploitation to third parties that would be infringed by your submission;
  • you grant to Roche Diagnostics GmbH the unrestricted, sublicensable and exclusive right  to use and exploit your submission by all means known today or in the future. This includes without limitation the rights to reproduce, distribute, and exhibit your submission, as well as the right to communicate your submission to the public. You also grant to Roche Diagnostics GmbH the right to edit the submission, to translate it, and to create abbreviations and summaries (abstracts); the aforesaid rights to use and exploit also apply to such edited versions, translations, abbreviations and summaries. 
  • Roche Diagnostics GmbH will designate you as the author of the submission, and will recognize and respect your moral rights in the submission.
  • The relationship you enter into by sending this via the submit button is governed by the laws of the Federal Republic of Germany, and the courts of Germany have international jurisdiction for any disputes arising under or in connection with this relationship..


Any problems with the submit button? Please send your submission to healthcare.xplorers@roche.com.


Further information on our privacy policy can be found here.


Any problems with the submit button?
Please send your submission to healthcare.xplorers@roche.com.


Further information on our privacy policy can be found here.