— Explore the future of sensing

PHC

How will trends affect lives and behaviors and influence the daily lives of our patients? How connected will our world be in the future and what could that mean for the future solutions for diabetics?

Apply until April 18, 2021 / Xplorers Camp on May 10, 2021 

Question to be solved

How might a future reality and a future solution for Type 1 and Type 2 diabetics look like to fit into new realities in a seamless way?​ ​As digitalization in all aspects of our lives and also in the healthcare industr​y increases, we want to know what the future of diabetes disease progression monitoring, and continuous blood glucose monitoring will look like.This is why we need you!

Scope 1:

What is your perspective on the future management of type 1 diabetes and continuous glucose monitoring? What trends do you see that will highly influence how we look at diabetes, and subsequently how might a solution look like to blend into the lives of Type 1 diabetics seamlessly considering aspects like energy supply, helpful additional factors like activity, temperature or others? How will data be exchanged and how would a constantly measuring solution be applied to the body? Interested in exploring? We want to know your perspective!

Scope 2:

Not convinced yet? Then you might want to think into what does the future hold for diabetes management and how could trends also improve life for Type 2 patients? How might the ongoing digitalization effect of sensing and subsequently which sensors (e.g. non-invasive) and factors could play a part in this growing segment? An example use case is type 2 on an oral medication who is at risk of progressing to insulin treatment is looking for a convenient and painless way to track their blood sugar levels without the hassle of pricking their finger. A wearable platform, in combination with other products can provide patients (and their physicians) with a better understanding of how their daily activities (medication compliance, diet, exercise, etc.) impacts their blood sugar and progression toward insulin treatment. Information delivered by the wearable platform could help them take better control of their disease and reduce the odds they progress to insulin treatment. The device could also be helpful for other patients (earlier in their disease journey) taking OADs seeking to improve their blood sugar levels and prevent disease progression. Eager to explore? Let us know your ideas and solutions!

We are looking forward to learning about your ideas and your vision!

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

  • Creativity and imagination
  • Great collaboration skills
  • Agile and flexible mindset
  • Customer Centric perspective
  • Systemic thinking
  • Diabetes know how basics required
  • Technical & technological understanding of sensing technologies and techniques

Mentors

Dr. David Krey
Senior Innovation Lead, Diabetes Care Global R&D Innovation

Dr. Nigel Surridge
Research Fellow, Diabetes Care Global R&D Innovation

Dr. Bernd Schneidinger
International Business Leader Glucose Monitoring Solutions, Diabetes Care Global Strategy & Customer Solutions

Jim Lefevre
International Business Leader Pre-Diabetes & OAD Diabetes Care Global Strategy & Customer Solutions

Form of Cooperation

Preferred scale: e.g. 6-12 months full-time (flexible models are also possible)
Possible format: working student, internship, bachelor or master thesis

How to present your Idea

Show us how you would approach the problem in 3 to 5 slides and a pitch about one of the two sopes . We do not expect a bullet-proof solution to the problem, however a strong perspective of your vision, the assumptions you made and reasons for your assumptions. You are free to choose the presentation medium (podcast, video, powerpoint, etc.) whatever conveys your vision and idea best.

 

Be as creative as possible – Be aware you only have 10 minutes to sell your idea

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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.