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Longtitudinal Digital Biomarkers for Parkinson's

Longtitudinal Digital Biomarkers for Parkinson's

Principal supervisor: Prof George Roussos

Digital assessments of motor severity could improve the sensitivity of clinical trials and personalise treatment in Parkinson’s disease (PD) but have yet to be widely adopted. Their ability to capture individual change across the heterogeneous motor presentations typical of PD facing multiple changes notably from a computational perspective, including the ability to reliably capture representative symptom measurements using commodity sensors, controlling and when possible mitigating different sources of vairiability in the measurement process, and to establish the clinical validity of extracted features. To this end, at the Deppartment of Computer Science and Information Systems at Birkbeck, we haave developed PDkit, an open source software toolkit supporting the collaborative development of novel methods of digital assessment for Parkinson’s Disease. PDkit can process symptom measurements captured continuously by wearables (passive monitoring) or by high-use-frequency smartphone apps (active monitoring) and supports a variety of common data formats and apps specifically targetting PD.

Developing on this work, this project will consider specific longtitudinal digital biomarkers for PD phenotyping that is quantitative metrics of sympotm presentation calculatded by observing a subject repeatedly over a period of time. This research will also contribute to the objectives underlying the development of PDkit, specificaly to help address the current lack of algorithmic and model transparency in this area by facilitating open sharing of standardised methods that allow the comparison of results across multiple centres and hardware variations.

Candidate Requirements:

This PhD requires excellent software development skills, familiarity with core data science methods and technqiues and a desire to become familiar with clinical practice in Parkinson's Disease.

Key References:

[1] Jha, A., Menozzi, E., Oyekan, R. et al. The CloudUPDRS smartphone software in Parkinson’s study: cross-validation against blinded human raters. npj Parkinsons Dis. 6, 36 (2020).
[2] Stamate C, Saez Pons J, Weston D, Roussos G (2021) PDKit: A data science toolkit for the digital assessment of Parkinson’s Disease. PLoS Comput Biol 17(3): e1008833.

Further details about the project may be obtained from:

Principal supervisor: Prof George Roussos (Enable JavaScript to view protected content.)

Further information about PhDs at CSIS is available via this link.