Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge

Sieberts, Solveig K.; Schaff, Jennifer; Duda, Marlena; Pataki, Bálint Ármin; Sun, Ming; Snyder, Phil; Daneault, Jean‐François; Parisi, Federico; Costante, Gianluca; Rubin, Udi; Banda, Peter; Chae, Yooree; Neto, Elias Chaibub; Dorsey, E. Ray; Aydın, Zafer; Chen, Aipeng; Elo, Laura L.; Espino, Carlos; Glaab, Enrico; Goan, Ethan; Golabchi, Fatemeh Noushin; Görmez, Yasin; Jaakkola, Maria K.; Jonnagaddala, Jitendra; Клен, Рику; Li, Dongmei; McDaniel, Christian; Perrin, Dimitri; Perumal, Thanneer M.; Rad, Nastaran Mohammadian; Rainaldi, Erin; Sapienza, Stefano; Schwab, Patrick; Shokhirev, Nikolai; Venäläinen, Mikko S; Vergara‐Diaz, Gloria; Zhang, Yuqian; Wang, Yuanjia; Guan, Yuanfang; Brunner, Daniela; Bonato, Paolo; Mangravite, Lara M.; Omberg, Larsson

doi:10.1038/s41746-021-00414-7

Cited by 36 publications

(28 citation statements)

References 24 publications

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“…Not only has this improved the affordability of such technology but it has also opened up the potential for self-monitoring by patients with their own devices, conceptually representing a digital biomarker for disease progression. 101 Many scientifically calibrated wearable accelerometry devices are available on the market and have demonstrated the ability to capture parameters of gait, such as stride length, gait speed, and cadence, 99 with close agreement for similar variables captured with static systems, such as GaitRite. Wearable accelerometry devices have also been useful for assessing FoG.…”

Section: Wearable Devices and Machine Learningmentioning

confidence: 99%

Current Perspectives on the Assessment and Management of Gait Disorders in Parkinson’s Disease

Smith¹,

Brazier²,

Henderson³

2021

NDT

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Gait dysfunction is a key defining feature of Parkinson’s disease (PD), and is associated with symptoms of freezing and an increased risk of falls. In this narrative review, we cover the putative mechanisms of gait dysfunction in PD, the assessment of gait abnormalities, and the management of symptoms caused by the inherent difficulty in walking. Our understanding of the causes of gait problems in PD has progressed in recent times, moving from neurocognitive theory to correlates of affected neuronal pathways. In particular, this can be shown to correspond with abnormalities in responses to dual-task paradigms and dysfunction in cholinergic signaling. Great progress has been made in the sophistication and precision of gait assessment; however, it has firmly remained in the research domain. There is significant momentum behind wearable technologies that can be used by patients in their own environment, acting as digital biomarkers that can not only reflect progression but also independently discriminate PD from non-PD individuals. The treatment of gait dysfunction has historically relied on physical therapies and training combined with a view to mitigating the impact of such consequences as falls. Pharmacological therapies that are the mainstay of treatment in PD have tended to address symptoms like bradykinesia; however, optimization of dopaminergic therapies likely has a positive effect on quality of gait. Other targets have been assessed with the goal of improving gait, of which medications that improve cholinergic signaling appear most promising. Neuromodulation techniques are increasingly used in the form of deep-brain stimulation; however, standard targets, such as the globus pallidus interna, have a modest effect on gait. Considerable benefit has been seen through targeting the pedunculopontine nucleus, and a dual-target approach may be warranted. Stimulation of the spinal cord and brain through direct or magnetic approaches has been assessed, but requires further evidence.

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Section: Wearable Devices and Machine Learningmentioning

confidence: 99%

Current Perspectives on the Assessment and Management of Gait Disorders in Parkinson’s Disease

Smith¹,

Brazier²,

Henderson³

2021

NDT

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“…The authors in ( 81 ) developed a machine learning approach to predict the progression of PD using a signature of 27 inflammatory cytokines measured in blood serum. Furthermore, mobile phone gyroscope and accelerometer data have been used in combination with demographic and clinical data to predict different measures of Parkinson's disease symptom severity ( 82 ). Finally, in ( 83 ) a subgrouping of PD patients based on their disease trajectories, described via a variety of outcome scores, was suggested.…”

Section: The Emerging Future: Digital Biomarkers In Precision Neurologymentioning

confidence: 99%

Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease

et al. 2022

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Recent years have witnessed a strongly increasing interest in digital technology within medicine (sensor devices, specific smartphone apps) and specifically also neurology. Quantitative measures derived from digital technology could provide Digital Biomarkers (DMs) enabling a quantitative and continuous monitoring of disease symptoms, also outside clinics. This includes the possibility to continuously and sensitively monitor the response to treatment, hence opening the opportunity to adapt medication pathways quickly. In addition, DMs may in the future allow early diagnosis, stratification of patient subgroups and prediction of clinical outcomes. Thus, DMs could complement or in certain cases even replace classical examiner-based outcome measures and molecular biomarkers measured in cerebral spinal fluid, blood, urine, saliva, or other body liquids. Altogether, DMs could play a prominent role in the emerging field of precision medicine. However, realizing this vision requires dedicated research. First, advanced data analytical methods need to be developed and applied, which extract candidate DMs from raw signals. Second, these candidate DMs need to be validated by (a) showing their correlation to established clinical outcome measures, and (b) demonstrating their diagnostic and/or prognostic value compared to established biomarkers. These points again require the use of advanced data analytical methods, including machine learning. In addition, the arising ethical, legal and social questions associated with the collection and processing of sensitive patient data and the use of machine learning methods to analyze these data for better individualized treatment of the disease, must be considered thoroughly. Using Parkinson's Disease (PD) as a prime example of a complex multifactorial disorder, the purpose of this article is to critically review the current state of research regarding the use of DMs, discuss open challenges and highlight emerging new directions.

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“…In 2017, a part of the dataset has been used in the Parkinson's Disease Digital Biomarker DREAM Challenge [8]. The participants were tasked with feature extraction.…”

Section: Dream Challengementioning

confidence: 99%

Deep learning for Parkinson’s disease symptom detection and severity evaluation using accelerometer signal

Gutowski¹

2022

ESANN 2022 Proceedings

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This paper presents a neural network for predicting the severity/presence of Parkinson's disease motor symptomstremor, bradykinesia and dyskinesia, based on accelerometer signals collected while the patient is executing selected tasks. The suggested network uses accelerometer signals as input along with the type of completed task and the side the device is worn on. The data was collected in the Levodopa Response Study funded by MJFF. The model has been trained for every symptom separately and the results have helped to identify the tasks that result in the best accuracy of symptom detection and evaluation.

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Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge

Cited by 36 publications

References 24 publications

Current Perspectives on the Assessment and Management of Gait Disorders in Parkinson’s Disease

Current Perspectives on the Assessment and Management of Gait Disorders in Parkinson’s Disease

Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease

Deep learning for Parkinson’s disease symptom detection and severity evaluation using accelerometer signal

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