Discovery of Parkinson's disease states and disease progression modelling: a longitudinal data study using machine learning

Severson, Kristen A.; Chahine, Lana M.; Smolensky, Luba; Dhuliawala, Murtaza; Frasier, Mark; Ng, Kenney; Ghosh, Soumya; Hu, Jianying

doi:10.1016/s2589-7500(21)00101-1

Cited by 56 publications

(38 citation statements)

References 30 publications

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“…It has recently become popular to directly train deep-learning-based classifier algorithms to turn raw signals into high-level categorical outputs, such as diagnostic 54 , 55 or clinical outcomes 41 , 56 , 57 . A direct clinical diagnostic prediction from the raw data could have been used in our context as well.…”

Section: Discussionmentioning

confidence: 99%

Intelligent wearable allows out-of-the-lab tracking of developing motor abilities in infants

et al. 2022

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Background Early neurodevelopmental care needs better, effective and objective solutions for assessing infants’ motor abilities. Novel wearable technology opens possibilities for characterizing spontaneous movement behavior. This work seeks to construct and validate a generalizable, scalable, and effective method to measure infants’ spontaneous motor abilities across all motor milestones from lying supine to fluent walking. Methods A multi-sensor infant wearable was constructed, and 59 infants (age 5–19 months) were recorded during their spontaneous play. A novel gross motor description scheme was used for human visual classification of postures and movements at a second-level time resolution. A deep learning -based classifier was then trained to mimic human annotations, and aggregated recording-level outputs were used to provide posture- and movement-specific developmental trajectories, which enabled more holistic assessments of motor maturity. Results Recordings were technically successful in all infants, and the algorithmic analysis showed human-equivalent-level accuracy in quantifying the observed postures and movements. The aggregated recordings were used to train an algorithm for predicting a novel neurodevelopmental measure, Baba Infant Motor Score (BIMS). This index estimates maturity of infants’ motor abilities, and it correlates very strongly (Pearson’s r = 0.89, p < 1e-20) to the chronological age of the infant. Conclusions The results show that out-of-hospital assessment of infants’ motor ability is possible using a multi-sensor wearable. The algorithmic analysis provides metrics of motility that are transparent, objective, intuitively interpretable, and they link strongly to infants’ age. Such a solution could be automated and scaled to a global extent, holding promise for functional benchmarking in individualized patient care or early intervention trials.

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Section: Discussionmentioning

confidence: 99%

Intelligent wearable allows out-of-the-lab tracking of developing motor abilities in infants

et al. 2022

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“…Similarly, comparable procedures, extracting specific features from available data, allowing the development of ML-based models for Parkinson's disease (PD). In fact, as reported for AD, several studies highlighted that through ML-based approaches applied to PD [279], it is possible to predict the progression of the disorder by employing serum cytokines [280], MRI [281], and walking tests [282]; to estimate the state of PD, employing longitudinal data [283]; to rate the main symptoms (resting tremor and bradykinesia) [284]; and to produce a correct diagnosis from EEG analysis [285,286] and from voice datasets [287,288].…”

Section: Ai/ml In Central Nervous System (Cns)-related Disordersmentioning

confidence: 99%

Artificial Intelligence in Translational Medicine

Brogi

Calderone

2021

IJTM

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The huge advancement in Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques, enable the scientific community to gain access to biological datasets, clinical data and several databases containing billions of pieces of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields, including the medical one. As a consequence of the mentioned scenario, scientific vocabulary was enriched by novel lexicons such as machine learning (ML)/deep learning (DL) and overall artificial intelligence (AI). Beyond the terminology, these computational techniques are revolutionizing the scientific research in drug discovery pitch, from the preclinical studies to clinical investigation. Interestingly, between preclinical and clinical research, translational research is benefitting from computer-based approaches, transforming the design and execution of translational research, resulting in breakthroughs for advancing human health. Accordingly, in this review article, we analyze the most advanced applications of AI in translational medicine, providing an up-to-date outlook regarding this emerging field.

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“…Additionally, research groups are applying ML and AI to improve cellular products ( Cohen-Karlik et al., 2021 ; Mota et al., 2021 ), biomaterial manufacturing ( An et al., 2021 ; Lee et al., 2020 ), and disease modeling ( Severson et al., 2021 ). Utilizing existing datasets, both from terrestrial experiments as well as LEO-based experiments ( da Silveira et al., 2020 ), ML approaches could be built into the automated LEO platforms.…”

Section: Automation Artificial Intelligence and Machine Learningmentioning

confidence: 99%

Biomanufacturing in low Earth orbit for regenerative medicine

et al. 2022

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Research in low Earth orbit (LEO) has become more accessible. The 2020 Biomanufacturing in Space Symposium reviewed spacebased regenerative medicine research and discussed leveraging LEO to advance biomanufacturing for regenerative medicine applications. The symposium identified areas where financial investments could stimulate advancements overcoming technical barriers. Opportunities in disease modeling, stem-cell-derived products, and biofabrication were highlighted. The symposium will initiate a roadmap to a sustainable market for regenerative medicine biomanufacturing in space. This perspective summarizes the 2020 Biomanufacturing in Space Symposium, highlights key biomanufacturing opportunities in LEO, and lays the framework for a roadmap to regenerative medicine biomanufacturing in space.

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Discovery of Parkinson's disease states and disease progression modelling: a longitudinal data study using machine learning

Cited by 56 publications

References 30 publications

Intelligent wearable allows out-of-the-lab tracking of developing motor abilities in infants

Intelligent wearable allows out-of-the-lab tracking of developing motor abilities in infants

Artificial Intelligence in Translational Medicine

Biomanufacturing in low Earth orbit for regenerative medicine

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