Digital Phenotyping in Clinical Neurology

Gupta, Anoopum S.

doi:10.1055/s-0041-1741495

Cited by 16 publications

(8 citation statements)

References 170 publications

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“…The field of quantitative phenotyping attempts to fill this gap by using sensors and devices to quantify aspects of motor and cognitive behavior that are important in cerebellar ataxias [5]. There has been increasing interest in the use of inertial measurement units (IMUs) that record accelerometer and gyroscope data to quantify gait or limb movement [6]- [11] features, computer mouse tasks that assess arm motor control [12], and eye tracking devices to quantify eye movement abnormalities [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Sensitive quantification of cerebellar speech abnormalities using deep learning models

Vattis¹,

Luddy²,

Ouillon³

et al. 2023

Preprint

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Objective: Objective, sensitive, and meaningful disease assessments are critical to support clinical trials and clinical care. Speech changes are one of the earliest and most evident manifestations of cerebellar ataxias. The purpose of this work is to develop models that can accurately identify and quantify these abnormalities. Methods: We use deep learning models such as ResNet 18, that take the time and frequency partial derivatives of the log-mel spectrogram representations of speech as input, to learn representations that capture the motor speech phenotype of cerebellar ataxia. We train classification models to separate patients with ataxia from healthy controls as well as regression models to estimate disease severity. Results: Our model was able to accurately distinguish healthy controls from individuals with ataxia, including ataxia participants with no detectable clinical deficits in speech. Furthermore the regression models produced accurate estimates of disease severity, were able to measure subclinical signs of ataxia, and captured disease progression over time in individuals with ataxia. Conclusion: Deep learning models, trained on time and frequency partial derivatives of the speech signal, can detect sub-clinical speech changes in ataxias and sensitively measure disease change over time. Significance: Such models have the potential to assist with early detection of ataxia and to provide sensitive and low- burden assessment tools in support of clinical trials and neurological care.

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Section: Introductionmentioning

confidence: 99%

Sensitive quantification of cerebellar speech abnormalities using deep learning models

Vattis¹,

Luddy²,

Ouillon³

et al. 2023

Preprint

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“…There is a great opportunity to reduce the size and cost of ALS trials, increase the population of individuals who can participate, and accelerate the evaluation of promising therapeutics through the development of new categories of sensitive quantitative motor outcome measures [13][14][15] . Quantitative motor outcome measures may be task-based (i.e., measuring behavior during performance of a specific task) or task-free, where an individual's natural behavior is measured passively and continuously at home.…”

Section: Introductionmentioning

confidence: 99%

Consumer-grade wearables and machine learning sensitively capture disease progression in amyotrophic lateral sclerosis

Gupta

Patel

Premasiri

et al. 2023

Preprint

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ALS causes degeneration of motor neurons, resulting in progressive muscle weakness and impairment in fine motor, gross motor, bulbar, and respiratory function. Promising drug development efforts have accelerated in ALS, but are constrained by a lack of objective, sensitive, and accessible outcome measures. Here we investigate the use of consumer-grade wearable sensors, worn on four limbs at home during natural behavior, to quantify motor function and disease progression in 376 individuals with ALS over a several year period. We utilized an analysis approach that automatically detects and characterizes submovements from passively collected accelerometer data and produces a machine-learned severity score for each limb that is independent of clinical ratings. The approach produced interpretable and highly reliable scores that progressed faster than the gold standard ALS Functional Rating Scale-Revised (-0.70 SD/year versus -0.48 SD/year), supporting its use as a sensitive, ecologically valid, and scalable measure for ALS trials and clinical care.

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“…Clinical assessment of ataxia is typically conducted by visual inspection of the neurological exam and assigned a severity score using clinical rating scales such as the Brief Ataxia Rating Scale [BARS, ( 1 )] and the Scale for the Assessment and Rating of Ataxia [SARA, ( 2 )]. Leveraging technology to provide quantitative motor and cognitive assessments can be of great value for early diagnosis, tracking disease progression, and feature characterization of the disease ( 53 ). Although very useful, clinical rating scales rely on subjective human assessments that depend on the experience and perspective of the clinician and, by design, are relatively imprecise as evidenced by poor test-retest and interrater reliability ( 3 – 5 ).…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification and Severity Estimation of Ataxia From Finger Tapping Videos

et al. 2022

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Digital assessments enable objective measurements of ataxia severity and provide informative features that expand upon the information obtained during a clinical examination. In this study, we demonstrate the feasibility of using finger tapping videos to distinguish participants with Ataxia (N = 169) from participants with parkinsonism (N = 78) and from controls (N = 58), and predict their upper extremity and overall disease severity. Features were extracted from the time series representing the distance between the index and thumb and its derivatives. Classification models in ataxia archived areas under the receiver-operating curve of around 0.91, and regression models estimating disease severity obtained correlation coefficients around r = 0.64. Classification and prediction model coefficients were examined and they not only were in accordance, but were in line with clinical observations of ataxia phenotypes where rate and rhythm are altered during upper extremity motor movement.

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Digital Phenotyping in Clinical Neurology

Cited by 16 publications

References 170 publications

Sensitive quantification of cerebellar speech abnormalities using deep learning models

Sensitive quantification of cerebellar speech abnormalities using deep learning models

Consumer-grade wearables and machine learning sensitively capture disease progression in amyotrophic lateral sclerosis

Automatic Classification and Severity Estimation of Ataxia From Finger Tapping Videos

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