Voiceprint and machine learning models for early detection of bulbar dysfunction in ALS

Tena, Alberto; Clarià, Francesc; Solsona, Francesc; Povedano, Mònica

doi:10.1016/j.cmpb.2022.107309

Cited by 4 publications

(1 citation statement)

References 17 publications

(34 reference statements)

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“…Very recently, acoustic analysis of voice has been proposed as a useful tool to improve the early detection of ALS bulbar impairment before clinical evident signs and to determine indices of disease progression both in the early stages of diagnosis and during the disease course [ 34 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Acoustic Voice Analysis as a Useful Tool to Discriminate Different ALS Phenotypes

Milella,

Sciancalepore,

Cavallaro

et al. 2023

Biomedicines

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Approximately 80–96% of people with amyotrophic lateral sclerosis (ALS) become unable to speak during the disease progression. Assessing upper and lower motor neuron impairment in bulbar regions of ALS patients remains challenging, particularly in distinguishing spastic and flaccid dysarthria. This study aimed to evaluate acoustic voice parameters as useful biomarkers to discriminate ALS clinical phenotypes. Triangular vowel space area (tVSA), alternating motion rates (AMRs), and sequential motion rates (SMRs) were analyzed in 36 ALS patients and 20 sex/age-matched healthy controls (HCs). tVSA, AMR, and SMR values significantly differed between ALS and HCs, and between ALS with prevalent upper (pUMN) and lower motor neuron (pLMN) impairment. tVSA showed higher accuracy in discriminating pUMN from pLMN patients. AMR and SMR were significantly lower in patients with bulbar onset than those with spinal onset, both with and without bulbar symptoms. Furthermore, these values were also lower in patients with spinal onset associated with bulbar symptoms than in those with spinal onset alone. Additionally, AMR and SMR values correlated with the degree of dysphagia. Acoustic voice analysis may be considered a useful prognostic tool to differentiate spastic and flaccid dysarthria and to assess the degree of bulbar involvement in ALS.

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Section: Introductionmentioning

confidence: 99%

Acoustic Voice Analysis as a Useful Tool to Discriminate Different ALS Phenotypes

Milella,

Sciancalepore,

Cavallaro

et al. 2023

Biomedicines

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Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective

Wu,

Xu,

Fang

et al. 2024

Arch Computat Methods Eng

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In the past three decades, biomedical engineering has emerged as a significant and rapidly growing field across various disciplines. From an engineering perspective, biomaterials, biomechanics, and biofabrication play pivotal roles in interacting with targeted living biological systems for diverse therapeutic purposes. In this context, in silico modelling stands out as an effective and efficient alternative for investigating complex interactive responses in vivo. This paper offers a comprehensive review of the swiftly expanding field of machine learning (ML) techniques, empowering biomedical engineering to develop cutting-edge treatments for addressing healthcare challenges. The review categorically outlines different types of ML algorithms. It proceeds by first assessing their applications in biomaterials, covering such aspects as data mining/processing, digital twins, and data-driven design. Subsequently, ML approaches are scrutinised for the studies on mono-/multi-scale biomechanics and mechanobiology. Finally, the review extends to ML techniques in bioprinting and biomanufacturing, encompassing design optimisation and in situ monitoring. Furthermore, the paper presents typical ML-based applications in implantable devices, including tissue scaffolds, orthopaedic implants, and arterial stents. Finally, the challenges and perspectives are illuminated, providing insights for academia, industry, and biomedical professionals to further develop and apply ML strategies in future studies.

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Precision medicine in ALS: Identification of new acoustic markers for dysarthria severity assessment

Dubbioso,

Spisto,

Verde

et al. 2024

Biomedical Signal Processing and Control

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Voiceprint and machine learning models for early detection of bulbar dysfunction in ALS

Cited by 4 publications

References 17 publications

Acoustic Voice Analysis as a Useful Tool to Discriminate Different ALS Phenotypes

Acoustic Voice Analysis as a Useful Tool to Discriminate Different ALS Phenotypes

Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective

Precision medicine in ALS: Identification of new acoustic markers for dysarthria severity assessment

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