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

Dubbioso, Raffaele; Spisto, Myriam; Verde, Laura; Iuzzolino, Valentina Virginia; Senerchia, Gianmaria; De Pietro, Giuseppe; De Falco, Ivanoe; Sannino, Giovanna

doi:10.1016/j.bspc.2023.105706

Cited by 5 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It helps decision-makers evaluate the best options using branches and nodes to depict decision routes and uncertainty. Decision trees are frequently employed in risk analysis, project management, and operations research [90,91]. • Linear Programming: Linear programming makes finding the optimal result in a linear mathematical model easier with linear programming, a mathematical technique for optimizing decision variables subject to restrictions.…”

Section: Classical Decision-making Theoriesmentioning

confidence: 99%

Leveraging Artificial Intelligence for Enhanced Sustainable Energy Management

Kaur,

Kumar,

Singh

et al. 2024

JSE

View full text Add to dashboard Cite

The integration of Artificial Intelligence (AI) into sustainable energy management presents a transformative opportunity to elevate the sustainability, reliability, and efficiency of energy systems. This article conducts an exhaustive analysis of the critical aspects concerning the AI-sustainable energy nexus, encompassing the challenges in technological integration and the facilitation of intelligent decision-making processes pivotal for sustainable energy frameworks. It is demonstrated that AI applications, ranging from optimization algorithms to predictive analytics, possess a revolutionary capacity to bolster intelligent decision-making in sustainable energy. However, this integration is not without its challenges, which span technological complexities and socio-economic impacts. The article underscores the imperative for deploying AI in a manner that is transparent, equitable, and inclusive. Best practices and solutions are proposed to navigate these challenges effectively. Additionally, the discourse extends to recent advancements in AI, including edge computing, quantum computing, and explainable AI, offering insights into the evolving landscape of sustainable energy. Future research directions are delineated, emphasizing the importance of enhancing explainability, mitigating bias, advancing privacy-preserving techniques, examining socio-economic ramifications, exploring models of human-AI collaboration, fortifying security measures, and evaluating the impact of emerging technologies. This comprehensive analysis aims to inform academics, practitioners, and policymakers, guiding the creation of a resilient and sustainable energy future.

show abstract

Section: Classical Decision-making Theoriesmentioning

confidence: 99%

Leveraging Artificial Intelligence for Enhanced Sustainable Energy Management

Kaur,

Kumar,

Singh

et al. 2024

JSE

View full text Add to dashboard Cite

show abstract

“…The current clinical gold standard to track disease progression in ALS is the ALS Functional Rating Scale -Revised (ALSFRS-R) [5], a questionnaire comprising 12 questions across four functional domains impacted by ALS [6]: bulbar, fine motor, gross motor and respiratory. However, there is evidence that the ALSFRS-R scale may track disease progression in a nonlinear manner and may lack sensitivity in the early stages of bulbar disease onset [7,8,9]. For example, Van Unnik et al have pointed out that such survey-based outcomes have "limited ability to detect subtle changes over time" [10].…”

Section: Als and Speech Biomarkersmentioning

confidence: 99%

“…The assumption here is that a latent growth process (functional decline) is responsible for the change in observed measures. GCM fitting was performed in R. GCM curves for distinct cohorts can help identify differences in the longitudinal trajectory 9 The maximum distance at which all features would be combined into one single cluster was 5.4…”

Section: Longitudinal Analysismentioning

confidence: 99%

Multimodal Speech Biomarkers for Remote Monitoring of ALS Disease Progression

Neumann,

Kothare,

Ramanarayanan

2024

Preprint

View full text Add to dashboard Cite

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that severely impacts affected persons’ speech and motor functions, yet early detection and tracking of disease progression remain challenging. The current gold standard for monitoring ALS progression, the ALS functional rating scale - revised (ALSFRS-R), is based on subjective ratings of symptom severity, and may not capture subtle but clinically meaningful changes due to a lack of granularity. Multimodal speech measures which can be automatically collected from patients in a remote fashion allow us to bridge this gap because they are continuous-valued and therefore, potentially more granular at capturing disease progression. Here we investigate the responsiveness and sensitivity of multimodal speech measures in persons with ALS (pALS) collected via a remote patient monitoring platform in an effort to quantify how long it takes to detect a clinically-meaningful change associated with disease progression. We recorded audio and video from 278 participants and automatically extracted multimodal speech biomarkers (acoustic, orofacial, linguistic) from the data. We find that the timing alignment of pALS speech relative to a canonical elicitation of the same prompt and the number of words used to describe a picture are the most responsive measures at detecting such change in both pALS with bulbar (n= 36) and non-bulbar onset (n= 107). Interestingly, the responsiveness of these measures is stable even at small sample sizes. We further found that certain speech measures are sensitive enough to track bulbar decline even when there is no patient-reported clinical change, i.e. the ALSFRS-R speech score remains unchanged at 3 out of a total possible score of 4. The findings of this study have the potential to facilitate improved, accelerated and cost-effective clinical trials and care.

show abstract