Toolkit to Examine Lifelike Language v.2.0: Optimizing Speech Biomarkers of Neurodegeneration.
Introduction: The Toolkit to Examine Lifelike Language (TELL) is a web-based application providing speech biomarkers of neurodegeneration. After deployment of TELL v.1.0 in over 20 sites, we now introduce TELL v.2.0. Methods: First, we describe the app's usability features, including functions for c...
| Publicado en: | Dementia & Geriatric Cognitive Disorders Vol. 54; no. 2; pp. 96 - 109 |
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| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | pictorial review tables/charts Journal Article |
| Publicado: |
Karger AG
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184599860&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184599860 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14208008 GF6 jtl: Dementia & Geriatric Cognitive Disorders issn: 14208008 maglogo: N pubinfo: dt: 2025 vid: 54 iid: 2 pid: 2485 pub: Karger AG artinfo: ui: 184599860 180619903 184599860 184599860 10.1159/000541581 184599860 ppf: 96 ppct: 13 formats: tig: atl: Toolkit to Examine Lifelike Language v.2.0: Optimizing Speech Biomarkers of Neurodegeneration. aug: au: García, Adolfo M. Ferrante, Franco J. Pérez, Gonzalo Ponferrada, Joaquín Sosa Welford, Alejandro Pelella, Nicolás Caccia, Matías Belloli, Laouen Mayal Louan Calcaterra, Cecilia González Santibáñez, Catalina Echegoyen, Raúl Cerrutti, Mariano Javier Johann, Fernando Hesse, Eugenia Carrillo, Facundo affil: Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina sug: subj: Neurodegenerative Diseases Diagnosis Speech and Language Assessment Biological Markers Digital Technology Implementation Science Videoconferencing Episodic Memory Motor Skills Functional Connectivity ab: Introduction: The Toolkit to Examine Lifelike Language (TELL) is a web-based application providing speech biomarkers of neurodegeneration. After deployment of TELL v.1.0 in over 20 sites, we now introduce TELL v.2.0. Methods: First, we describe the app's usability features, including functions for collecting and processing data onsite, offline, and via videoconference. Second, we summarize its clinical survey, tapping on relevant habits (e.g., smoking, sleep) alongside linguistic predictors of performance (language history, use, proficiency, and difficulties). Third, we detail TELL's speech-based assessments, each combining strategic tasks and features capturing diagnostically relevant domains (motor function, semantic memory, episodic memory, and emotional processing). Fourth, we specify the app's new data analysis, visualization, and download options. Finally, we list core challenges and opportunities for development. Results: Overall, TELL v.2.0 offers scalable, objective, and multidimensional insights for the field. Conclusion: Through its technical and scientific breakthroughs, this tool can enhance disease detection, phenotyping, and monitoring. Plain Language Summary: Neurodegenerative disorders (NDs), such as Alzheimer's and Parkinson's disease, are a leading cause of disability, caregiver stress, and financial strain worldwide. The number of cases, now estimated at 60 million, will triple by 2050. Early detection is crucial to improve treatments, management, and financial planning. Unfortunately, standard diagnostic methods are costly, stressful, and often hard to access due to scheduling delays and availability issues. A promising alternative consists in digital speech analysis. This affordable, noninvasive approach can identify NDs based on individuals' voice recordings and their transcriptions. In 2023, we launched the Toolkit to Examine Lifelike Language (TELL), an online app providing robust speech biomarkers for clinical and research purposes. This paper introduces TELL v.2.0, a novel version with improved data collection, encryption, processing, storing, download, and visualization features. First, we explain the app's basic operations and its possibilities for online and offline data collection. Second, we describe its language survey, which covers questions about demographics as well as language history, usage, competence, and difficulties. Third, we describe TELL's speech tests, which assess key clinical features. Fourth, we outline the app's functions for analyzing, visualizing, and downloading data. We finish by discussing the main challenges and future opportunities for TELL and the speech biomarker field. With this effort, we hope to boost the use of digital speech markers in medical and research fields. pubtype: Academic Journal doctype: pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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