SAMI: an M-Health application to telemonitor intelligibility and speech disorder severity in head and neck cancers.

Autor:	Quintas S; IRIT, Université de Toulouse, CNRS, Toulouse INP, UT3, Toulouse, France., Vaysse R; IRIT, Université de Toulouse, CNRS, Toulouse INP, UT3, Toulouse, France., Balaguer M; IRIT, Université de Toulouse, CNRS, Toulouse INP, UT3, Toulouse, France., Roger V; IRIT, Université de Toulouse, CNRS, Toulouse INP, UT3, Toulouse, France., Mauclair J; IRIT, Université de Toulouse, CNRS, Toulouse INP, UT3, Toulouse, France., Farinas J; IRIT, Université de Toulouse, CNRS, Toulouse INP, UT3, Toulouse, France., Woisard V; IUC Toulouse, CHU Toulouse, Service ORL de l'Hôpital Larrey, Toulouse, France.; Laboratoire de NeuroPsychoLinguistique, UR 4156, Université de Toulouse, Toulouse, France., Pinquier J; IRIT, Université de Toulouse, CNRS, Toulouse INP, UT3, Toulouse, France.
Jazyk:	angličtina
Zdroj:	Frontiers in artificial intelligence [Front Artif Intell] 2024 May 09; Vol. 7, pp. 1359094. Date of Electronic Publication: 2024 May 09 (Print Publication: 2024).
DOI:	10.3389/frai.2024.1359094
Abstrakt:	Perceptual measures, such as intelligibility and speech disorder severity, are widely used in the clinical assessment of speech disorders in patients treated for oral or oropharyngeal cancer. Despite their widespread usage, these measures are known to be subjective and hard to reproduce. Therefore, an M-Health assessment based on an automatic prediction has been seen as a more robust and reliable alternative. Despite recent progress, these automatic approaches still remain somewhat theoretical, and a need to implement them in real clinical practice rises. Hence, in the present work we introduce SAMI, a clinical mobile application used to predict speech intelligibility and disorder severity as well as to monitor patient progress on these measures over time. The first part of this work illustrates the design and development of the systems supported by SAMI. Here, we show how deep neural speaker embeddings are used to automatically regress speech disorder measurements (intelligibility and severity), as well as the training and validation of the system on a French corpus of head and neck cancer. Furthermore, we also test our model on a secondary corpus recorded in real clinical conditions. The second part details the results obtained from the deployment of our system in a real clinical environment, over the course of several weeks. In this section, the results obtained with SAMI are compared to an a posteriori perceptual evaluation, conducted by a set of experts on the new recorded data. The comparison suggests a high correlation and a low error between the perceptual and automatic evaluations, validating the clinical usage of the proposed application. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2024 Quintas, Vaysse, Balaguer, Roger, Mauclair, Farinas, Woisard and Pinquier.)
Databáze:	MEDLINE
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