ArticleThe Journal of the Acoustical Society of America2019
Differentiating post-cancer from healthy tongue muscle coordination patterns during speech using deep learning.
Article in The Journal of the Acoustical Society of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 7 citations in OpenAlex.
- Analysis of Tongue Muscle Strain During Speech From Multimodal Magnetic Resonance Imaging.Journal of speech, language, and hearing research : JSLHR · 2023Article
- Machine Learning in Dentistry: A Scoping Review.Journal of clinical medicine · 2023Article
- VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI.IEEE journal of biomedical and health informatics · 2022Article
- Oral-Gut Microbiome Analysis in Patients With Metabolic-Associated Fatty Liver Disease Having Different Tongue Image Feature.Frontiers in cellular and infection microbiology · 2022Article
- A deep joint sparse non-negative matrix factorization framework for identifying the common and subject-specific functional units of tongue motion during speech.Medical image analysis · 2021Article
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Corrections and comments
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Authors and funding
9 authors at 7 institutions in 1 country.
Funding
Abstract
The ability to differentiate post-cancer from healthy tongue muscle coordination patterns is necessary for the advancement of speech motor control theories and for the development of therapeutic and rehabilitative strategies. A deep learning approach is presented to classify two groups using muscle coordination patterns from magnetic resonance imaging (MRI). The proposed method uses tagged-MRI to track the tongue's internal tissue points and atlas-driven non-negative matrix factorization to reduce the dimensionality of the deformation fields. A convolutional neural network is applied to the classification task yielding an accuracy of 96.90%, offering the potential to the development of therapeutic or rehabilitative strategies in speech-related disorders.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.