This paper is in the following e-collection/theme issue:

Decision Support for Health Professionals (1211) Machine Learning (1505) Artificial Intelligence (1430) New Methods (964)

Published on in Vol 9, No 3 (2021): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21331, first published .
Detection of Bulbar Involvement in Patients With Amyotrophic Lateral Sclerosis by Machine Learning Voice Analysis: Diagnostic Decision Support Development Study

Detection of Bulbar Involvement in Patients With Amyotrophic Lateral Sclerosis by Machine Learning Voice Analysis: Diagnostic Decision Support Development Study

Detection of Bulbar Involvement in Patients With Amyotrophic Lateral Sclerosis by Machine Learning Voice Analysis: Diagnostic Decision Support Development Study

Authors of this article:

Alberto Tena1 Author Orcid Image ;   Francec Claria2 Author Orcid Image ;   Francesc Solsona2 Author Orcid Image ;   Einar Meister3 Author Orcid Image ;   Monica Povedano4 Author Orcid Image
Article Authors Cited by (12) Tweetations (5) Metrics

Journals

  1. Ball L, Geske J, Burton E, Pattee G. A clinical bulbar assessment scale (CBAS) for amyotrophic lateral sclerosis. Muscle & Nerve 2022;66(6):694 View
  2. Tena A, Clarià F, Solsona F, Povedano M. Voiceprint and machine learning models for early detection of bulbar dysfunction in ALS. Computer Methods and Programs in Biomedicine 2023;229:107309 View
  3. Ahangaran M, Peimankar A, Wiil U, Chio A. Impact of laboratory biomarkers on ALS disease progression: a probabilistic causation approach. Network Modeling Analysis in Health Informatics and Bioinformatics 2022;11(1) View
  4. Bede P, Murad A, Lope J, Li Hi Shing S, Finegan E, Chipika R, Hardiman O, Chang K. Phenotypic categorisation of individual subjects with motor neuron disease based on radiological disease burden patterns: A machine-learning approach. Journal of the Neurological Sciences 2022;432:120079 View
  5. Tena A, Clarià F, Solsona F, Povedano M. Detecting Bulbar Involvement in Patients with Amyotrophic Lateral Sclerosis Based on Phonatory and Time-Frequency Features. Sensors 2022;22(3):1137 View
  6. Idrisoglu A, Dallora A, Anderberg P, Berglund J. Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review. Journal of Medical Internet Research 2023;25:e46105 View
  7. Bouvier L, McKinlay S, Truong J, Genge A, Dupré N, Dionne A, Kalra S, Yunusova Y. Speech timing and monosyllabic diadochokinesis measures in the assessment of amyotrophic lateral sclerosis in Canadian French. International Journal of Speech-Language Pathology 2024;26(2):267 View
  8. Milella G, Sciancalepore D, Cavallaro G, Piccirilli G, Nanni A, Fraddosio A, D’Errico E, Paolicelli D, Fiorella M, Simone I. Acoustic Voice Analysis as a Useful Tool to Discriminate Different ALS Phenotypes. Biomedicines 2023;11(9):2439 View
  9. Dubbioso R, Spisto M, Verde L, Iuzzolino V, Senerchia G, Salvatore E, De Pietro G, De Falco I, Sannino G. Voice signals database of ALS patients with different dysarthria severity and healthy controls. Scientific Data 2024;11(1) View
  10. Rocha P, Bento N, Svärd H, Lopes D, Hespanhol S, Folgado D, Carreiro A, de Carvalho M, Miranda B. Voice Assessment in Patients with Amyotrophic Lateral Sclerosis: An Exploratory Study on Associations with Bulbar and Respiratory Function. Brain Sciences 2024;14(11):1082 View
  11. Gupta R, Bhandari M, Grover A, Al-shehari T, Kadrie M, Alfakih T, Alsalman H. Predictive modeling of ALS progression: an XGBoost approach using clinical features. BioData Mining 2024;17(1) View