Published on in Vol 8, No 9 (2020): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18689, first published .
An Intelligent Mobile-Enabled System for Diagnosing Parkinson Disease: Development and Validation of a Speech Impairment Detection System

An Intelligent Mobile-Enabled System for Diagnosing Parkinson Disease: Development and Validation of a Speech Impairment Detection System

An Intelligent Mobile-Enabled System for Diagnosing Parkinson Disease: Development and Validation of a Speech Impairment Detection System

Authors of this article:

Liang Zhang1 Author Orcid Image ;   Yue Qu2 Author Orcid Image ;   Bo Jin2 Author Orcid Image ;   Lu Jing3 Author Orcid Image ;   Zhan Gao4 Author Orcid Image ;   Zhanhua Liang3 Author Orcid Image

Journals

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  9. Maremmani C, Rovini E, Salvadori S, Pecori A, Pasquini J, Ciammola A, Rossi S, Berchina G, Monastero R, Cavallo F. Hands–feet wireless devices: Test–retest reliability and discriminant validity of motor measures in Parkinson's disease telemonitoring. Acta Neurologica Scandinavica 2022;146(3):304 View
  10. Sarankumar R, Vinod D, Anitha K, Manohar G, Vijayanand K, Pant B, Sundramurthy V, Uddin Z. Severity Prediction over Parkinson’s Disease Prediction by Using the Deep Brooke Inception Net Classifier. Computational Intelligence and Neuroscience 2022;2022:1 View
  11. Pramanik M, Pradhan R, Nandy P, Qaisar S, Bhoi A, Ahmad S. Assessment of Acoustic Features and Machine Learning for Parkinson’s Detection. Journal of Healthcare Engineering 2021;2021:1 View
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  13. Adams J, Kangarloo T, Tracey B, O’Donnell P, Volfson D, Latzman R, Zach N, Alexander R, Bergethon P, Cosman J, Anderson D, Best A, Severson J, Kostrzebski M, Auinger P, Wilmot P, Pohlson Y, Waddell E, Jensen-Roberts S, Gong Y, Kilambi K, Herrero T, Ray Dorsey E, Adams J, Tarolli C, Soto J, Hogarth P, Wahedi M, Wakeman K, Espay A, Brown J, Wurzelbacher C, Gunzler S, Khawam E, Kilbane C, Spindler M, Engeland M, Tarakad A, Barrett M, Cloud L, Norris V, Mari Z, Wyant K, Chou K, Stovall A, Poon C, Simuni T, Tingling K, Luthra N, Tanner C, Yilmaz E, Romero D, Thomas K, Matson L, Richardson L, Fullard M, Feuerstein J, Shelton E, Shprecher D, Callan M, Feigin A, Romano C, Romain M, Shum M, Botting E, Harrell L, Rocha C, Ramdhani R, Gardner J, Parker G, Ross V, Stephen S, Fisher K, Edgerton J, Cedarbaum J, Rubens R, Padmanabhan J, Stephenson D, Severson B, Merickel M, Amato D, Carroll T. Using a smartwatch and smartphone to assess early Parkinson’s disease in the WATCH-PD study. npj Parkinson's Disease 2023;9(1) View
  14. Mondol S, Kim R, Lee S. Hybrid Machine Learning Framework for Multistage Parkinson’s Disease Classification Using Acoustic Features of Sustained Korean Vowels. Bioengineering 2023;10(8):984 View
  15. 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
  16. Hireš M, Drotár P, Pah N, Ngo Q, Kumar D. On the inter-dataset generalization of machine learning approaches to Parkinson's disease detection from voice. International Journal of Medical Informatics 2023;179:105237 View