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

Web-based and Mobile Health Interventions (4018) Machine Learning (2076) Artificial Intelligence (2129) New Methods (1134) Big Data Analytics for Elder Care (37) Mobile Devices and Apps for Seniors and Healthy Aging (265) Speech-Language Therapy (31) Speech and Phonetics in Verbal Protocols (Think Aloud, Recall, and Explanation Tasks) (95)

Published on in Vol 23, No 10 (2021): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26305, first published .
Detecting Parkinson Disease Using a Web-Based Speech Task: Observational Study

Detecting Parkinson Disease Using a Web-Based Speech Task: Observational Study

Detecting Parkinson Disease Using a Web-Based Speech Task: Observational Study

Authors of this article:

Wasifur Rahman1 Author Orcid Image ;   Sangwu Lee1 Author Orcid Image ;   Md Saiful Islam1 Author Orcid Image ;   Victor Nikhil Antony1 Author Orcid Image ;   Harshil Ratnu1 Author Orcid Image ;   Mohammad Rafayet Ali1 Author Orcid Image ;   Abdullah Al Mamun1 Author Orcid Image ;   Ellen Wagner2 Author Orcid Image ;   Stella Jensen-Roberts2 Author Orcid Image ;   Emma Waddell2 Author Orcid Image ;   Taylor Myers2 Author Orcid Image ;   Meghan Pawlik2 Author Orcid Image ;   Julia Soto2 Author Orcid Image ;   Madeleine Coffey2 Author Orcid Image ;   Aayush Sarkar2 Author Orcid Image ;   Ruth Schneider2, 3 Author Orcid Image ;   Christopher Tarolli2, 3 Author Orcid Image ;   Karlo Lizarraga2, 3 Author Orcid Image ;   Jamie Adams2, 3 Author Orcid Image ;   Max A Little4, 5 Author Orcid Image ;   E Ray Dorsey2 Author Orcid Image ;   Ehsan Hoque1 Author Orcid Image
Article Authors Cited by (41) Tweetations (17) Metrics

Journals

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  10. Rahman W, Abdelkader A, Lee S, Yang P, Islam M, Adnan T, Hasan M, Wagner E, Park S, Dorsey E, Schwartz C, Jaffe K, Hoque E. A User-Centered Framework to Empower People with Parkinson's Disease. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2023;7(4):1 View
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  18. Calà F, Frassineti L, Cantarella G, Buccichini G, Battilocchi L, Manfredi C, Lanatà A. Towards an explainable Artificial intelligence system for voice pathology identification and post-treatment characterisation. Biomedical Signal Processing and Control 2025;104:107530 View
  19. Templeton J, Poellabauer C, Schneider S, Rahimi M, Braimoh T, Tadamarry F, Margolesky J, Burke S, Al Masry Z. Modernizing the Staging of Parkinson Disease Using Digital Health Technology. Journal of Medical Internet Research 2025;27:e63105 View
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  21. Silva D, Ribeiro C, Souza L, Pereira A. Application of Open-Source, Low-Code Machine-Learning Library in Python to Diagnose Parkinson's Disease Using Voice Signal Features. Brazilian Archives of Biology and Technology 2025;68 View
  22. Adnan T, Abdelkader A, Liu Z, Hossain E, Park S, Islam M, Hoque E. A novel fusion architecture for detecting Parkinson’s Disease using semi-supervised speech embeddings. npj Parkinson's Disease 2025;11(1) View
  23. Lizárraga K, Camargo Salazar I, Zizzi C, Romero S, Centeno Arispe J, Vela Quico A, Dye T, Dorsey E, Castaneda B. Locally adapting digital health technologies to address the global challenge of Parkinson’s disease. Equity Neuroscience 2025;1(2):100010 View
  24. Gundler C, Wiederhold A, Pötter-Nerger M. Assessing the Generalizability of Foundation Models for the Recognition of Motor Examinations in Parkinson’s Disease. Sensors 2025;25(17):5523 View
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Books/Policy Documents

  1. . Digital Technologies in Movement Disorders. View
  2. Adams J, Waddell E, Chunga N, Quinn L. Biomarkers for Huntington's Disease. View
  3. Jansi K, Vidhya S, Sandhia G. Intelligent Solutions for Cognitive Disorders. View
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Books/Policy Documents

  1. Saiz Manzanares M, Martínez Martín M, Escolar Llamazares M, Ortiz Huerta J, Santamaría Vázquez M, Mercado Val E, Marticorena Sánchez R, Arnáiz González Á, Díez Pastor J, Rodríguez Arribas S. Training and specialisation in early intervention: use of technological resources and artificial intelligence. View
  2. Saiz Manzanares M, Martínez Martín M, Escolar Llamazares M, Ortiz Huerta J, Santamaría Vázquez M, Mercado Val E, Marticorena Sánchez R, Arnáiz González Á, Díez Pastor J, Rodríguez Arribas S. Formación y especialización en atención temprana: uso de recursos tecnológicos y de inteligencia artificial. View

Conference Proceedings

  1. Nalini M, Gayathiri R, Srimathi R, Vidyathmikaa R, Jenifer S. 2022 International Conference on Communication, Computing and Internet of Things (IC3IoT). Detection of Parkinson's Disease Using Voice Changes and Hand-tremor View
  2. Islam M, Lee S, Abdelkader A, Park S, Hoque E. 2023 11th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW). PARK: Parkinson’s Analysis with Remote Kinetic-tasks View
  3. Spann J, Chen S, Ashizawa T, Hoque E. Proceedings of the 29th International Conference on Intelligent User Interfaces. Getting on the Right Foot: Using Observational and Quantitative Methods to Evaluate Movement Disorders View
  4. Om Prakash P, Reddy B, Lohith S. 2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). Machine Learning-based Prediction of Parkinson’s Disease: A Comparative Analysis of Algorithms View
  5. Jasim H, Alshakarchy N. 2024 4th International Conference of Science and Information Technology in Smart Administration (ICSINTESA). Predicting the Severity of Parkinson’s Disease Based on Voice Analysis Using Deep Learning View
  6. Sandhya C, Hemalatha S. 3RD PROCESS SYSTEMS ENGINEERING & SAFETY (PROSES) SYMPOSIUM 2023. Investigations to identify Parkinson’s disease using machine learning algorithms View
  7. Satyanarayana T, Bhargavi M, Kavarakuntla V, Gayaz S, Reddy S. 2025 International Conference on Information, Implementation, and Innovation in Technology (I2ITCON). Exploring the Role of Vocal and Temporal Features in Parkinson's Disease Detection with Machine Learning View