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

Artificial Intelligence (2129) New Methods (1134) Neurology and Neurosciences (286) Computer-Aided Diagnosis (251) Machine Learning (2076) Digital Health, Telehealth and e-Innovation in Clinical Settings (556) Development of Novel Medical Devices and Innovations for Existing Devices (102) Methodological Innovations (37)

Published on in Vol 23, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29554, first published .
A Markerless 2D Video, Facial Feature Recognition–Based, Artificial Intelligence Model to Assist With Screening for Parkinson Disease: Development and Usability Study

A Markerless 2D Video, Facial Feature Recognition–Based, Artificial Intelligence Model to Assist With Screening for Parkinson Disease: Development and Usability Study

A Markerless 2D Video, Facial Feature Recognition–Based, Artificial Intelligence Model to Assist With Screening for Parkinson Disease: Development and Usability Study

Authors of this article:

Xinyao Hou1 Author Orcid Image ;   Yu Zhang2 Author Orcid Image ;   Yanping Wang3 Author Orcid Image ;   Xinyi Wang4 Author Orcid Image ;   Jiahao Zhao2 Author Orcid Image ;   Xiaobo Zhu2 Author Orcid Image ;   Jianbo Su1 Author Orcid Image
Article Authors Cited by (20) Tweetations (6) Metrics

Journals

  1. Qiang J, Wu D, Du H, Zhu H, Chen S, Pan H. Review on Facial-Recognition-Based Applications in Disease Diagnosis. Bioengineering 2022;9(7):273 View
  2. Oliveira G, Ngo Q, Passos L, Papa J, Jodas D, Kumar D. Tabular data augmentation for video-based detection of hypomimia in Parkinson’s disease. Computer Methods and Programs in Biomedicine 2023;240:107713 View
  3. Phienphanich P, Lerthirunvibul N, Charnnarong E, Munthuli A, Tantibundhit C, Suwanwela N. Generalizing a Small Facial Image Dataset Using Facial Generative Adversarial Networks for Stroke’s Facial Weakness Screening. IEEE Access 2023;11:64886 View
  4. Bianchini E, Rinaldi D, Alborghetti M, Simonelli M, D’Audino F, Onelli C, Pegolo E, Pontieri F. The Story behind the Mask: A Narrative Review on Hypomimia in Parkinson’s Disease. Brain Sciences 2024;14(1):109 View
  5. ZHENG T, WANG X, PENG X, SU N, XU T, XIE X, HUANG J, XIE L, TIAN F. Survey of neurocognitive disorder detection methods based on speech, visual, and virtual reality technologies. Virtual Reality & Intelligent Hardware 2024;6(6):421 View
  6. Goudarzi N, Taheri Z, Nezhad Salari A, Kazemzadeh K, Tafakhori A. Recognition and classification of facial expression using artificial intelligence as a key of early detection in neurological disorders. Reviews in the Neurosciences 2025;36(5):479 View
  7. Markowska-Kaczmar U, Karolewski M, Wawrzyniak M, Szymończyk K, Szołomicka J, Pyka J, Kulawiec K, Demidaś A. Masticatory organ dysfunction recognition based on multimodal information. Engineering Applications of Artificial Intelligence 2025;144:109998 View
  8. Oliveira G, Ngo Q, Passos L, Jodas D, Papa J, Kumar D. Facial Expression Analysis in Parkinsons’s Disease Using Machine Learning: A Review. ACM Computing Surveys 2025;57(8):1 View
  9. Vitturi B, Nerdal P, Maetzler W. Collection of the digital data from the neurological examination. npj Digital Medicine 2025;8(1) View
  10. Song J, He M, Zheng X, Zhang Y, Bi C, Feng J, Du J, Li H, Shen B. Face-based machine learning diagnostics: applications, challenges and opportunities. Artificial Intelligence Review 2025;58(8) View
  11. Yang N, Liu J, Sun D, Ding J, Sun L, Qi X, Yan W. Motor symptoms of Parkinson’s disease: critical markers for early AI-assisted diagnosis. Frontiers in Aging Neuroscience 2025;17 View
  12. Yang N, Liu J, Wang L, Ding J, Sun L, Qi X, Lu Y, Yan W. Automated identification of early to mid-stage Parkinson’s disease using deep convolutional neural networks on static facial images. Frontiers in Medical Technology 2025;7 View
  13. Toptaş A, Bozkurt H, Ekinci E, Güzey Aras Y, Garip Z. Ensemble Deep Learning with Majority Voting for Parkinson’s Diagnosis via Facial Images. Journal of Intelligent Systems: Theory and Applications 2025;8(2):95 View
  14. Filali Razzouki A, Jeancolas L, Petrovska-Delacrétaz D, El-Yacoubi M. Facial digital markers For hypomimia detection in Parkinson’s disease: A systematic review. Pattern Recognition 2026;172:112573 View
  15. Huang X, Li H, Ma J, Bi X, Meng F, Jiang W, Ma X. Facial expression-based hypomimia detection for Parkinson’s disease diagnosis: A static-dynamic mixed feature approach. Biomedical Signal Processing and Control 2026;112:108762 View

Books/Policy Documents

  1. Bordallo López M, Álvarez Casado C, Susarla P, Lage Cañellas M, Nguyen L. Handbook of Digital Technologies in Movement Disorders. View
  2. Muñoz J, Villaroel B, Taramasco C, Gatica G. Advanced Research in Technologies, Information, Innovation and Sustainability. View

Conference Proceedings

  1. Bushey J. 2023 IEEE International Conference on Big Data (BigData). AI-Generated Images as an Emergent Record Format View
  2. Pereira C, Barros P, Rodrigues J, Araújo P, Borges R, Almeida K, Veras R. Anais da XII Escola Regional de Computação do Ceará, Maranhão e Piauí (ERCEMAPI 2024). Identificação de Parkinson em Imagens Faciais Usando Modelos de Deep Learning Pré-Treinados View
  3. Duan S, Dadashzadeh A, Whone A, Mirmehdi M. 2025 IEEE 19th International Conference on Automatic Face and Gesture Recognition (FG). Trajectory-guided Motion Perception for Facial Expression Quality Assessment in Neurological Disorders View