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Artificial Intelligence (2129) New Methods (1134) Imaging Informatics (302) Telehealth and Telemonitoring (2088)

Published on in Vol 23, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21037, first published .
Automated Computer Vision Assessment of Hypomimia in Parkinson Disease: Proof-of-Principle Pilot Study

Automated Computer Vision Assessment of Hypomimia in Parkinson Disease: Proof-of-Principle Pilot Study

Automated Computer Vision Assessment of Hypomimia in Parkinson Disease: Proof-of-Principle Pilot Study

Authors of this article:

Avner Abrami1 Author Orcid Image ;   Steven Gunzler2 Author Orcid Image ;   Camilla Kilbane2 Author Orcid Image ;   Rachel Ostrand1 Author Orcid Image ;   Bryan Ho3 Author Orcid Image ;   Guillermo Cecchi1 Author Orcid Image
Article Authors Cited by (32) Tweetations (4) Metrics

Journals

  1. Novotny M, Tykalova T, Ruzickova H, Ruzicka E, Dusek P, Rusz J. Automated video-based assessment of facial bradykinesia in de-novo Parkinson’s disease. npj Digital Medicine 2022;5(1) View
  2. Xu Z, Shen B, Tang Y, Wu J, Wang J. Deep Clinical Phenotyping of Parkinson’s Disease: Towards a New Era of Research and Clinical Care. Phenomics 2022;2(5):349 View
  3. Birnbaum M, Abrami A, Heisig S, Ali A, Arenare E, Agurto C, Lu N, Kane J, Cecchi G. Acoustic and Facial Features From Clinical Interviews for Machine Learning–Based Psychiatric Diagnosis: Algorithm Development. JMIR Mental Health 2022;9(1):e24699 View
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  6. Sarang S, Sonawane B, Sharma P, Yeradkar R. To study the effect of a newly developed emotion detection and grading system software for identifying and grading expressions of patients with Parkinson’s disease. Multimedia Tools and Applications 2023;83(8):22855 View
  7. Conelea C, Liang H, DuBois M, Raab B, Kellman M, Wellen B, Jacob S, Wang S, Sun J, Lim K. Automated Quantification of Eye Tics Using Computer Vision and Deep Learning Techniques. Movement Disorders 2024;39(1):183 View
  8. 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
  9. Huang W, Xu W, Wan R, Zhang P, Zha Y, Pang M. Auto Diagnosis of Parkinson's Disease Via a Deep Learning Model Based on Mixed Emotional Facial Expressions. IEEE Journal of Biomedical and Health Informatics 2024;28(5):2547 View
  10. Jiang D, Yan L, Mayrand F. Emotion expressions and cognitive impairments in the elderly: review of the contactless detection approach. Frontiers in Digital Health 2024;6 View
  11. 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
  12. Loewenstern Y, Benaroya-Milshtein N, Belelovsky K, Bar-Gad I. Automatic Identification of Facial Tics Using Selfie-Video. IEEE Journal of Biomedical and Health Informatics 2025;29(1):409 View
  13. 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
  14. 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
  15. Friedrich M, Relton S, Wong D, Alty J. Computer Vision in Clinical Neurology. JAMA Neurology 2025;82(4):407 View
  16. Rodrigues J, De Melo Souza Veras R, De Sousa Britto Neto L, Henrique Ximenes Ramalho Barros P, Da Silva Moura W, James Silva De Almeida K, Romulo Teixeira Aires K. Identification of Parkinson’s Disease Through Facial Image Classification: A Systematic Review. IEEE Access 2025;13:46720 View
  17. Filali Razzouki A, Jeancolas L, Sambin S, Mangone G, Chalançon A, Gomes M, Lehéricy S, Vidailhet M, Arnulf I, Corvol J, Petrovska-Delacrétaz D, El-Yacoubi M. Explaining facial action units' correlation with hypomimia and clinical scores in Parkinson’s disease. npj Parkinson's Disease 2025;11(1) View
  18. Ryu H, Kang J, Kwon D. Structured speech tasks reveal temporal facial variability as an objective measure in the de-novo Parkinson's disease patients. Parkinsonism & Related Disorders 2025;135:107840 View
  19. Candea T, Franklin R. Masking leadership: the impact of the SPEAK OUT! ® Therapy Program on the perceived leadership qualities of individuals with Parkinson’s disease. Journal of Clinical Practice in Speech-Language Pathology 2025;27(1):38 View
  20. Skaramagkas V, Boura I, Karamanis G, Kyprakis I, Fotiadis D, Kefalopoulou Z, Spanaki C, Tsiknakis M. Dual stream transformer for medication state classification in Parkinson’s disease patients using facial videos. npj Digital Medicine 2025;8(1) View
  21. Pecoraro P, Marsili L, Espay A, Bologna M, di Biase L. Computer Vision Technologies in Movement Disorders: A Systematic Review. Movement Disorders Clinical Practice 2025;12(9):1229 View
  22. Kälble L, Tykalova T, Zogala D, Dusek P, Rusz J, Novotny M. Automatic analysis of eyelid movement in de-novo Parkinson’s disease. npj Parkinson's Disease 2025;11(1) View
  23. Adnan T, Islam M, Lee S, Wasifur Rahman Chowdhury E, Tithi S, Noshin K, Islam M, Sarker I, Rahman M, Schneider R, Adams J, Dorsey E, Hoque E. AI-Enabled Parkinson’s Disease Screening Using Smile Videos. NEJM AI 2025;2(7) View
  24. Rom H, Peleg O, Rom Y, Mirelman A, Blumrosen G, Maidan I. Remote clinical decision support tool for Parkinson’s disease assessment using a novel approach that combines AI and clinical knowledge. BMC Medical Informatics and Decision Making 2025;25(1) View
  25. 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
  26. 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
  27. di Biase L, Pecoraro P, Bugamelli F. AI Video Analysis in Parkinson’s Disease: A Systematic Review of the Most Accurate Computer Vision Tools for Diagnosis, Symptom Monitoring, and Therapy Management. Sensors 2025;25(20):6373 View
  28. Contreras López W, Valenzuela B, Arevalo J, Martínez F. Spatio-Temporal Hypomimic Deep Descriptor to Discriminate Parkinsonian Patients. NeuroTarget 2025;19(2):144 View

Conference Proceedings

  1. Valenzuela B, Arevalo J, Contreras W, Martinez F. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). A Spatio-Temporal Hypomimic Deep Descriptor to Discriminate Parkinsonian Patients View
  2. Xu Z, Lv D, Li H, Li H, Gao H. 2023 International Conference on New Trends in Computational Intelligence (NTCI). Application of ResLSTM in Hypomimia Video Detection for Parkinson's Disease View
  3. Cai X, Xu Y, Zhou Z, Xue M, Li Z, Weng C, Luo W, Yao C, Lin B, Yin J. Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. "Break the Mask Barrier": An AU-based Rehabilitation Training System for Parkinson's Hypomimia View
  4. Huang X, Bi X, Lv C, Wang X, Zhang H, Jiang W, Ma X, Li Y. 2025 44th Chinese Control Conference (CCC). Dynamic Facial Expressions Analysis Based Parkinson's Disease Auxiliary Diagnosis View