Parkinson’s disease: application of modern digital systems and approaches for the assessment of neurological dysfunction in patients (a literature review)

Background. A comprehensive analysis of modern digital technologies, including wearable sensors, computer vision, and artificial intelligence algorithms, was conducted.

Objective. To evaluate the use of these technologies for the diagnosis, monitoring, and rehabilitation of patients with Parkinson’s disease, focusing on their potential to objectively assess motor symptoms, identify key advantages, and highlight systemic limitations that hinder their widespread clinical integration.

Materials and methods. A systematic review of the scientific literature from 2020 to 2025 was conducted using the PubMed, Scopus, Web of Science, and eLibrary.ru databases. The search employed key terms such as digital biomarkers, computer vision, machine learning, Parkinson’s disease, and telemedicine platforms. The methodology included critical analysis and systematization of data, emphasizing architectural solutions, algorithmic approaches, and results of clinical testing of digital systems.

Results. Digital technologies, particularly multi-level platforms such as the Parkinson Expert System, demonstrate high efficiency in forming objective digital biomarkers for assessing tremor, bradykinesia, and gait disturbances, showing strong correlation with traditional clinical scales. The key limitation lies in the lack of standardized protocols, validation procedures, and unified methodological approaches, which complicates the comparability of results and their translation into routine clinical practice.

Conclusion. Digital technologies possess significant transformative potential for the personalization of Parkinson’s disease diagnosis and monitoring by enabling continuous and objective data collection. However, successful clinical integration requires the development of unified standards, large-scale multicenter studies, and solutions addressing algorithm validation, data protection, and system interoperability. Further development of this field will improve the accuracy and efficiency of medical care for patients with neurodegenerative diseases.

1. Stephen C. D., Parisi F., Mancini M., Artusi C. A. Digital biomarkers in movement disorders. Front. Neurol. 2025; 16: 1600018.

2. Harris C., Tang Y., Birnbaum E., Cherian C., Mendhe D., Chen M. H. Digital neuropsychology beyond computerized cognitive assessment: applications of novel digital technologies. Arch. Clin. Neuropsychol. 2024; 39 (3): 290-304.

3. Salchow-Hömmen C., Skrobot M., Jochner M. C. E., Schauer T., Kühn A. A., Wenger N. Emerging portable technologies for gait analysis in neurological disorders. Front. Hum. Neurosci. 2022; 16: 768575.

4. Caro C., Malpica N. Video and optoelectronics in movement disorders. Int. Rev. Mov. Disord. 2023; 5: 227-244.

5. Horak F. B., Shah V. V., Mancini M. Digital gait and balance measures. Int. Rev. Mov. Disord. 2023; 5: 115-151.

6. Deb R., An S., Bhat G., Shill H., Ogras U. Y. A systematic survey of research trends in technology usage for Parkinson’s disease. Sensors. 2022; 22 (15): 5491.

7. Park K. W., Mirian M. S., McKeown M. J. Artificial intelligence-based video monitoring of movement disorders in the elderly: a review on current and future landscapes. Singap. Med. J. 2024; 65 (3): 141-149.

8. Rábano-Suárez P., Del Campo N., Benatru I., Moreau C., Desjardins C., Sánchez-Ferro Á., Fabbri M. Digital outcomes as biomarkers of disease progression in early Parkinson’s disease: a systematic review. Mov. Disord. 2025; 40 (2): 184-203.

9. Sun Y. M., Wang Z. Y., Liang Y. Y., Hao C. W., Shi C. H. Digital biomarkers for precision diagnosis and monitoring in Parkinson’s disease. NPJ Digit. Med. 2024; 7 (1): 218.

10. Thankathuraipandian S., Greenleaf W., Kyani A., Tomlinson T., Balasingh B., Ross E., Pathak Y. Development of a remote therapeutic monitoring platform: applications for movement disorders. Sci. Rep. 2024; 14 (1): 29837.

11. Bernad A. E., Woelfle T., Granziera C., Kappos L., Lorscheider J., Barragan A., Pupo Ó. R. A novel methodology for developing smartphoneinstrumented tests for assessing movement, dexterity, and balance in neurological patients. Neurology. 2024; 102 (7 Suppl 1): 5218.

12. Beswick E., Fawcett T., Hassan Z., Forbes D., Dakin R., Newton J., et al. A systematic review of digital technology to evaluate motor function and disease progression in motor neuron disease. J. Neurol. 2022; 269 (12): 6254-6268.

13. Adams J. L., Kangarloo T., Gong Y., Khachadourian V., Tracey B., Volfson D., et al. Using a smartwatch and smartphone to assess early Parkinson’s disease in the WATCH-PD study over 12 months. NPJ Parkinson’s Dis. 2024; 10 (1): 112.

14. Güney G., Jansen T. S., Dill S., Schulz J. B., Dafotakis M., Hoog Antink C., Braczynski A. K. Video-based hand movement analysis of Parkinson patients before and after medication using high-frame-rate videos and MediaPipe. Sensors. 2022; 22 (20): 7992.

15. De Queiroz R. S., Alves J. H., Sasaki J. E. Digital biomarkers in the assessment of mobility in individuals with multiple sclerosis. Sclerosis. 2023; 1 (3): 134-150.

16. Mahboobeh D. J., Dias S. B., Khandoker A. H., Hadjileontiadis L. J. Machine learning-based analysis of digital movement assessment and ExerGame scores for Parkinson’s disease severity estimation. Front. Psychol. 2022; 13: 857249.

17. Franco A., Russo M., Amboni M., Ponsiglione A. M., Di Filippo F., Romano M., et al. The role of deep learning and gait analysis in Parkinson’s disease: a systematic review. Sensors. 2024; 24 (18): 5957.

18. Mancini M., McKay J. L., Cockx H., D’Cruz N., Esper C. D., Filtjens B., et al. Technology for measuring freezing of gait: current state of the art and recommendations. J. Parkinson’s Dis. 2025; 15 (1): 19-40.

19. Garbey M., Lesport Q., Girma H., Öztosun G., Abu-Rub M., Guidon A. C., et al. Application of digital tools and artificial intelligence in the Myasthenia Gravis Core Examination. Front. Neurol. 2024; 15: 1474884.

20. Adams J. L., Kangarloo T., Tracey B., O’Donnell P., Volfson D., Latzman R. D., et al. Using a smartwatch and smartphone to assess early Parkinson’s disease in the WATCH-PD study. NPJ Parkinson’s Dis. 2023; 9 (1): 64.

21. Morinan G., Dushin Y., Sarapata G., Rupprechter S., Peng Y., Girges C., et al. Computer vision quantification of whole-body Parkinsonian bradykinesia using a large multi-site population. NPJ Parkinson’s Dis. 2023; 9 (1): 10.

22. Kim K., Lyu S., Mantri S., Dunn T. W. TULIP: multi-camera 3D precision assessment of Parkinson's disease. Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. 2024; 22551–22562.

23. Zhang Y., Zeng Z., Mirian M. S., Yen K., Park K. W., Doo M., et al. Investigating the efficacy and importance of mobile-based assessments for Parkinson's disease: uncovering the potential of novel digital tests. Sci. Rep. 2024; 14 (1): 5307.

24. Panero E., D’Alessandro R., Cavallina I., Davico C., Mongini T., Gastaldi L., Ricci F. Wearable inertial devices in Duchenne muscular dystrophy: a scoping review. Appl. Sci. 2023; 13 (3): 1268.

25. Yu T., Park K. W., McKeown M. J., Wang Z. J. Clinically informed automated assessment of finger tapping videos in Parkinson’s disease. Sensors. 2023; 23 (22): 9149.

26. Xiang X., Zhang Z., Ma J., Deng Y. AI WALKUP: a computer-vision approach to quantifying MDS-UPDRS in Parkinson's disease. arXiv preprint arXiv:2404.01654. 2024.

27. Lipsmeier F., Taylor K. I., Postuma R. B., Volkova-Volkmar E., Kilchenmann T., Mollenhauer B., et al. Reliability and validity of the Roche PD mobile application for remote monitoring of early Parkinson’s disease. Sci. Rep. 2022; 12 (1): 12081.

28. Seo N. Y., Jeong E. W., Lee J. H., Cho S. Y., Jung Y. J. Objective assessment of motor ataxia via quantitative analysis of Romberg's test utilizing webcam-based motion capture with AI. J. Magn. 2023; 28 (4): 470-476.

29. Acien A., Calcagno N., Burke K. M., Mondesire-Crump I., Holmes A. A., Mruthik S., et al. A novel digital tool for detection and monitoring of amyotrophic lateral sclerosis motor impairment via keystroke dynamics. Sci. Rep. 2024; 14 (1): 16851.

30. Graham L., Vitorio R., Walker R., Barry G., Godfrey A., Morris R., Stuart S. Digital eye-movement outcomes (DEMOs) as biomarkers for neurological conditions: a narrative review. Big Data Cogn. Comput. 2024; 8 (12): 198