Nicole Y.K. Li-Jessen
Membre régulier
McGill University, The Research Institute of the McGill University Health Centre
School of Communication Sciences and Disorders
2001 McGill College, Suite 800
, Montreal
(QC)
H3A1G1
Partager sur
Domaine·s de recherche
- Environmental Pollutants
- Upper Airway Health
- Tissue Inflammation
- Laryngology
- Digital Health
Université
McGill University
Axe primaire du Réseau AIRS
Déterminants omiques et biologiques de la santé
Axe(s) secondaire(s)
axe3
Secteur·s de recherche
- Santé
- Nature et technologie
Type·s de recherche
- Fondamentale
- Clinique
- Translationnelle
Diplôme·s
- Ph.D
Travaux de recherche
I hold a Canada Research Chair (T2) in Personalized Medicine of Upper Airway Health and Diseases. My research program addresses the pressing public health issue of voice and upper airway (VUA) conditions by developing individualized healthcare solutions through advanced computational and engineering technologies.
Vocal folds in the larynx ...
NUMÉRO ORCID :
https://orcid.org/0000-0003-2963-4763
Références bibliographiques et DOI
Chao, A., Martignetti, L., Groh, R., Kist, A., & Li‐Jessen, N. Y. K. (2025). A mobile health application and system architecture for respiratory disease monitoring: design principles, tool development and pilot usability test. JMIR Formative Research. 9: e7384.
Landry, V., Matschek, J., Pang, R., Munipalle, M., Tan, K., Boruff, J. & Li‐Jessen, N. Y. K. (2025). Audio-based digital biomarkers in diagnosing and managing respiratory diseases: a systematic review and bibliometric analysis. European Respiratory Review. 34 (176): 240246.
Brown, M., Okuyama H., Li, Ling, Yang, Z., Li, J. Y., Tabrizian, M.† & Li‐Jessen, N. Y. K.† (2025). Click-tetrazine dECM–alginate hydrogels for injectable, mechanically mimetic, and biologically active vocal fold biomaterials. Biomaterials. 325, 123590.
Saint-Jules, W., Massé-Alarie, H., Li‐Jessen, N. Y. K. & Desjardins, M.. Laryngeal hypersensitivity from the perspective of pain science: an integrative review of empirical studies on associated factors and processes. Journal of Voice. S0892-1997, (25), 00126-2.
Groh, R., Lei, Z. D., Martignetti, L., Li‐Jessen, N. Y. K.† & Kist, A. M.† (2022). Efficient and explainable deep neural networks for airway symptom detection in support of wearable health technology. Advanced Intelligent Systems. 4 (7), 2100284.