Identification of osteoarthritis kinematic phenotypes using cluster analysis on knee kinesiography data [r-libre/3615]

Mezghani, Neila; Loulou, Karim; Ouakrim, Youssef; Ayena, Johannes C; Cagnin, Alix; Choinière, Manon; Bureau, Nathalie J et Hagemeister, Nicola (2024). Identification of osteoarthritis kinematic phenotypes using cluster analysis on knee kinesiography data. Dans 2024 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET) (p. 1-5). https://doi.org/10.1109/IC_ASET61847.2024.10596254

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Catégorie de document :	Communications dans des actes de congrès/colloques
Évaluation par un comité de lecture :	Oui
Étape de publication :	Publié
Résumé :	Previous studies highlight that identifying phenotypes is crucial for developing effective treatments for knee osteoarthritis (OA). This study aims to identify kinematic phenotypes in a knee OA population and characterize them by patient biomechanical markers which are functional parameters used in clinical settings. Knee kinematics are measured using the KneeKG (Knee Kinesiography) system, a technology that objectively assesses 3D knee kinematics. Kinematic data were categorized into homogeneous groups using a clustering process with a discriminant model called Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH). We identified five distinct phenotypes, exhibiting significant statistical differences (p < 0.05) in 3D kinematics, and linked these phenotypes to biomechanical markers measurable in clinical settings.
Adresse de la version officielle :	https://ieeexplore.ieee.org/abstract/document/1059...
Déposant:	Ayena, Johannes
Responsable :	Neila Mezghani
Dépôt :	28 févr. 2025 19:33
Dernière modification :	28 févr. 2025 19:33

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