20.500.12556/RUP-22781 Machine learning identifies distinct movement control impairment clusters in patients with chronic neck pain atients with chronic neck pain experience various impairments, with reduced movement control suggested as a significant contributing factor. The heterogeneity of this patient population and suboptimal rehabilitation outcomes suggests the existence of latent subgroup characteristics. The aim of this study was to identify distinct groups among patients with neck pain based on the movement control test and pain intensity and to provide information on cluster-specific impairments. 135 patients with idiopathic neck pain performed a movement control test (the Butterfly test) at three difficulty levels and were assessed for pain intensity, providing 13 different parameters (classifiers). Louvain, hierarchical and k-means clustering methods were applied and the number of clusters determined by observing the symmetry and size of silhouette scores. Further, different machine learning algorithms were applied to develop and evaluate a classification framework (based on AUC, classification accuracy, sensitivity, and specificity) and to provide information on individual cluster characteristics using the Shapley Additive Explanations. The k-means and deep learning neural network methods provided the most efficient clustering and classification performance extracting 4 meaningful clusters. Patients between groups differed in the amount of impairment, with cluster 2 and 1 representing the most severe impairments and with clusters 3 and 4 the least severe impairments. Additionally, specific motor control impairments were observed in individual clusters suggesting distinct neck movement control adaptations. Identifying subgroups of patients with neck pain and their specific characteristics based on the results of the Butterfly test may inform future development of targeted rehabilitation strategies. neck pain kinesthesia proprioception clustering machine learning bolečina v vratu kinestezija propriocepcija grozdenje strojno učenje true false true Angleški jezik Slovenski jezik Članek v reviji 2026-03-16 02:06:01 2026-03-16 02:06:05 2026-03-16 03:04:24 0000-00-00 00:00:00 2026 0 0 str. 1-48 2026 0000-00-00 Zaloznikova NiDoloceno NiDoloceno 0000-00-00 0000-00-00 2026-03-10 616.711.1-009.7 2045-2322 10.1038/s41598-026-43903-z 271706627 https://doi.org/10.1038/s41598-026-43903-z 1 4d84cb3d-20d4-11f1-9e8d-005056ac49c0 https://repozitorij.upr.si/Dokument.php?lang=slv&id=33211 https://www.nature.com/articles/s41598-026-43903-z#citeas 1 4c62253e-20d4-11f1-9e8d-005056ac49c0 https://repozitorij.upr.si/Dokument.php?lang=slv&id=33210 RAZ_Majcen_Rosker_Ziva_2026.pdf RAZ_Majcen_Rosker_Ziva_2026.pdf 1 9183F2E1DA3567B124C3F90938F538C0 cd5955cd6f4cf608052812d8f653db75f12f29c3ea2cc298d90f41bcc7b24096 7ff9a076-20d4-11f1-9e8d-005056ac49c0 https://repozitorij.upr.si/Dokument.php?lang=slv&id=33212 Fakulteta za vede o zdravju 0 0 0