A preliminary study in classification of the severity of spine deformation in adolescents with lumbar/thoracolumbar idiopathic scoliosis using machine learning algorithms based on lumbosacral joint efforts during gait

Bahare Samadi, Maxime Raison , Philippe Mahaudens, Christine Detrembleur et Sofiane Achiche

Article de revue (2023)

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Département:	Département de génie mécanique
URL de PolyPublie:	https://publications.polymtl.ca/51874/
Titre de la revue:	Computer Methods in Biomechanics and Biomedical Engineering (vol. 26, no 11)
Maison d'édition:	Taylor & Francis
DOI:	10.1080/10255842.2022.2117547
URL officielle:	https://doi.org/10.1080/10255842.2022.2117547
Date du dépôt:	18 avr. 2023 14:59
Dernière modification:	25 sept. 2024 16:42

Citer en APA 7:	Samadi, B., Raison, M., Mahaudens, P., Detrembleur, C., & Achiche, S. (2023). A preliminary study in classification of the severity of spine deformation in adolescents with lumbar/thoracolumbar idiopathic scoliosis using machine learning algorithms based on lumbosacral joint efforts during gait. Computer Methods in Biomechanics and Biomedical Engineering, 26(11), 1341-1352. https://doi.org/10.1080/10255842.2022.2117547

Citer en APA 7:

Samadi, B., Raison, M., Mahaudens, P., Detrembleur, C., & Achiche, S. (2023). A preliminary study in classification of the severity of spine deformation in adolescents with lumbar/thoracolumbar idiopathic scoliosis using machine learning algorithms based on lumbosacral joint efforts during gait. Computer Methods in Biomechanics and Biomedical Engineering, 26(11), 1341-1352. https://doi.org/10.1080/10255842.2022.2117547

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