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Mandel, W., Korez, R., Nault, M. L., Parent, S., & Kadoury, S. (octobre 2016). Classification of Progressive and Non-progressive Scoliosis Patients Using Discriminant Manifolds [Communication écrite]. 4th International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, Ahtens, Greece. Lien externe
Nault, M. L., Labelle, H., Aubin, C.-É., Sangole, A., & Balazinski, M. (2009). Fuzzy logic assisted surgical planning in adolescent idiopathic scoliosis. Journal of Spinal Disorders & Techniques, 22(4), 263-269. Lien externe
Nault, M. L., Labelle, H., Aubin, C.-É., & Balazinski, M. (2007). The Use of Fuzzy Logic to Select Which Curves Need to Be Instrumented and Fused in Adolescent Idiopathic Scoliosis - a Feasibility Study. Journal of Spinal Disorders & Techniques, 20(8), 594-603. Lien externe
Nault, M. L., Labelle, H., Aubin, C.-É., & Balazinski, M. (juin 2004). Fuzzy logic as an aid to surgery planning in idiopathic scoliosis [Communication écrite]. 5th International Research Society for Spinal Deformities Symposium (IRSSD 2004), Vancouver, Canada. Non disponible
Nault, M. L., Labelle, H., & Aubin, C.-É. (2003). Modélisation par logique floue du processus de sélection de fusion des courbures thoraciques hautes dans la scoliose idiopathique. Médecines sciences, 2(19), 44-44. Non disponible
Phan, P., Mezghani, N., Nault, M. L., Aubin, C.-É., Parent, S., De Guise, J., & Labelle, H. (2010). A Decision Tree Can Increase Accuracy When Assessing Curve Types According to Lenke Classification of Adolescent Idiopathic Scoliosis. Spine, 35(10), 1054-1059. Lien externe
