References
- A complete guide to the random forest algorithm. 2021. Built in [Internet]; [accessed 2021 Jan 2]. https://builtin.com/data-science/random-forest-algorithm.
- An introduction to support Vector Machines. 1999. Guide books; [accessed 2020 Jul 20]. 10.5555/345662.
- Bloch KE, Brack T, Simonds AK. 2015. Self-assessment in respiratory medicine. European Respiratory Society.
- Brownlee J. 2016. K-nearest neighbors for machine learning. Machine Learning Mastery [Internet]; [accessed 2021 Jan 2]. https://machinelearningmastery.com/k-nearest-neighbors-for-machine-learning/.
- Brownlee J. 2018. A gentle introduction to k-fold cross-validation. Machine Learning Mastery [Internet]. [accessed 2022 Feb 17]. https://machinelearningmastery.com/k-fold-cross-validation/.
- Bruyneel A-V, Chavet P, Bollini G, Allard P, Berton E, Mesure S. 2009. Dynamical asymmetries in idiopathic scoliosis during forward and lateral initiation step. Eur Spine J. 18(2):188–195.
- Chan V, Fong GCY, Luk KDK, Yip B, Lee M-K, Wong M-S, Lu DDS, Chan T-K. 2002. A genetic locus for adolescent idiopathic scoliosis linked to chromosome 19p13.3. Am J Hum Genet. 71(2):401–406.
- Cho J, Cho Y-S, Moon S-B, Kim M-J, Lee HD, Lee SY, Ji Y-H, Park Y-S, Han C-S, Jang S-H. 2018. Scoliosis screening through a machine learning based gait analysis test. Int J Precis Eng Manuf. 19(12):1861–1872.
- Chockalingam N, Dangerfield PH, Rahmatalla A, Ahmed E-N, Cochrane T. 2004. Assessment of ground reaction force during scoliotic gait. Eur Spine J. 13(8):750–754.
- Cobb JR, Cobb JL. 1948. Outline for the study of scoliosis [Internet]; [accessed 2018 Nov 19]. https://www.scienceopen.com/document?vid=76a12f1e-c7ef-4cc2-8aec-0904b520cd98.
- Davis RB, Õunpuu S, Tyburski D, Gage JR. 1991. A gait analysis data collection and reduction technique. Human Movement Sci. 10(5):575–587.
- Ensemble methods. scikit-learn 0.23.1 documentation; [accessed 2020 Jul 20]. https://scikit-learn.org/stable/modules/ensemble.html.
- Fix E, Hodges JL. 1989. Discriminatory analysis. Nonparametric discrimination: consistency properties. Int Statis Rev/Rev Int Statis. 57(3):238–247.
- Galbusera F, Casaroli G, Bassani T. 2019. Artificial intelligence and machine learning in spine research. JOR Spine. 2(1):e1044.
- Gaussian Processes. scikit-learn 0.23.1 documentation; [accessed 2020 Aug 3]. https://scikit-learn.org/stable/modules/gaussian_process.html#gaussian-process-classification-gpc.
- Ghaneei M, Ekyalimpa R, Westover L, Parent EC, Adeeb S. 2019. Customized k-nearest neighbourhood analysis in the management of adolescent idiopathic scoliosis using 3D markerless asymmetry analysis. Comput Methods Biomech Biomed Eng. 22(7):696–705.
- Ghaneei M, Komeili A, Li Y, Parent EC, Adeeb S. 2018. 3D Markerless asymmetry analysis in the management of adolescent idiopathic scoliosis. BMC Musculoskelet Disord. 19(1):385.
- Greiner AK. 2002. Adolescent idiopathic scoliosis: radiologic decision-making. Am Fam Physician. 65(9):1817.
- Guilbert ML, Raison M, Fortin C, Achiche S. 2019. Development of a multibody model to assess efforts along the spine for the rehabilitation of adolescents with idiopathic scoliosis. J Musculoskelet Neuronal Interact. 19(1):4–12.
- Hoffman DA, Lonstein JE, Morin MM, Visscher W, Harris BS, Boice JD. 1989. Breast cancer in women with scoliosis exposed to multiple diagnostic x rays. J Natl Cancer Inst. 81(17):1307–1312.
- Kanko RM, Laende E, Selbie WS, Deluzio KJ. 2021. Inter-session repeatability of markerless motion capture gait kinematics. J Biomech. 121:110422.
- Lenke LG, Betz RR, Harms J, Bridwell KH, Clements DH, Lowe TG, Blanke K. 2001. Adolescent idiopathic scoliosis: a new classification to determine extent of spinal arthrodesis. J Bone Joint Surg Am. 83(8):1169–1181.
- Liu XC, Thometz JG, Lyon RM, Klein J. 2001. Functional classification of patients with idiopathic scoliosis assessed by the Quantec system: a discriminant functional analysis to determine patient curve magnitude. Spine. 26(11):1274–1278; discussion 1279.
- Lowe TG, Edgar M, Margulies JY, Miller NH, Raso VJ, Reinker KA, Rivard CH. 2000. Etiology of idiopathic scoliosis: current trends in research. J Bone Joint Surg Am. 82(8):1157–1168.
- Lu TW, O'Connor JJ. 1999. Bone position estimation from skin marker co-ordinates using global optimisation with joint constraints. J Biomech. 32(2):129–134.
- Mahaudens P, Banse X, Mousny M, Detrembleur C. 2009. Gait in adolescent idiopathic scoliosis: kinematics and electromyographic analysis. Eur Spine J. 18(4):512–521.
- Mahaudens P, Banse X, Mousny M, Raison M, Detrembleur C. 2013. Very short-term effect of brace wearing on gait in adolescent idiopathic scoliosis girls. Eur Spine J. 22(11):2399–2406.
- Mahaudens P, Detrembleur C, Mousny M, Banse X. 2009. Gait in adolescent idiopathic scoliosis: energy cost analysis. Eur Spine J. 18(8):1160–1168.
- Mahaudens P, Raison M, Banse X, Mousny M, Detrembleur C. 2014. Effect of long-term orthotic treatment on gait biomechanics in adolescent idiopathic scoliosis. Spine J. 14(8):1510–1519.
- Morrissy R, Weinstein S. 1996. Lovell and Winter’s pediatric orthopedics. undefined [Internet]; [accessed 2020 Sep 24]. https://www.wolterskluwer.com/en/solutions/ovid/lovell-and-winters-pediatric-orthopaedics-768
- Murtagh F. 1991. Multilayer perceptrons for classification and regression. Neurocomputing. 2(5-6):183–197.
- Nash CLJ, Gregg EC, Brown RH, Pillai K. 1979. Risks of exposure to X-rays in patients undergoing long-term treatment for scoliosis. J Bone Joint Surg Am. 61(3):371–374.
- Neural Network Model – an overview | ScienceDirect Topics; [accessed 2020 Jul 17]. https://www.sciencedirect.com/topics/computer-science/neural-network-model.
- Nishida M, Nagura T, Fujita N, Hosogane N, Tsuji T, Nakamura M, Matsumoto M, Watanabe K. 2017. Position of the major curve influences asymmetrical trunk kinematics during gait in adolescent idiopathic scoliosis. Gait & Posture. 51:142–148.
- Raison M, Aubin C-E, Detrembleur C, Fisette P, Mahaudens P, Samin J-C. 2010. Quantification of global intervertebral torques during gait: comparison between two subjects with different scoliosis severities. Stud Health Technol Inform. 158:107–111.
- Raison M, Ballaz L. 2018. Lombo-sacral joint efforts during gait: comparison between healthy and scoliotic subjects. Studies in health technology and informatics [Internet]; [accessed 2018 Nov 23]. https://www.academia.edu/14823860/Lombo-sacral_joint_efforts_during_gait_comparison_between_healthy_and_scoliotic_subjects.
- Ramirez L, Durdle NG, Raso VJ, Hill DL. 2006. A support vector machines classifier to assess the severity of idiopathic scoliosis from surface topography. IEEE Trans Inf Technol Biomed. 10(1):84–91.
- Robotran. Home. [accessed 2018 Dec 3]. http://www.robotran.be/.
- Roy S, Grünwald ATD, Alves-Pinto A, Lampe R. 2020. Automatic analysis method of 3D images in patients with scoliosis by quantifying asymmetry in transverse contours. Biocybern Biomed Eng. 40(4):1486–1498.
- Samadi B, Raison M, Achiche S, Fortin C. 2020. Identification of the most relevant intervertebral effort indicators during gait of adolescents with idiopathic scoliosis. Comput Methods Biomech Biomed Eng.23(10):1–11.
- Samin J, Fisette P. 2003. Symbolic modeling of multibody systems, 2003, Volume 112, ISBN: 978-90-481-6425-7.
- Schmid S, Schmid S, Studer D, Hasler C, Romkes J, Taylor W, Brunner R, Lorenzetti S. 2015. Non-invasive assessment of spinal kinematics during gait in patients with adolescent idiopathic scoliosis. Physiotherapy. 101(Suppl1):E1346.
- scikit-learn. 3.2. Tuning the hyper-parameters of an estimator. [Internet]; [accessed 2021 Nov 15]. https://scikit-learn/stable/modules/grid_search.html.
- sklearn.discriminant_analysis.LinearDiscriminantAnalysis. scikit-learn 0.23.1 documentation; [accessed 2020 Aug 3]. https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html.
- sklearn.ensemble.AdaBoostClassifier. scikit-learn 0.23.1 documentation; [accessed 2020 Aug 3]. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html.
- sklearn.ensemble.BaggingClassifier. scikit-learn 0.23.1 documentation; [accessed 2020 Aug 3]. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html.
- sklearn.ensemble.ExtraTreesClassifier. scikit-learn 0.23.1 documentation; [accessed 2020 Aug 3]. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html.
- sklearn.ensemble.RandomForestClassifier. scikit-learn 0.23.1 documentation; [accessed 2020 Aug 3]. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html.
- sklearn.linear_model.LogisticRegression. scikit-learn 0.23.1 documentation; [accessed 2020 Aug 3]. https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#.
- sklearn.naive_bayes.GaussianNB. scikit-learn 0.23.1 documentation; [accessed 2020 Aug 3]. https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html.
- sklearn.neighbors.KNeighborsClassifier. scikit-learn 0.23.1 documentation; [accessed 2020 Aug 3]. https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier.
- sklearn.neighbors.RadiusNeighborsClassifier. scikit-learn 0.23.1 documentation; [accessed 2020 Jul 22]. https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.RadiusNeighborsClassifier.html.
- sklearn.neural_network.MLPClassifier. scikit-learn 0.23.1 documentation; [accessed 2020 Aug 3]. https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html.
- sklearn.preprocessing.StandardScaler. scikit-learn 0.23.1 documentation; [accessed 2020 Jul 20]. https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html.
- sklearn.svm.SVC. scikit-learn 0.23.1 documentation; [accessed 2020 Aug 3]. https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html.
- Support Vector Machines. scikit-learn 0.23.1 documentation; [accessed 2020 Jul 20]. https://scikit-learn.org/stable/modules/svm.html.
- Tabard-Fougère A, Bonnefoy-Mazure A, Hanquinet S, Lascombes P, Armand S, Dayer R. 2017. Validity and reliability of spine rasterstereography in patients with adolescent idiopathic scoliosis. Spine. 42(2):98–105.
- Tamura H, Tanaka R, Kawanishi H. 2020. Reliability of a markerless motion capture system to measure the trunk, hip and knee angle during walking on a flatland and a treadmill. J Biomech. 109:109929.
- Weisz I, Jefferson RJ, Turner-Smith AR, Houghton GR, Harris JD. 1988. ISIS scanning: a useful assessment technique in the management of scoliosis. Spine. 13(4):405–408.
- Yazji M, Raison M, Aubin C-É, Labelle H, Detrembleur C, Mahaudens P, Mousny M. 2015. Are the mediolateral joint forces in the lower limbs different between scoliotic and healthy subjects during gait? Scoliosis. 10(Suppl 2):S3.