A Kohonen neural network description of scoliosis fused regions and their corresponding Lenke classification

  • N. Mezghani
  • P. Phan
  • A. Mitiche
  • H. Labelle
  • J. A. de Guise
Original Article



Surgical instrumentation for adolescent idiopathic scoliosis (AIS) is a complex procedure where selection of the appropriate curve segment to fuse, i.e., fusion region, is a challenging decision in scoliosis surgery. Currently, the Lenke classification model is used for fusion region evaluation and surgical planning. Retrospective evaluation of Lenke classification and fusion region results was performed.


Using a database of 1,776 surgically treated AIS cases, we investigated a topologically ordered self organizing Kohonen network, trained using Cobb angle measurements, to determine the relationship between the Lenke class and the fusion region selection. Specifically, the purpose was twofold (1) produce two spatially matched maps, one of Lenke classes and the other of fusion regions, and (2) associate these two maps to determine where the Lenke classes correlate with the fused spine regions.


Topologically ordered maps obtained using a multi-center database of surgically treated AIS cases, show that the recommended fusion region agrees with the Lenke class except near boundaries between Lenke map classes. Overall agreement was 88%.


The Lenke classification and fusion region agree in the majority of adolescent idiopathic scoliosis when reviewed retrospectively. The results indicate the need for spinal fixation instrumentation variation associated with the Lenke classification.


Adolescent idiopathic scoliosis Neural network Lenke classification Fusion level Computer-aided decision 


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  1. 1.
    Aubin CE, Labelle H, Ciolofan OC (2007) Variability of spinal instrumentation configurations in adolescent idiopathic scoliosis. Eur Spine J 16(1): 57–64PubMedCrossRefGoogle Scholar
  2. 2.
    Carman DL, Browne RH, Birch JG (1990) Measurement of scoliosis and kyphosis radiographs: intraobserver and interobserver variation. J Bone Joint Surg Am 72: 328–333PubMedGoogle Scholar
  3. 3.
    Cil A, Pekmezci M, Yazici M (2005) The validity of lenke criteria for defining structural proximal thoracic curves in patients with adolescent idiopathic scoliosis. Spine 30: 2550–2555PubMedCrossRefGoogle Scholar
  4. 4.
    Duda RO, Hart PE (1973) Pattern Classification and Scene Analysis. Wiley, New YorkGoogle Scholar
  5. 5.
    Duong L, Cheriet F, Labelle H (2006) Three-dimensional classification of spinal deformities using fuzzy clustering. Spine 31(8): 923–930PubMedCrossRefGoogle Scholar
  6. 6.
    Kohonen T (1995) Self-organizing maps. Springer, BerlinCrossRefGoogle Scholar
  7. 7.
    LeBail E, Mitiche A (1989) Quantification vectorielle d’images par le rèseau neuronal de kohonen. Traitement du Signal 6(6): 529–539Google Scholar
  8. 8.
    Lenke L (2007) The lenke classification system of operative adolescent idiopathic scoliosis. Neurosurg Clin N Am 18(2): 199–206PubMedCrossRefGoogle Scholar
  9. 9.
    Lenke LG, Betz RR, Bridwell KH, Clements DH, Harms J, Lowe TG, Shufflebarger HL (1998) Intraobserver and interobserver reliability of the classification of thoracic adolescent idiopathic scoliosis. J Bone Joint Surg Am 80: 1097–1106PubMedGoogle Scholar
  10. 10.
    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: 1169–1181PubMedGoogle Scholar
  11. 11.
    Lenke LG, Betz RR, Clements D, Merola A, Haher T, Lowe T, Newton P, Bridwell KH, Blanke K (2002) Curve prevalence of a new classification of operative adolescent idiopathic scoliosis. Spine 27(6): 604–611PubMedCrossRefGoogle Scholar
  12. 12.
    Lippman R (1987) An introduction to computing with neural networks. IEEE ASSP Mag 3: 4–22CrossRefGoogle Scholar
  13. 13.
    Loder RT, Urquhart A, Sten H (1995) Variability in cobb angle measurements in children with congenital scoliosis. J Bone Joint Surg Am 77: 768–770Google Scholar
  14. 14.
    Lowe TG, Alongi PR, Smith DAB (2003) Anterior single rod instrumentation for thoracolumbar adolescent idiopathic scoliosis with and without the use of structural interbody support. Spine 28: 208–216CrossRefGoogle Scholar
  15. 15.
    Lowe TG, Alongi PR, Smith DAB (2003) Anterior single rod instrumentation for thoracolumbar adolescent idiopathic scoliosis with and without the use of structural interbody support. Spine 28: 2232–2241PubMedCrossRefGoogle Scholar
  16. 16.
    Mezghani N, Chav R, Humbert L, Parent S, Skalli W, de Guise JA (2008) A computer-based classifier of three dimensional spinal scoliosis severity. Int J Comput Assist Radiol Surg 3(1–2): 55–60CrossRefGoogle Scholar
  17. 17.
    Mezghani N, Cheriet M, Mitiche A (2003) Combination of pruned kohonen maps for on-line Arabic characters recognition. In: Seventh international conference on document analysis and recognition, vol 2, Edinburgh. pp 900–905Google Scholar
  18. 18.
    Mitiche A, Aggarwal JK (1996) Pattern category assignement by neural networks and the nearest neighbors rule. Int J Pattern Recog Artif Intell 10: 393–408CrossRefGoogle Scholar
  19. 19.
    Oja M, Kaski S, Kohonen T (2003) Bibliography of self-organizing map SOM papers: 1998–2001 addendum. Neural Comput Surv 3: 1–156Google Scholar
  20. 20.
    Phan P, Labelle H, Ouellet J, Mezghani N, de Guise JA (2011) The use of a decision tree based on the literature can efficiently output the levels of fusion alternatives in the surgical treatment of ais. In: Canadian spine society annual meetingGoogle Scholar
  21. 21.
    Ritter H, Schulten K (1988) Kohonen’s self-organizing maps: exploring their computational capabilities. In: IEEE international joint conference on neural networks, pp 109–116, San DiegoGoogle Scholar
  22. 22.
    Robitaille M, Aubin CE, Labelle H (2007) Intra and interobserver variability of preoperative planning for surgical instrumentation in adolescent idiopathic scoliosis. Eur Spine J 16(10): 1604–1614PubMedCrossRefGoogle Scholar
  23. 23.
    Sabourin M, Mitiche A (1993) Modeling and classification of shape using a Kohonen associative memory with selective multiresolution. Neural Netw 6(2): 275–283CrossRefGoogle Scholar
  24. 24.
    Stokes IA, Sangole AP, Aubin CE (2009) Classification of scoliosis deformity three-dimensional spinal shape by cluster analysis. Spine 34(6): 584–590PubMedCrossRefGoogle Scholar
  25. 25.
    Su M, Chang H, Chou C (2002) A novel measure for quantifying the topology preservation of self-organizing feature maps. Neural Process Lett 15: 137–145CrossRefGoogle Scholar
  26. 26.
    Tso B, Mather PM (2009) Classification methods for remotely sensed data. 2nd edn. CRC Press, New YorkCrossRefGoogle Scholar
  27. 27.
    Uriarte E, Martín F (2005) Topology preservation in som. Int J Appl Math Comput Sci 1: 19–22Google Scholar

Copyright information

© CARS 2012

Authors and Affiliations

  • N. Mezghani
    • 1
  • P. Phan
    • 2
  • A. Mitiche
    • 3
  • H. Labelle
    • 4
  • J. A. de Guise
    • 1
  1. 1.Laboratoire de recherche en imagerie et orthopédieÉcole de technologie supérieure Centre de recherche du CHUM, Hôpital Notre-DameMontrealCanada
  2. 2.Research Center, Sainte-Justine University Hospital CenterMontrealCanada
  3. 3.Institut National de la Recherche Scientifique, INRS-EMTMontrealCanada
  4. 4.Department of Orthopaedics, Research CenterSainte-Justine HospitalMontrealCanada

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