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A Kohonen neural network description of scoliosis fused regions and their corresponding Lenke classification [r-libre/391]

Mezghani, Neila; Phan, Philippe; Mitiche, Amar; Labelle, Hubert, & De Guise, Jacques A. (2012). A Kohonen neural network description of scoliosis fused regions and their corresponding Lenke classification. International Journal of Computer Assisted Radiology and Surgery, 7 (2), 257-264. https://doi.org/10.1007/s11548-011-0667-0

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Item Type: Journal Articles
Refereed: Yes
Status: Published
Abstract: Purpose - 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 Methods - 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. Results - 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%. Conclusion - 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.
Depositor: Mezghani, Neila
Owner / Manager: Neila Mezghani
Deposited: 24 Oct 2014 16:43
Last Modified: 29 Oct 2019 15:39

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