Philippe Phan1, Neila Mezghani, Eugene K Wai, Jacques de Guise, Hubert Labelle. 1. Research Center, Sainte-Justine University Hospital Center, 3175 Chemin de la Côte-Sainte-Catherine, Montreal, Quebec, Canada H3T 1C5; Division of Orthopaedic Surgery, Department of Surgery, The Ottawa Hospital, University of Ottawa, Civic Campus, 1053 Carling Ave., Ottawa, Ontario, Canada K2A 3C8. Electronic address: philippe.phan@gmail.com.
Abstract
BACKGROUND CONTEXT: Variability in classifying and selecting levels of fusion in adolescent idiopathic scoliosis (AIS) has been repeatedly documented. Several computer algorithms have been used to classify AIS based on the geometrical features, but none have attempted to analyze its treatment patterns. PURPOSE: To use self-organizing maps (SOM), a kind of artificial neural networks, to reliably classify AIS cases from a large database. To analyze surgeon's treatment pattern in selecting curve regions to fuse in AIS using Lenke classification and SOM. STUDY DESIGN: This is a technical concept article on the possibility and benefits of using neural networks to classify AIS and a retrospective analysis of AIS curve regions selected for fusion. PATIENT SAMPLE: A total of 1,776 patients surgically treated for AIS were prospectively enrolled in a multicentric database. Cobb angles were measured on AIS patient spine radiographies, and patients were classified according to Lenke classification. OUTCOME MEASURES: For each patient in the database, surgical approach and levels of fusion selected by the treating surgeon were recorded. METHODS: A Kohonen SOM was generated using 1,776 surgically treated AIS cases. The quality of the SOM was tested using topological error. Percentages of prediction of fusion based on Lenke classification for each patient in the database and for each node in the SOM were calculated. Lenke curve types, treatment pattern, and kappa statistics for agreement between fusion realized and fusion recommended by Lenke classification were plotted on each node of the map. RESULTS: The topographic error for the SOM generated was 0.02, which demonstrates high accuracy. The SOM differentiates clear clusters of curve type nodes on the map. The SOM also shows epicenters for main thoracic, double thoracic, and thoracolumbar/lumbar curve types and transition zones between clusters. When cases are taken individually, Lenke classification predicted curve regions fused by the surgeon in 46% of cases. When those cases are reorganized by the SOM into nodes, Lenke classification predicted the curve regions to fuse in 82% of the nodes. Agreement with Lenke classification principles was high in epicenters for curve types 1, 2, and 5, moderate in cluster for curve types 3, 4, and 6, and low in transition zones between curve types. CONCLUSIONS: An AIS SOM with high accuracy was successfully generated. Lenke classification principles are followed in 46% of the cases but in 82% of the nodes on the SOM. The SOM highlights the tendency of surgeons to follow Lenke classification principles for similar curves on the SOM. Self-organizing map classification of AIS could be valuable to surgeons because it bypasses the limitations imposed by rigid classification such as cutoff values on Cobb angle to define curve types. It can extract similar cases from large databases to analyze and guide treatment.
BACKGROUND CONTEXT: Variability in classifying and selecting levels of fusion in adolescent idiopathic scoliosis (AIS) has been repeatedly documented. Several computer algorithms have been used to classify AIS based on the geometrical features, but none have attempted to analyze its treatment patterns. PURPOSE: To use self-organizing maps (SOM), a kind of artificial neural networks, to reliably classify AIS cases from a large database. To analyze surgeon's treatment pattern in selecting curve regions to fuse in AIS using Lenke classification and SOM. STUDY DESIGN: This is a technical concept article on the possibility and benefits of using neural networks to classify AIS and a retrospective analysis of AIS curve regions selected for fusion. PATIENT SAMPLE: A total of 1,776 patients surgically treated for AIS were prospectively enrolled in a multicentric database. Cobb angles were measured on AISpatient spine radiographies, and patients were classified according to Lenke classification. OUTCOME MEASURES: For each patient in the database, surgical approach and levels of fusion selected by the treating surgeon were recorded. METHODS: A Kohonen SOM was generated using 1,776 surgically treated AIS cases. The quality of the SOM was tested using topological error. Percentages of prediction of fusion based on Lenke classification for each patient in the database and for each node in the SOM were calculated. Lenke curve types, treatment pattern, and kappa statistics for agreement between fusion realized and fusion recommended by Lenke classification were plotted on each node of the map. RESULTS: The topographic error for the SOM generated was 0.02, which demonstrates high accuracy. The SOM differentiates clear clusters of curve type nodes on the map. The SOM also shows epicenters for main thoracic, double thoracic, and thoracolumbar/lumbar curve types and transition zones between clusters. When cases are taken individually, Lenke classification predicted curve regions fused by the surgeon in 46% of cases. When those cases are reorganized by the SOM into nodes, Lenke classification predicted the curve regions to fuse in 82% of the nodes. Agreement with Lenke classification principles was high in epicenters for curve types 1, 2, and 5, moderate in cluster for curve types 3, 4, and 6, and low in transition zones between curve types. CONCLUSIONS: An AIS SOM with high accuracy was successfully generated. Lenke classification principles are followed in 46% of the cases but in 82% of the nodes on the SOM. The SOM highlights the tendency of surgeons to follow Lenke classification principles for similar curves on the SOM. Self-organizing map classification of AIS could be valuable to surgeons because it bypasses the limitations imposed by rigid classification such as cutoff values on Cobb angle to define curve types. It can extract similar cases from large databases to analyze and guide treatment.
Authors: Wesley M Durand; Renaud Lafage; D Kojo Hamilton; Peter G Passias; Han Jo Kim; Themistocles Protopsaltis; Virginie Lafage; Justin S Smith; Christopher Shaffrey; Munish Gupta; Michael P Kelly; Eric O Klineberg; Frank Schwab; Jeffrey L Gum; Gregory Mundis; Robert Eastlack; Khaled Kebaish; Alex Soroceanu; Richard A Hostin; Doug Burton; Shay Bess; Christopher Ames; Robert A Hart; Alan H Daniels Journal: Eur Spine J Date: 2021-04-15 Impact factor: 3.134
Authors: Edgar García-Cano; Fernando Arámbula Cosío; Luc Duong; Christian Bellefleur; Marjolaine Roy-Beaudry; Julie Joncas; Stefan Parent; Hubert Labelle Journal: Med Biol Eng Comput Date: 2018-06-09 Impact factor: 2.602