A Decision Tree Can Increase Accuracy When Assessing Curve Types According to Lenke Classification of Adolescent Idiopathic Scoliosis


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Abstract

Study Design.The assignment of adolescent idiopathic scoliosis (AIS) curves into curve types (1–6), as described by Lenke et al, was evaluated by 12 independent observers using the original description versus a decisional tree algorithm.Objective.To determine whether a decision tree algorithm can improve classification accuracy using the Lenke classification for AIS.Summary of Background Data.Curve type classification in AIS relies on several parameters to consider, and its relative complexity has lead to conflicting studies that reported fair-to-excellent interobserver reliability. King's classification reliability was shown to be improved using a rule-based automated algorithm. No similar algorithm for Lenke's classification currently exists.Methods.A clinical diagram derived from a decision tree was developed to help clinicians classify AIS curves. Twelve clinicians and research assistants were asked to classify AIS curves using 2 methods: the original Lenke chart alone and the decision tree diagram in addition to the Lenke Chart. Wilcoxon ranking tests were used to evaluate any difference in classification accuracy and speed for both methods. Mann-Whitney tests were used to compare experts and nonexperts results. Pearson correlation was calculated to evaluate the relationship between accuracy and time taken to classify.Results.Use of the decision tree for curve type determination improved classification accuracy from 77.2% to 92.9% (P = 0.005) without requiring more time to classify. This improvement was statistically significant (P < 0.05). A statistically significant correlation between accuracy and time spent classifying when the decision tree is used was also observed (R = 0.62, P = 0.032).Conclusion.Transfer of a computer algorithm, a decision tree, to a clinical diagram improved both accuracy ofAIS classification. Algorithmic diagrams could prove beneficial to increase classification reliability due to their systematic approach.

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