BACKGROUND: Variability in surgical strategies for the treatment of adolescent idiopathic scoliosis (AIS) has been demonstrated despite the existence of classifications to guide selection of AIS curves to include in fusion. Decision trees and rule-based algorithms have demonstrated their potential to improve reliability of AIS classification because of their systematic approach and they have also been proposed in algorithms for selection of instrumentation levels in scoliosis. Our working hypothesis is that a rule-based algorithm with a knowledge base extracted from the literature can efficiently output surgical strategies alternatives for a given AIS case. Our objective is to develop a rule-based algorithm based on peer-reviewed literature to output alternative surgical strategies for approach and levels of fusion. METHODS: A literature search of all English Manuscripts published between 2000 and December 2009 with Pubmed and Google scholar electronic search using the following keywords: "adolescent idiopathic scoliosis" and "surgery" alternatively with "levels of fusion" or "approach". All returned abstracts were screened for contents that could contain rules to include in the knowledge base. A dataset of 1,556 AIS cases treated surgically was used to test the surgical strategy rule-based algorithm (SSRBA) and evaluate how many surgical treatments are covered by the algorithm. The SSRBA was programmed using Matlab. Descriptive statistic was used to evaluate the ability of the rule-based algorithm to cover all treatment alternatives. RESULTS: A SSRBA was successfully developed following Lenke classification's concept that the spine is divided into three curve segments [proximal thoracic (PT), main thoracic (MT) and thoracolumbar/lumbar (TL)]. Each of the 1,556 AIS patients in the dataset was ran through the SSRBA. It proposed an average of 3.78 (±2.06) surgical strategies per case. Overall, the SSRBA is able to match the treatment offered by the surgeon in approach and level of fusion 70 % of the time (with one vertebral level leeway). CONCLUSION: This study is to the author's knowledge the first attempt at proposing an algorithm to output all surgical alternatives for a given AIS case. It uses a rule-based algorithm with a knowledge base extracted from peer-reviewed literature in an area with great variability. When tested against a database of AIS patients treated surgically, the SSRBA developed has the ability to propose a surgical plan with respect to approach and levels of fusion that match the surgeon's plan in a great majority of cases. Since this SSRBA seems to output multiple valid surgical strategies, it could allow the comparisons of various strategies and the outcomes achieved in similar cases in large databases for a given case and guide surgical treatment.
BACKGROUND: Variability in surgical strategies for the treatment of adolescent idiopathic scoliosis (AIS) has been demonstrated despite the existence of classifications to guide selection of AIS curves to include in fusion. Decision trees and rule-based algorithms have demonstrated their potential to improve reliability of AIS classification because of their systematic approach and they have also been proposed in algorithms for selection of instrumentation levels in scoliosis. Our working hypothesis is that a rule-based algorithm with a knowledge base extracted from the literature can efficiently output surgical strategies alternatives for a given AIS case. Our objective is to develop a rule-based algorithm based on peer-reviewed literature to output alternative surgical strategies for approach and levels of fusion. METHODS: A literature search of all English Manuscripts published between 2000 and December 2009 with Pubmed and Google scholar electronic search using the following keywords: "adolescent idiopathic scoliosis" and "surgery" alternatively with "levels of fusion" or "approach". All returned abstracts were screened for contents that could contain rules to include in the knowledge base. A dataset of 1,556 AIS cases treated surgically was used to test the surgical strategy rule-based algorithm (SSRBA) and evaluate how many surgical treatments are covered by the algorithm. The SSRBA was programmed using Matlab. Descriptive statistic was used to evaluate the ability of the rule-based algorithm to cover all treatment alternatives. RESULTS: A SSRBA was successfully developed following Lenke classification's concept that the spine is divided into three curve segments [proximal thoracic (PT), main thoracic (MT) and thoracolumbar/lumbar (TL)]. Each of the 1,556 AIS patients in the dataset was ran through the SSRBA. It proposed an average of 3.78 (±2.06) surgical strategies per case. Overall, the SSRBA is able to match the treatment offered by the surgeon in approach and level of fusion 70 % of the time (with one vertebral level leeway). CONCLUSION: This study is to the author's knowledge the first attempt at proposing an algorithm to output all surgical alternatives for a given AIS case. It uses a rule-based algorithm with a knowledge base extracted from peer-reviewed literature in an area with great variability. When tested against a database of AIS patients treated surgically, the SSRBA developed has the ability to propose a surgical plan with respect to approach and levels of fusion that match the surgeon's plan in a great majority of cases. Since this SSRBA seems to output multiple valid surgical strategies, it could allow the comparisons of various strategies and the outcomes achieved in similar cases in large databases for a given case and guide surgical treatment.
Authors: T R Kuklo; L G Lenke; D S Won; E J Graham; F A Sweet; R R Betz; K H Bridwell; K M Blanke Journal: Spine (Phila Pa 1976) Date: 2001-09-15 Impact factor: 3.468
Authors: R R Betz; J Harms; D H Clements; L G Lenke; T G Lowe; H L Shufflebarger; D Jeszenszky; B Beele Journal: Spine (Phila Pa 1976) Date: 1999-02-01 Impact factor: 3.468
Authors: Peter O Newton; Frances D Faro; Lawrence G Lenke; Randal R Betz; David H Clements; Thomas G Lowe; Thomas R Haher; Andrew A Merola; Linda P D'Andrea; Michelle Marks; Dennis R Wenger Journal: Spine (Phila Pa 1976) Date: 2003-10-15 Impact factor: 3.468
Authors: Baron S Lonner; Joshua D Auerbach; Michael Estreicher; Andrew H Milby; Kristin E Kean; Georgia Panagopoulos; David Chang Journal: Spine (Phila Pa 1976) Date: 2009-01-15 Impact factor: 3.468