The use of artificial intelligence algorithms to guide surgical treatment of adolescent idiopathic scoliosis

Adolescent idiopathic scoliosis (AIS) is a three-dimensional deformity of the spine. Management of AIS includes conservative treatment with observation, the use of braces to limit its progression or surgery to correct the deformity and cease its progression. Surgical treatment of AIS remains controversial with respect to not only indications but also surgical strategy.  Despite the existence of classifications to guide AIS treatment, intra- and inter-observer variability in surgical strategy has been described in the literature.  Technological advances and their integration into the medical field have led to the use of artificial intelligence (AI) algorithms to assist with AIS classification and three-dimensional evaluation. With the evolution of surgical techniques and instrumentation, it is probable that the intra- and inter-observer variability could increase. However, some AI algorithms have shown the potential to lower variability in classification and guide treatment.  The overall objective of this thesis was to develop software using AI tools that has the capacity to integrate AIS patient data and available evidence from the literature to guide AIS surgical treatment. To do so, a literature review on existing computer applications developed with regards to AIS evaluation and management was undertaken to gather all the elements that would lead to usable software in the clinical setting. This review highlighted the fact that many applications use a non-descript “black box” between input and output, which limits clinical integration where management based on evidence is essential.  In the first study, we developed a decision tree to classify AIS based on the Lenke scheme. The Lenke scheme was  popular in the past, but has recently been criticized for its complexity leading to intra and inter-observer variability. The resultant decision tree  demonstrated an ability to increase classification accuracy in proportion to the time spent classifying. Importantly, this increase in accuracy was independently of previous knowledge about AIS. In the second study, a surgical strategy rule-based algorithm was developed using rules extracted from the literature to guide surgeons in the selection of the approach and levels of fusion for AIS. When this rule-based algorithm was tested against a database of 1,556 AIS cases, it was able to output a surgical strategy similar to the one undertaken by an expert surgeon in 70% of cases. This study confirmed the ability of a rule-based algorithm based on the literature to output valid surgical strategies.  In the third study, classification of 1,776 AIS patients was undertaken using Kohonen Self-Organizing-Maps (SOM), which is a kind of neural network that demonstrates  there are typical AIS curve types (i.e: single curves and double thoracic curves) for which there is little variability in surgical treatment when compared to the recommendations from the Lenke scheme. Other curve types (i.e: multiple curves or in transition zones between typical curves) have much greater variability in surgical strategy. Finally, a software platform integrating all the above studies was developed. The  interface of this software platform allows for: 1) the input of  AIS patient radiographic measurements; 2) classification of the curve type using the decision tree; 3) output of surgical strategy options based on rules extracted from the literature. A comparison of surgical correction obtained by patients receiving surgical treatment suggested by the software showed a tendency to obtain better balance -though non-statistically significant - than those who were treated differently from the surgical strategies outputted by the software.  Overall, studies from this thesis suggest that the use of AI algorithms in the classification and selection of surgical strategies for AIS can be integrated in a software platform that could assist the surgeon in the planning of appropriate surgical treatment.

Keyphrases: Adolescent idiopathic scoliosis, levels of fusion, approach, artificial intelligence, algorithms, decision trees, rule-based algorithms