Abstract
Purpose
This study applied a machine learning semi-supervised clustering approach to radiographs of adolescent sagittal spines from a single pediatric institution to identify patterns of sagittal alignment in the normal adolescent spine. We sought to explore the inherent variability found in adolescent sagittal alignment using machine learning to remove bias and determine whether clusters of sagittal alignment exist.
Methods
Multiple semi-supervised machine learning clustering algorithms were applied to 111 normal adolescent sagittal spines. Sagittal parameters for resultant clusters were determined.
Results
Machine learning analysis found that the spines did cluster into distinct groups with an optimal number of clusters ranging from 3 to 5. We performed an analysis on both 3 and 5-cluster groups. The 3-cluster groups analysis found good consistency between methods with 96 of 111, while the analysis of 5-cluster groups found consistency with 105 of 111 spines. When assessing for differences in sagittal parameters between the groups for both analyses, there were differences in T4-12 TK, L1-S1 LL, SS, SVA, PI-LL mismatch, and TPA. However, the only parameter that was statistically different for all groups was SVA.
Conclusions
Based on machine learning, the adolescent sagittal spine alignments do cluster into distinct groups. While there were distinguishing features with TK and LL, the most important parameter distinguishing these groups was SVA. Further studies may help to understand these findings in relation to spinal deformities.
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The data that support the findings of this study are available upon reasonable request from the authors.
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Funding
Benny Dahl receives consulting fees from Stryker and is supported by the Alfred Benzon Foundation. Lorenzo Deveza has ownership interests with Lento Medical, Inc. The other authors have no financial interests to report.
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Dion Birhiray: Study design, data collection, image labeling, machine-learning analysis, manuscript preparation. Srikhar V. Chilukuri: data collection, data interpretation, manuscript preparation, revisions, and approval. Caleb Witsken: Data collection, data interpretation, manuscript preparation, revisions, and approval. Maggie Wang: Data collection, data analysis, data interpretation, manuscript preparation, revisions, and approval. Jacob Scioscia: Data collection, data analysis, data interpretation, manuscript preparation, revisions, and approval. Martin Gehrchen: Study design, data interpretation, manuscript review and approval. Lorenzo Deveza: Study design, data collection, image labeling, machine-learning analysis, manuscript preparation and approval, Co-Principal Investigator. Benny Dahl: Study design, manuscript review and approval, Principal Investigator.
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Birhiray, D.G., Chilukuri, S.V., Witsken, C.C. et al. Machine learning identifies clusters of the normal adolescent spine based on sagittal balance. Spine Deform (2024). https://doi.org/10.1007/s43390-024-00952-6
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DOI: https://doi.org/10.1007/s43390-024-00952-6