ArticleSpine deformity2023
Automated Clustering Technique (ACT) for Early Onset Scoliosis: A preliminary report.
Article in Spine deformity, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 6 citations in OpenAlex.
- Scoliosis Induction in the Porcine Spine Using a Growth-Modulating Posterior Tether: Finite Element Model Development and Calibration.Annals of biomedical engineering · 2026Article
- Algorithmic fairness reporting in artificial intelligence for spinal deformity: a systematic review.Spine deformity · 2026Review
- The application of a functional-anatomical model in pulmonary risk assessment for adolescent idiopathic scoliosis.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Artificial intelligence in early onset scoliosis: a scoping review.Spine deformity · 2026Article
- Advent of Artificial Intelligence in Spine Research: An Updated Perspective.Journal of clinical medicine · 2026Article
- Predicting pulmonary function using thoracic deformity parameters in early onset scoliosis patients.PloS one · 2025Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWhile the C-EOS system helps organize and classify Early Onset Scoliosis (EOS) pathology, it is not data-driven and does not help achieve consensus for surgical treatment. The current study aims to create an automated method to cluster EOS patients based on pre-operative clinical indices.
methodsA total of 1114 EOS patients were used for the study, with the following distribution by etiology: congenital (240), idiopathic (217), neuromuscular (417), syndromic (240). Pre-operative clinical indices used for clustering were age, major curve (Cobb) angle, kyphosis, number of levels involved in a major curve (Cobb angle) and kyphosis along with deformity index (defined as the ratio of major Cobb angle and kyphosis). Fuzzy C-means clustering was performed for each etiology individually, with one-way ANOVA performed to assess statistical significance (p < 0.05).
resultsThe automated clustering method resulted in three clusters per etiology as the optimal number based on the highest average membership values. Statistical analyses showed that the clusters were significantly different for all the clinical indices within and between etiologies. Link to the ACT-EOS web application: https://biomed.drexel.edu/labs/obl/toolkits/act-eos-application .
conclusionAn automated method to cluster EOS patients based on pre-operative clinical indices was developed identifying three unique, data-driven subgroups for each C-EOS etiology category. Adoption of such an automated clustering framework can help improve the standardization of clinical decision-making for EOS.
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