Orthopaedic surgery · 2025
An Innovative Stepwise C-Means Clustering Approach for Classification of Adolescent Idiopathic Scoliosis.
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Gong Jiale, Zhang Zifang, Cheng Yunzhang, Cheng Liang, Dong Yating, Sha Lin, Fan Qin, Chen Jian, Wu Chaomeng, Sui Wenyuan, Zhang Yaqing, Liu Fuyun, Hu Weiming, Wei Wenqing, Yang Junlin
Аннотация
OBJECTIVE: Existing 3D classification systems for scoliosis primarily guide surgical treatment, with limited application in conservative management. This study aims to establish a preliminary 3D classification system for moderate adolescent idiopathic scoliosis patients in China, providing a theoretical foundation for the standardization and automation of conservative treatment plans.
METHODS: Data from 404 adolescent idiopathic scoliosis patients who did not undergo surgery were retrospectively collected from 2022 to 2025. EOS imaging technology was used to perform 3D reconstruction for each patient. The parameters included the 3D centroid coordinates of the vertebrae and vertebral angular displacement. A total of 102 features were extracted per model, and dimensionality reduction yielded 30 final features by the Stacked Autoencoder method. Fuzzy C-means clustering with two classification approaches is used: direct clustering and iterative clustering. Iterative clustering was performed based on coronal plane parameters for initial classification, followed by further clustering. Direct classification involved immediate clustering without further subdivision.
RESULTS: Clustering identified 8 distinct 2D curve types, which were further subdivided into 13 3D subtypes. A comparison of the 13 clusters from direct classification with those obtained from iterative clustering was made using Euclidean and Mahalanobis distances between cluster centers and clinical data. The difference in similarity was higher for direct classification, indicating greater variability.
CONCLUSION: EOS imaging technology combined with Fuzzy C-Means iterative clustering enables a preliminary 3D classification of AIS by capturing more detailed and individualized morphological features. Compared to direct clustering, the iterative method not only improves geometric interpretability but also enhances classification accuracy by better identifying subtle variations in spinal curvature. It further improves specificity, particularly in distinguishing sagittal and axial plane deformities, which are often overlooked in 2D systems. This enhanced resolution provides a stronger basis for developing personalized conservative treatment plans, such as brace design and rehabilitation strategy. Although the proposed method shows promise, further clinical validation is needed to confirm its effectiveness in guiding conservative treatment decisions.
METHODS: Data from 404 adolescent idiopathic scoliosis patients who did not undergo surgery were retrospectively collected from 2022 to 2025. EOS imaging technology was used to perform 3D reconstruction for each patient. The parameters included the 3D centroid coordinates of the vertebrae and vertebral angular displacement. A total of 102 features were extracted per model, and dimensionality reduction yielded 30 final features by the Stacked Autoencoder method. Fuzzy C-means clustering with two classification approaches is used: direct clustering and iterative clustering. Iterative clustering was performed based on coronal plane parameters for initial classification, followed by further clustering. Direct classification involved immediate clustering without further subdivision.
RESULTS: Clustering identified 8 distinct 2D curve types, which were further subdivided into 13 3D subtypes. A comparison of the 13 clusters from direct classification with those obtained from iterative clustering was made using Euclidean and Mahalanobis distances between cluster centers and clinical data. The difference in similarity was higher for direct classification, indicating greater variability.
CONCLUSION: EOS imaging technology combined with Fuzzy C-Means iterative clustering enables a preliminary 3D classification of AIS by capturing more detailed and individualized morphological features. Compared to direct clustering, the iterative method not only improves geometric interpretability but also enhances classification accuracy by better identifying subtle variations in spinal curvature. It further improves specificity, particularly in distinguishing sagittal and axial plane deformities, which are often overlooked in 2D systems. This enhanced resolution provides a stronger basis for developing personalized conservative treatment plans, such as brace design and rehabilitation strategy. Although the proposed method shows promise, further clinical validation is needed to confirm its effectiveness in guiding conservative treatment decisions.