Morphological variation in paediatric lower limb bones

• Published in: Scientific reports 2022-02, Vol.12 (1), p.3251-3251, Article 3251
• Main Authors: Carman, Laura, Besier, Thor F., Choisne, Julie
• Format: Article
• Language: English
• Subjects: 631/114; 631/443/63; 631/443/7; Adult; Bone growth; Child; Computed tomography; Femur; Femur - diagnostic imaging; Fibula; Geometry; Humanities and Social Sciences; Humans; Lower Extremity; Medical imaging; multidisciplinary; Pediatrics; Pelvis; Pelvis - diagnostic imaging; Principal components analysis; Radiography; Scaling; Science; Science (multidisciplinary); Tibia; Tibia - anatomy & histology; Tibia - diagnostic imaging; Tomography, X-Ray Computed; Variation
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-07267-4

Available methods for generating paediatric musculoskeletal geometry are to scale generic adult geometry, which is widely accessible but can be inaccurate, or to obtain geometry from medical imaging, which is accurate but time-consuming and costly. A population-based shape model is required to generate accurate and accessible musculoskeletal geometry in a paediatric population. The pelvis, femur, and tibia/fibula were segmented from 333 CT scans of children aged 4–18 years. Bone morphology variation was captured using principal component analysis (PCA). Subsequently, a shape model was developed to predict bone geometry from demographic and linear bone measurements and validated using a leave one out analysis. The shape model was compared to linear scaling of adult and paediatric bone geometry. The PCA captured growth-related changes in bone geometry. The shape model predicted bone geometry with root mean squared error (RMSE) of 2.91 ± 0.99 mm in the pelvis, 2.01 ± 0.62 mm in the femur, and 1.85 ± 0.54 mm in the tibia/fibula. Linear scaling of an adult mesh produced RMSE of 4.79 ± 1.39 mm in the pelvis, 4.38 ± 0.72 mm in the femur, and 4.39 ± 0.86 mm in the tibia/fibula. We have developed a method for capturing and predicting lower limb bone shape variation in a paediatric population more accurately than linear scaling without using medical imaging.