AI Insight
Researchers developed MiniMAP, an automated pipeline that generates personalized musculoskeletal models of pediatric lower limbs using statistical shape models derived from CT scans of over 300 children aged 4-18 years. The system predicts bone geometries from demographic data, bone measurements, and 12 skin surface landmarks, achieving high accuracy with average bone surface errors of 2.64mm for pelvis, 1.91mm for femora, and 1.69mm for tibiae/fibulae. A random forest algorithm was developed to account for skin padding (distance from bone to skin surface), which varied considerably across anatomical locations from 3.4mm to 25.9mm on average.
Why it matters
This automated tool enables rapid creation of accurate, customized musculoskeletal models for children without requiring expensive and radiation-intensive medical imaging for each patient. The methodology could improve clinical gait analysis, surgical planning, and biomechanical research in pediatric populations where individualized models are currently difficult to obtain.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
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The generation of paediatric musculoskeletal models often requires compromise between data availability, accuracy, and computation time. Statistical shape models allow for rapid generation of bone geometries for use in musculoskeletal modelling. An articulated shape model allows for movement at the joints during optimisation to better fit to experimental data. Here we present a methodology for generating custom paediatric OpenSim models using an articulated shape model of the lower limb generated from CT scans of over 300 children aged 4 to 18 years old. Bone geometries are predicted using demographic factors (age, height, mass, and sex), linear bone measurements (bone widths and lengths), and 12 skin surface landmarks (L/R ASIS, L/R PSIS, medial/lateral epicondyles, and medial/lateral malleoli). To account for skin padding (bone to skin surface distance) a random forest algorithm was developed for skin padding prediction for these 12 landmarks. Accuracy of the model prediction was assessed using a leave-one-out analysis to assess landmark errors, bone surface errors, and clinical bone measurement errors (linear measurements and angular values). Motion capture and MRI data were collected from two paediatric test cases to test the workflow on unseen data. Skin padding values varied widely in this population with average values from 3.4mm at the lateral malleolus to 25.9mm at the PSIS. Skin padding was best predicted using age, height, mass, ASIS width, epicondylar width, and malleolar width with an average RMSE of 4.68mm. The articulated shape model predicted accurate bone geometries from skin surface landmarks with average bone surface RMSEs of 2.64 {+/-} 0.99mm, 1.91 {+/-} 0.59mm, and 1.69 {+/-} 0.56mm in the pelvis, femora, and tibiae/fibulae respectively. In the two unseen test cases, average bone surface errors ranged from 1.64 {+/-} 1.2mm to 3.41 {+/-} 3.1mm. This study demonstrated the accuracy of "MiniMAP", for the rapid generation of custom paediatric musculoskeletal models.
Source: MiniMAP: An automatic pipeline for paediatric lower limb musculoskeletal model generation