ArticleFrontiers in bioengineering and biotechnology2020
Predicting Knee Joint Instability Using a Tibio-Femoral Statistical Shape Model.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 27 citations in OpenAlex.
- Review
- Statistical shape model-based estimation of registration error in computer-assisted total knee arthroplasty.International journal of computer assisted radiology and surgery · 2026Article
- Analysis of leg bone lengths from whole-body dual-energy X-ray absorptiometry in the UK biobank.BJR artificial intelligence · 2026Article
- Patellar and Femoral Bone Morphology Is Associated With Overweight and Sports Participation in Young Adolescents.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2025Article
- Leveraging deep learning for nonlinear shape representation in anatomically parameterized statistical shape models.International journal of computer assisted radiology and surgery · 2025Article
- Multiscale correlations between joint and tissue-specific biomechanics and anatomy in postmortem ovine stifles.Scientific reports · 2025Article
- OpenHands: An Open-Source Statistical Shape Model of the Finger Bones.Annals of biomedical engineering · 2024Article
- Bioinspired Design of 3D-Printed Cellular Metamaterial Prosthetic Liners for Enhanced Comfort and Stability.Biomimetics (Basel, Switzerland) · 2024Article
- Three-dimensional magnetic resonance imaging-based statistical shape analysis and machine learning-based prediction of patellofemoral instability.Scientific reports · 2024Article
- Statistical shape model of the talus bone morphology: A comparison between impinged and nonimpinged ankles.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2023Article
- Can we predict the humerus stem component size required to achieve rotational stability in metaphyseal stability concept?JSES international · 2022Article
- Three-Dimensional Subject-Specific Knee Shape Reconstruction with Asynchronous Fluoroscopy Images Using Statistical Shape Modeling.Frontiers in bioengineering and biotechnology · 2021Article
- Statistical-Shape Prediction of Lower Limb Kinematics During Cycling, Squatting, Lunging, and Stepping-Are Bone Geometry Predictors Helpful?Frontiers in bioengineering and biotechnology · 2021Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Statistical shape models (SSMs) are a well established computational technique to represent the morphological variability spread in a set of matching surfaces by means of compact descriptive quantities, traditionally called "modes of variation" (MoVs). SSMs of bony surfaces have been proposed in biomechanics and orthopedic clinics to investigate the relation between bone shape and joint biomechanics. In this work, an SSM of the tibio-femoral joint has been developed to elucidate the relation between MoVs and bone angular deformities causing knee instability. The SSM was built using 99 bony shapes (distal femur and proximal tibia surfaces obtained from segmented CT scans) of osteoarthritic patients. Hip-knee-ankle (HKA) angle, femoral varus-valgus (FVV) angle, internal-external femoral rotation (IER), tibial varus-valgus (TVV) angles, and tibial slope (TS) were available across the patient set. Discriminant analysis (DA) and logistic regression (LR) classifiers were adopted to underline specific MoVs accounting for knee instability. First, it was found that thirty-four MoVs were enough to describe 95% of the shape variability in the dataset. The most relevant MoVs were the one encoding the height of the femoral and tibial shafts (MoV #2) and the one representing variations of the axial section of the femoral shaft and its bending in the frontal plane (MoV #5). Second, using quadratic DA, the sensitivity results of the classification were very accurate, being all >0.85 (HKA: 0.96, FVV: 0.99, IER: 0.88, TVV: 1, TS: 0.87). The results of the LR classifier were mostly in agreement with DA, confirming statistical significance for MoV #2 (
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