Periodic Reporting for period 1 - HIPSTAR (Modelling trajectories and mechanisms of childhood hip dysplasia)
Berichtszeitraum: 2023-01-01 bis 2025-06-30
With 40 million patients in Europe, osteoarthritis (OA) is the most common chronic and disabling disease. OA is incurable, and symptomatic treatments have limited effect. Therefore, prevention by identifying and targeting asymptomatic persons that develop a clear OA risk factor is sorely needed.
Hip dysplasia is the strongest risk factor for hip OA. Hip dysplasia is a condition of mechanical instability of the hip caused by insufficient coverage of the femoral head (ball) by a shallow or obliquely oriented acetabulum (socket). This results in high cartilage stress, and subsequent hip OA. In Europe we screen for developmental hip dysplasia in infants (prevalence 2%), enabling early treatment. However, we discovered that hip dysplasia can (further) develop during skeletal maturation and thus remains unrecognized. In 1100 Dutch 9-year-old children we found a 6% and 26% prevalence of marked and mild hip dysplasia, respectively.
Hip dysplasia can be influenced until the stage where hip growth plates close at about age 13. HIPSTAR is a novel research program where we will uniquely unravel the mechanisms behind late childhood hip dysplasia in order to pave the way for devising preventive measures that reduce the prevalence of adult hip dysplasia and, thus, hip OA. In a birth cohort of 8000 children (followed from foetal life until adulthood), we will first uniquely built a 5D growth model (3D hip shape, bone density, and growth over time) on how hip shape develops over time from age 2-18 years.
We will gain novel knowledge regarding causal factors of dysplastic growth (based on both 2D shape and 3D shape), and whether there are various phenotypes that have different underlying mechanisms. We also will study how and when dysplastic growth will already impact on the integrity of the young adult joint, and discover very early signs of joint aberration leading to OA. Finally, in a computational model, we will test the influence of loading factors on dysplastic growth mechanistically, and provide detailed information regarding the potential remedial options.
Hip dysplasia is the strongest risk factor for hip OA. Hip dysplasia is a condition of mechanical instability of the hip caused by insufficient coverage of the femoral head (ball) by a shallow or obliquely oriented acetabulum (socket). This results in high cartilage stress, and subsequent hip OA. In Europe we screen for developmental hip dysplasia in infants (prevalence 2%), enabling early treatment. However, we discovered that hip dysplasia can (further) develop during skeletal maturation and thus remains unrecognized. In 1100 Dutch 9-year-old children we found a 6% and 26% prevalence of marked and mild hip dysplasia, respectively.
Hip dysplasia can be influenced until the stage where hip growth plates close at about age 13. HIPSTAR is a novel research program where we will uniquely unravel the mechanisms behind late childhood hip dysplasia in order to pave the way for devising preventive measures that reduce the prevalence of adult hip dysplasia and, thus, hip OA. In a birth cohort of 8000 children (followed from foetal life until adulthood), we will first uniquely built a 5D growth model (3D hip shape, bone density, and growth over time) on how hip shape develops over time from age 2-18 years.
We will gain novel knowledge regarding causal factors of dysplastic growth (based on both 2D shape and 3D shape), and whether there are various phenotypes that have different underlying mechanisms. We also will study how and when dysplastic growth will already impact on the integrity of the young adult joint, and discover very early signs of joint aberration leading to OA. Finally, in a computational model, we will test the influence of loading factors on dysplastic growth mechanistically, and provide detailed information regarding the potential remedial options.
An Automated Search Model (ASM) for the 2D shape has been developed and validated for the 9-,13-, and 17-year-olds, but for the 6-year-old a new ASM had to be created. Here it became obvious that traditional measures of dysplasia using ‘the best fitting circle’ around the femoral head are invalid in this age group given the immaturity of the bones and subsequent non-circular shape of the femoral head. This gave rise to a novel research question on whether the hemispherical acetabulum could be used, rather than the femoral head, to establish the center of rotation of the hip joint (see ‘Novel methodologies’).
The landmark placement and subsequent calculations of morphological measurements (shape) on the DXA images for the 9-13-year-olds have been completed. We also performed the first analyses with regard to the difference in risk factors for hip dysplasia in newborns (DDH) and for hip dysplasia at age 13 years.
One of the first steps in studying 3D shape variations in the hip joint based on medical images is post-processing, particularly the extraction of the anatomical region of interest from the images, known as segmentation. Due to data access challenges in the first phase of the project (~6 months), we used a publicly available dataset instead of starting with MRI scans from the Generation R cohort. This novel pipeline offers several benefits for pediatric medical image processing. Our pipeline demonstrated robust performance in handling growth-related bone changes and addressing image quality variations, including irrelevant objects such as the appearance of a child’s parents' hands on top of their child's hands.
We have recently extended our segmentation pipeline to the semantic segmentation of the pelvis and femurs from MRIs. The strength of our segmentation pipeline lies in its ability to segment MRIs at any age without requiring manually annotated datasets, regardless of the specific age it was trained for. Currently, we are manually segmenting a set of MRIs, including approximately 60 left and right femurs and 30 pelvises, to use as a gold standard for quantitatively evaluating the performance of our segmentation pipeline. Additionally, we are building two 3D Statistical Shape Models (SSMs), one for the pelvis and one for the femur, to study their shape variations across different age groups, specifically 9, 13, and 17 years. Statistical Shape Models (SSMs), one for the pelvis and one for the femur, to study their shape variations across different age groups, specifically 9, 13, and 17 years. In the next phase of the project, we will extend the 3D hip growth models, which represent 3D geometry, to a 5D hip growth model (geometry, density, and time) by leveraging deep-learning-based approaches.
The semiquantitative measurements for joint integrity using the Scoring Hip Osteoarthritis with MRI (SHOMRI) system have been performed in a subset of 576 hips. As it is not feasible to manually score all hips in the dataset, we will build an artificial intelligence (AI)-based model to automatically distinguish between healthy hips and hips with osteoarthritis-related abnormalities (based on SHOMRI scores). We also have developed a model to automatically segment cartilage and bone and extract quantitative measurements for hip dysplasia and joint integrity assessment from pelvic MRI. The algorithm allows for automatic extraction of several morphology measurements including cartilage volume measurements and 3D acetabular coverage of femur.
We have been developing a pipeline to automate the creation of subject-specific finite element models to study the effects of mechanical loads on hip growth. The automation process includes, for instance, the automated determination of muscle attachment points. To achieve this, we have built two 3D Statistical Shape Models (SSMs), one for the pelvis and one for the femur. These models will help identify muscle insertion points for previously unseen individual femurs and pelvises. Another exemplary automation feature is the identification of the region on the femoral head covered by the acetabulum. Additionally, we have implemented a bone remodeling model that relates mechanical loading to changes in the density distributions of relevant bones. We have performed a set of finite element analyses using the publicly available finite element modeling software FEBIO, based on medical images of individuals with and without dysplastic hips. These analyses aim to study the effects of mechanical loading on bone density distributions in both healthy and dysplastic hip cases. Currently, we are post-processing the results of these computational analyses.
The landmark placement and subsequent calculations of morphological measurements (shape) on the DXA images for the 9-13-year-olds have been completed. We also performed the first analyses with regard to the difference in risk factors for hip dysplasia in newborns (DDH) and for hip dysplasia at age 13 years.
One of the first steps in studying 3D shape variations in the hip joint based on medical images is post-processing, particularly the extraction of the anatomical region of interest from the images, known as segmentation. Due to data access challenges in the first phase of the project (~6 months), we used a publicly available dataset instead of starting with MRI scans from the Generation R cohort. This novel pipeline offers several benefits for pediatric medical image processing. Our pipeline demonstrated robust performance in handling growth-related bone changes and addressing image quality variations, including irrelevant objects such as the appearance of a child’s parents' hands on top of their child's hands.
We have recently extended our segmentation pipeline to the semantic segmentation of the pelvis and femurs from MRIs. The strength of our segmentation pipeline lies in its ability to segment MRIs at any age without requiring manually annotated datasets, regardless of the specific age it was trained for. Currently, we are manually segmenting a set of MRIs, including approximately 60 left and right femurs and 30 pelvises, to use as a gold standard for quantitatively evaluating the performance of our segmentation pipeline. Additionally, we are building two 3D Statistical Shape Models (SSMs), one for the pelvis and one for the femur, to study their shape variations across different age groups, specifically 9, 13, and 17 years. Statistical Shape Models (SSMs), one for the pelvis and one for the femur, to study their shape variations across different age groups, specifically 9, 13, and 17 years. In the next phase of the project, we will extend the 3D hip growth models, which represent 3D geometry, to a 5D hip growth model (geometry, density, and time) by leveraging deep-learning-based approaches.
The semiquantitative measurements for joint integrity using the Scoring Hip Osteoarthritis with MRI (SHOMRI) system have been performed in a subset of 576 hips. As it is not feasible to manually score all hips in the dataset, we will build an artificial intelligence (AI)-based model to automatically distinguish between healthy hips and hips with osteoarthritis-related abnormalities (based on SHOMRI scores). We also have developed a model to automatically segment cartilage and bone and extract quantitative measurements for hip dysplasia and joint integrity assessment from pelvic MRI. The algorithm allows for automatic extraction of several morphology measurements including cartilage volume measurements and 3D acetabular coverage of femur.
We have been developing a pipeline to automate the creation of subject-specific finite element models to study the effects of mechanical loads on hip growth. The automation process includes, for instance, the automated determination of muscle attachment points. To achieve this, we have built two 3D Statistical Shape Models (SSMs), one for the pelvis and one for the femur. These models will help identify muscle insertion points for previously unseen individual femurs and pelvises. Another exemplary automation feature is the identification of the region on the femoral head covered by the acetabulum. Additionally, we have implemented a bone remodeling model that relates mechanical loading to changes in the density distributions of relevant bones. We have performed a set of finite element analyses using the publicly available finite element modeling software FEBIO, based on medical images of individuals with and without dysplastic hips. These analyses aim to study the effects of mechanical loading on bone density distributions in both healthy and dysplastic hip cases. Currently, we are post-processing the results of these computational analyses.
The initial results we found with respect to the different phenotypes of hip dysplasia based on difference in risk factors is encouraging with respect to the next ambitions in this project with respect to the research on whether there are various phenotypes that have different underlying mechanisms, and subsequently whether it is possible to interfene on these mechanisms.
Also, the fact that we found that in 5% of the young adults (17 years) the integrity of their hip joint was affected, enables us to investigate whether the dysplasia (and which phenotype) leads to such early OA manifestation in the joint.
Also, the fact that we found that in 5% of the young adults (17 years) the integrity of their hip joint was affected, enables us to investigate whether the dysplasia (and which phenotype) leads to such early OA manifestation in the joint.