Statistical shape modelling reveals large and distinct subchondral bony differences in osteoarthritic knees

Lynch, Joseph T.; Schneider, M.; Perriman, Diana; Scarvell, Jennie M.; Pickering, Mark; Asikuzzaman, Md.; Galvin, Catherine R.; Besier, Thor F.; Smith, Paul N.

doi:10.1016/j.jbiomech.2019.07.003

Cited by 31 publications

(24 citation statements)

References 44 publications

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“…SSM was performed using the technique described previously. 8,25 An overview of the SSM process is described in Figure 2. Following the setting of the axes, each model was cropped to standard proportions.…”

Section: Ssm Generationmentioning

confidence: 99%

“…Specifically, the OA distal femoral condyles are wider and flatter, there is an expansion of the cartilage plate margins, the intercondylar notch is narrowed, and there is general bony expansion, most notably of the posterior-medial condyle. [6][7][8] Tibial changes include medial plateau flattening and widening, an "elevated" lateral plateau, a reduction in the space between the tibial spines, and an increase in the bony prominence along the medial and posterior perimeter of the plateau. 8,9 The bony changes seen in OA may contribute to the altered kinematics reported in osteoarthritic patients.…”

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confidence: 99%

“…[6][7][8] Tibial changes include medial plateau flattening and widening, an "elevated" lateral plateau, a reduction in the space between the tibial spines, and an increase in the bony prominence along the medial and posterior perimeter of the plateau. 8,9 The bony changes seen in OA may contribute to the altered kinematics reported in osteoarthritic patients. Which of these changes is the most important in terms of influence on joint kinematics is not clear.…”

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confidence: 99%

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Shape is only a weak predictor of deep knee flexion kinematics in healthy and osteoarthritic knees

Lynch

Perriman

Scarvell

et al. 2020

Journal Orthopaedic Research

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Tibiofemoral shape influences knee kinematics but little is known about the effect of shape on deep knee flexion kinematics. The aim of this study was to examine the association between tibiofemoral joint shape and kinematics during deep kneeling in patients with and without osteoarthritis (OA). Sixty‐one healthy participants and 58 patients with end‐stage knee OA received a computed tomography (CT) of their knee. Participants completed full flexion kneeling while being imaged using single‐plane fluoroscopy. Six‐degree‐of‐freedom kinematics were measured by registering a three‐dimensional (3D)‐static CT onto 2D‐dynamic fluoroscopic images. Statistical shape modeling and bivariate functional principal component analysis (bfPCA) were used to describe variability in knee shape and kinematics, respectively. Random‐forest‐regression models were created to test the ability of shape to predict kinematics controlling for body mass index, sex, and group. The first seven modes of the shape model up to three modes of the bfPCAs captured more than 90% of the variation. The ability of the random forest models to predict kinematics from shape was low, with no more than 50% of the variation being explained in any model. Furthermore, prediction errors were high, ranging between 24.2% and 29.4% of the data. Variations in the bony morphology of the tibiofemoral joint were weakly associated with the kinematics of deep knee flexion. The models only explained a small amount of variation in the data with high error rates indicating that additional predictors need to be identified. These results contribute to the clinical understanding of knee kinematics and potentially the expectations placed on high‐flexion total knee replacement design.

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Section: Ssm Generationmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Shape is only a weak predictor of deep knee flexion kinematics in healthy and osteoarthritic knees

Lynch

Perriman

Scarvell

et al. 2020

Journal Orthopaedic Research

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“…Knee bone morphology has been characterised as a function of sex 7,8 , osteoarthritis (OA) [9][10][11][12] , and knee ligament injury 13 . Studies have also quantified cartilage thickness across individuals 5,[14][15][16] and found that cartilage is generally thicker where cartilage stresses are high 6,17 .…”

Section: Introduction (475 Words)mentioning

confidence: 99%

Cartilage Thickness and Bone Shape Variations as a Function of Sex, Height, Body Mass, and Age in Adult Knees.

Schneider

Rooks

Besier

2021

Preprint

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The functional relationship between bone and cartilage is modulated by mechanical factors. Scarce data exist on the relationship between bone shape and the spatial distribution of cartilage thickness. This study has three aims: first, to characterise the coupled variation in knee bone morphology and cartilage thickness distributions in knees with healthy cartilage. The second aim was to investigate this relationship as a function of sex, height, body mass, and age. The third aim was to characterise the morphological differences between males and females. MR images of 51 adult knees (28.4±4.1 years) were obtained from a previous study and used to train a statistical shape model of the femur, tibia, and patella and their cartilages. Five linear regression models were fitted to characterise morphology as a function of sex, height, body mass, and age. A logistic regression classifier was fitted to characterise morphological differences between males and females, and 10-fold cross-validation was performed to evaluate the models’ performance. Our results showed that cartilage thickness and its distribution was coupled to bone morphology, including both size (mode 1) and shape variations (mode 2 onwards). The first three shape modes captured over 90% of the variance and described the overall size, diaphysis size, femoral shaft angle, and corresponding changes to the spatial distribution of the cartilages. These modes were sex-linked (p < .0001, p < .05, p < .01, for modes 1, 2, and 3 respectively) and could classify sex with an accuracy of 94.1% (95% CI [83.8%, 98.8%]). Height was a predictor of joint size (p <. 0001) and diaphysis size (p < .05). Body mass was a predictor of joint size (p < .1) and femoral shaft angle (p < .1). Age was not correlated with any of the modes. This study demonstrated the coupled relationship between bone and cartilage, showing that cartilage is thicker with increased bone size, diaphysis size, and decreased femoral shaft angle. Our findings show that sexual dimorphism is strong in these first three modes, and that bone shape and cartilage thickness at the joint are strongly correlated with height but weakly correlated with mass.

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“…In addition, the formation of osteophytes, especially in case of long-term impingement, makes the bony profiles extremely irregular and the delineation of surface boundaries difficult even for expert radiologists [18]- [20]. Not only the quality of the manual segmentation of lower limb bones is prone to such complexity but also traditional semi-automated algorithms, based on gray histograms, edge detection, region growing and statistical shape models were proven sub-optimal requiring extensive manual post-processing [21]- [24]. As a consequence, due to the limited speed and weak robustness of bone segmentation on CT scans, such techniques have had limited spread hitherto in PSI-based surgical planning for knee arthroplasty.…”

Section: Introductionmentioning

confidence: 99%

Deep 3D Convolutional Networks to Segment Bones Affected by Severe Osteoarthritis in CT Scans for PSI-Based Knee Surgical Planning

et al. 2020

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Statistical shape modelling reveals large and distinct subchondral bony differences in osteoarthritic knees

Cited by 31 publications

References 44 publications

Shape is only a weak predictor of deep knee flexion kinematics in healthy and osteoarthritic knees

Shape is only a weak predictor of deep knee flexion kinematics in healthy and osteoarthritic knees

Cartilage Thickness and Bone Shape Variations as a Function of Sex, Height, Body Mass, and Age in Adult Knees.

Deep 3D Convolutional Networks to Segment Bones Affected by Severe Osteoarthritis in CT Scans for PSI-Based Knee Surgical Planning

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