How does the femoral cortex depend on bone shape? A methodology for the joint analysis of surface texture and shape

Computer-aided diagnosis for China-Japan Friendship Hospital classification of necrotic femurs using statistical shape and appearance model based on CT scans

The purpose of this study is to quantify the three-dimensional (3D) structural morphology, bone mineral density (BMD) distribution, and mechanical properties of different China-Japan Friendship Hospital (CJFH) classification types and assist clinicians in classifying necrotic femurs accurately. In this study, 41 cases were classified as types L2 and L3 based on CT images. Then, 3D Statistical Shape and Appearance Models (SSM and SAM) were established, and 80 principal component (PC) modes were extracted from the SSM and SAM as the candidate features. The bone strength of each case was also calculated as the candidate feature using finite element analysis (FEA). Support vector machine (SVM) and Extreme Gradient Boosting (XGBoost) were used to establish 10 machine learning models. Feature selection methods were used to screen the candidate features. The performance of each model was evaluated based on sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve. This resulted in a SVM model for CJFH classification with the performance: accuracy of 87.5%, sensitivity of 85.0%, specificity of 76.0%, and AUC of 94.2%. This study provided effective machine learning models for assisting in diagnosing CJFH types, increasing the objectivity of the diagnosis. They may have great potential for application in clinical assessments of CJFH classification.

Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer's disease

We introduce a novel framework for the classification of functional data supported on nonlinear, and possibly random, manifold domains. The motivating application is the identification of subjects with Alzheimer's disease from their cortical surface geometry and associated cortical thickness map. The proposed model is based upon a reformulation of the classification problem as a regularized multivariate functional linear regression model. This allows us to adopt a direct approach to the estimation of the most discriminant direction while controlling for its complexity with appropriate differential regularization. Our approach does not require prior estimation of the covariance structure of the functional predictors, which is computationally prohibitive in our application setting. We provide a theoretical analysis of the out-of-sample prediction error of the proposed model and explore the finite sample performance in a simulation setting. We apply the proposed method to a pooled dataset from Alzheimer's Disease Neuroimaging Initiative and Parkinson's Progression Markers Initiative. Through this application, we identify discriminant directions that capture both cortical geometric and thickness predictive features of Alzheimer's disease that are consistent with the existing neuroscience literature.

Cortical and Trabecular Bone Deficit in Middle-Aged Men Living with HIV

A significant increase in the risk of hip fracture was observed in middle-aged men living with human immunodeficiency virus (MLWH), almost a decade earlier than those without infection. Data regarding cortical and trabecular bone deficit of hip, an important determinant of bone strength, in MLWH are limited. Quantitative CT was performed in consecutive MLWH aged ≥30 years between November 2017 and October 2018 at Severance Hospital, Seoul, Korea. Volumetric bone mineral density (vBMD) and cortical bone mapping parameters of hip (cortical thickness [CTh], cortical bone vBMD [CBMD], cortical mass surface density [CMSD], endocortical trabecular density [ECTD]) were compared to age-matched and body mass index (BMI)-matched controls (1:2) using a community-based healthy adults cohort. Among 83 MLWH and 166 controls (mean age: 47.2 years; BMI: 23.6 kg/m2 ), MLWH had lower total hip vBMD (280 ± 41 versus 296 ± 41 mg/cm3 ), CMSD (155 versus 160 mg/cm2 ), and ECTD (158 versus 175 mg/cm3 ) than controls that remained robust after adjustment for covariates (adjusted β: total hip vBMD, -18.8; CMSD, -7.3; ECTD, -18.0; p < 0.05 for all). Cortical bone mapping revealed localized deficit of CTh, CBMD, and CMSD in the anterolateral trochanteric region and femoral neck in MLWH compared to controls, with a more extensive ECTD deficit. In MLWH, lower CD4 T-cell count (/100 cells/mm3 decrement) and protease inhibitor (PI)-based regimen (versus non-PI regimen) at the time of antiretroviral treatment initiation were associated with lower total hip vBMD (adjusted β -7.5 for lower CD4 count; -28.3 for PI-based regimen) and CMSD (adjusted β -2.6 for lower CD4 count; -12.7 for PI-based regimen; p < 0.05 for all) after adjustment for covariates including age, BMI, smoking, alcohol use, hepatitis C virus co-infection, tenofovir exposure, and CT scanner types. MLWH had lower hip bone density with cortical and trabecular bone deficit compared to community-dwelling controls. © 2023 American Society for Bone and Mineral Research (ASBMR).

Prediction of femoral strength of elderly men based on quantitative computed tomography images using machine learning

Hip fracture is the most common complication of osteoporosis, and its major contributor is compromised femoral strength. This study aimed to develop practical machine learning models based on clinical quantitative computed tomography (QCT) images for predicting proximal femoral strength. Eighty subjects with entire QCT data of the right hip region were randomly selected from the full MrOS cohorts, and their proximal femoral strengths were calculated by QCT-based finite element analysis (QCT/FEA). A total of 50 parameters of each femur were extracted from QCT images as the candidate predictors of femoral strength, including grayscale distribution, regional cortical bone mapping (CBM) measurements, and geometric parameters. These parameters were simplified by using feature selection and dimensionality reduction. Support vector regression (SVR) was used as the machine learning algorithm to develop the prediction models, and the performance of each SVR model was quantified by the mean squared error (MSE), the coefficient of determination (R² ), the mean bias, and the SD of bias. For feature selection, the best prediction performance of SVR models was achieved by integrating the grayscale value of 30% percentile and specific regional CBM measurements (MSE ≤ 0.016, R² ≥ 0.93); and for dimensionality reduction, the best prediction performance of SVR models was achieved by extracting principal components with eigenvalues greater than 1.0 (MSE ≤ 0.014, R² ≥ 0.93). The femoral strengths predicted from the well-trained SVR models were in good agreement with those derived from QCT/FEA. This study provided effective machine learning models for femoral strength prediction, and they may have great potential in clinical bone health assessments.

Statistical Shape and Appearance Models: Development Towards Improved Osteoporosis Care

PURPOSE OF REVIEW

Statistical models of shape and appearance have increased their popularity since the 1990s and are today highly prevalent in the field of medical image analysis. In this article, we review the recent literature about how statistical models have been applied in the context of osteoporosis and fracture risk estimation.

RECENT FINDINGS

Recent developments have increased their ability to accurately segment bones, as well as to perform 3D reconstruction and classify bone anatomies, all features of high interest in the field of osteoporosis and fragility fractures diagnosis, prevention, and treatment. An increasing number of studies used statistical models to estimate fracture risk in retrospective case-control cohorts, which is a promising step towards future clinical application. All the reviewed application areas made considerable steps forward in the past 5-6 years. Heterogeneities in validation hinder a thorough comparison between the different methods and represent one of the future challenges to be addressed to reach clinical implementation.

Cortical Bone Mapping: Measurement and Statistical Analysis of Localised Skeletal Changes

PURPOSE OF REVIEW

Cortical bone mapping (CBM) is a technique for measuring localised skeletal changes from computed tomography (CT) images. It can provide measurements with accuracy surpassing the underlying imaging resolution. CBM can detect changes in several properties of the cortex, with no prior assumptions about the likely location of said changes. This paper summarises the theory behind CBM, discusses its strengths and limitations, and reviews some studies in which it has been applied.

RECENT FINDINGS

CBM has revealed associations between fracture risk and cortical properties in specific regions of the proximal femur which present feasible therapeutic targets. Analyses of several pharmaceutical and exercise interventions quantify effects that are distinct both in location and in the nature of the micro-architectural changes. CBM has illuminated age-related changes in the proximal femur and has recently been applied to other bones, as well as to the assessment of cartilage. The CBM processing pipeline is designed primarily for large cohort studies. Its main impact thus far has not been in the realm of clinical practice, but rather to improve our fundamental understanding of localised bone structure and changes.