QCT-based 3D finite element modeling to assess patient-specific hip fracture risk and risk factors.

Early assessment of hip fracture risk may play a critical role in designing preventive mechanisms to reduce the occurrence of hip fracture in geriatric people. The loading direction, clinical, and morphological variables play a vital role in hip fracture. Analyzing the effects of these variables helps predict fractures risk more accurately; thereby suggesting the critical variable that needs to be considered. Hence, this work considered the fall postures by varying the loading direction on the coronal plane (α) and on the transverse plane (ß) along with the clinical variables-age, sex, weight, and bone mineral density, and morphological variables-femoral neck axis length, femoral neck width, femoral neck angle, and true moment arm. The strain distribution obtained via finite element analysis (FEA) shows that the angle of adduction (α) during a fall increases the risk of fracture at the greater trochanter and femoral neck, whereas with an increased angle of rotation (ß) during the fall, the FRI increases by ∼1.35 folds. The statistical analysis of clinical, morphological, and loading variables (αandß) delineates that the consideration of only one variable is not enough to realistically predict the possibility of fracture as the correlation between individual variables and FRI is less than 0.1, even though they are shown to be significant (p<0.01). On the contrary, the correlation (R2=0.48) increases as all variables are considered, suggesting the need for considering different variables fork predicting FRI. However, the effect of each variable is different. While loading, clinical, and morphological variables are considered together, the loading direction on transverse plane (ß) has high significance, and the anatomical variabilities have no significance.

Predicting one-year left ventricular mass index regression following transcatheter aortic valve replacement in patients with severe aortic stenosis: A new era is coming.

Aortic stenosis (AS) is the most common valvular heart disease in the western world, particularly worrisome with an ever-aging population wherein postoperative outcome for aortic valve replacement is strongly related to the timing of surgery in the natural course of disease. Yet, guidelines for therapy planning overlook insightful, quantified measures from medical imaging to educate clinical decisions. Herein, we leverage statistical shape analysis (SSA) techniques combined with customized machine learning methods to extract latent information from segmented left ventricle (LV) shapes. This enabled us to predict left ventricular mass index (LVMI) regression a year after transcatheter aortic valve replacement (TAVR). LVMI regression is an expected phenomena in patients undergone aortic valve replacement reported to be tightly correlated with survival one and five year after the intervention. In brief, LV geometries were extracted from medical images of a cohort of AS patients using deep learning tools, and then analyzed to create a set of statistical shape models (SSMs). Then, the supervised shape features were extracted to feed a support vector regression (SVR) model to predict the LVMI regression. The average accuracy of the predictions was validated against clinical measurements calculating root mean square error and R 2 score which yielded the satisfactory values of 0.28 and 0.67, respectively, on test data. Our work reveals the promising capability of advanced mathematical and bioinformatics approaches such as SSA and machine learning to improve medical output prediction and treatment planning.

Study of the significance of parameters and their interaction on assessing femoral fracture risk by quantitative statistical analysis.

Early assessment of hip fracture helps develop therapeutic and preventive mechanisms that may reduce the occurrence of hip fracture. An accurate assessment of hip fracture risk requires proper consideration of the loads, the physiological and morphological parameters, and the interactions between these parameters. Hence, this study aims at analyzing the significance of parameters and their interactions by conducting a quantitative statistical analysis. A multiple regression model was developed considering different loading directions during a sideways fall (angle [Formula: see text] and [Formula: see text] on the coronal and transverse planes, respectively), age, gender, patient weight, height, and femur morphology as independent parameters and Fracture Risk Index (FRI) as a dependent parameter. Strain-based criteria were used for the calculation of FRI with the maximum principal strain obtained from quantitative computed tomography-based finite element analysis. The statistical result shows that [Formula: see text] [Formula: see text], age [Formula: see text], true moment length [Formula: see text], gender [Formula: see text], FNA [Formula: see text], height [Formula: see text], and FSL [Formula: see text] significantly affect FRI where [Formula: see text] is the most influential parameter. The significance of two-level interaction [Formula: see text] and three-level interaction [Formula: see text] shows that the effect of parameters is dissimilar and depends on other parameters suggesting the variability of FRI from person to person.