Anatomy-Aware Inference of the 3D Standing Spine Posture from 2D Radiographs

An important factor for the development of spinal degeneration, pain and the outcome of spinal surgery is known to be the balance of the spine. It must be analyzed in an upright, standing position to ensure physiological loading conditions and visualize load-dependent deformations. Despite the complex 3D shape of the spine, this analysis is currently performed using 2D radiographs, as all frequently used 3D imaging techniques require the patient to be scanned in a prone position. To overcome this limitation, we propose a deep neural network to reconstruct the 3D spinal pose in an upright standing position, loaded naturally. Specifically, we propose a novel neural network architecture, which takes orthogonal 2D radiographs and infers the spine’s 3D posture using vertebral shape priors. In this work, we define vertebral shape priors using an atlas and a spine shape prior, incorporating both into our proposed network architecture. We validate our architecture on digitally reconstructed radiographs, achieving a 3D reconstruction Dice of 0.95, indicating an almost perfect 2D-to-3D domain translation. Validating the reconstruction accuracy of a 3D standing spine on real data is infeasible due to the lack of a valid ground truth. Hence, we design a novel experiment for this purpose, using an orientation invariant distance metric, to evaluate our model’s ability to synthesize full-3D, upright, and patient-specific spine models. We compare the synthesized spine shapes from clinical upright standing radiographs to the same patient’s 3D spinal posture in the prone position from CT

Application of various robust techniques to study and evaluate the role of effective parameters on rock fragmentation

In this paper, an attempt has been made to implement various robust techniques to predict rock fragmentation due to blasting in open pit mines using effective parameters. As rock fragmentation prediction is very complex and complicated, and due to that various artificial intelligence-based techniques, such as artificial neural network (ANN), classification and regression tree and support vector machines were selected for the modeling. To validate and compare the prediction results, conventional multivariate regression analysis was also utilized on the same data sets. Since accuracy and generality of the modeling is dependent on the number of inputs, it was tried to collect enough required information from four different open pit mines of Iran. According to the obtained results, it was revealed that ANN with a determination coefficient of 0.986 is the most precise method of modeling as compared to the other applied techniques. Also, based on the performed sensitivity analysis, it was observed that the most prevailing parameters on the rock fragmentation are rock quality designation, Schmidt hardness value, mean in-situ block size and the minimum effective ones are hole diameter, burden and spacing. The advantage of back propagation neural network technique for using in this study compared to other soft computing methods is that they are able to describe complex and nonlinear multivariable problems in a transparent way. Furthermore, ANN can be used as a first approach, where much knowledge about the influencing parameters are missing. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature

Health-related quality of life and disease activity in rheumatoid arthritis

Background: The present study sought to 1) investigate the degrees of correlations between different disease activity scores (DASs) and health-related quality of life (HRQoL), and 2) determine if DASs correlate with either physical or mental HRQoL. Methods: Eighty patients with rheumatoid arthritis (RA) were assessed for different DASs, measured with erythrocyte sedimentation rate (ESR) or C-reactive protein (CRP), namely DAS4-ESR, DAS-3 ESR, DAS4-CRP, DAS3-CRP, DAS4-28 ESR, DAS3-28 ESR, DAS4-28 CRP, and DAS3-28 CRP, and Simplified Disease Activity Indexes namely SDAI-ESR, and SDAI-CRP. Physical and mental HRQoL were measured using the SF-36. The Pearson correlation test was employed to examine the correlations between HRQoL and different DAS indices. PASS 2000 (Power Analysis and Sample Size) software was utilized to find significant differences between the correlations. Results: SF-36 total score showed a significant inverse correlation with the DAS4-ESR, DAS-3 ESR, DAS4-CRP, DAS3-CRP, DAS4-28 ESR, DAS3-28 ESR, DAS4-28 CRP, and DAS3-28 CRP, with correlation coefficients of -0.320, -0.314, -0.330, -0.323, -0.327, -0.318, -0.360 and -0.348, respectively (P < 0.01 for all). The correlation coefficients between different DAS indices and the HRQoL score were not significantly different. In addition, all DASs showed significant correlations with physical HRQoL, but not with mental HRQoL. Conclusions: Among patients with RA, disease severity indices are associated with physical, but not mental HRQoL. However this study failed to show any differences between various DASs in their clinical use