Subject-specific Models Research Articles

Group-level brain decoding with deep learning.

Decoding brain imaging data are gaining popularity, with applications in brain-computer interfaces and the study of neural representations. Decoding is typically subject-specific and does not generalise well over subjects, due to high amounts of between subject variability. Techniques that overcome this will not only provide richer neuroscientific insights but also make it possible for group-level models to outperform subject-specific models. Here, we propose a method that uses subject embedding, analogous to word embedding in natural language processing, to learn and exploit the structure in between-subject variability as part of a decoding model, our adaptation of the WaveNet architecture for classification. We apply this to magnetoencephalography data, where 15 subjects viewed 118 different images, with 30 examples per image; to classify images using the entire 1 s window following image presentation. We show that the combination of deep learning and subject embedding is crucial to closing the performance gap between subject- and group-level decoding models. Importantly, group models outperform subject models on low-accuracy subjects (although slightly impair high-accuracy subjects) and can be helpful for initialising subject models. While we have not generally found group-level models to perform better than subject-level models, the performance of group modelling is expected to be even higher with bigger datasets. In order to provide physiological interpretation at the group level, we make use of permutation feature importance. This provides insights into the spatiotemporal and spectral information encoded in the models. All code is available on GitHub (https://github.com/ricsinaruto/MEG-group-decode).

The gait pattern and not the femoral morphology is the main contributor to asymmetric hip joint loading.

Gait asymmetry and skeletal deformities are common in many children with cerebral palsy (CP). Changes of the hip joint loading, i.e. hip joint contact force (HJCF), can lead to pathological femoral growth. A child's gait pattern and femoral morphology affect HJCFs. The twofold aim of this study was to (1) evaluate if the asymmetry in HJCFs is higher in children with CP compared to typically developing (TD) children and (2) identify if the bony morphology or the subject-specific gait pattern is the main contributor to asymmetric HJCFs. Magnetic resonance images (MRI) and three-dimensional gait analysis data of twelve children with CP and fifteen TD children were used to create subject-specific musculoskeletal models and calculate HJCF using OpenSim. Root-mean-square-differences between left and right HJCF magnitude and orientation were computed and compared between participant groups (CP versus TD). Additionally, the influence on HJCF asymmetries solely due to the femoral morphology and solely due to the gait pattern was quantified. Our findings demonstrate that the gait pattern is the main contributor to asymmetric HJCFs in CP and TD children. Children with CP have higher HJCF asymmetries which is probably the result of larger asymmetries in their gait pattern compared to TD children. The gained insights from our study highlight that clinical interventions should focus on normalizing the gait pattern and therefore the hip joint loading to avoid the development of femoral deformities.

Determination of spinal tracer dispersion after intrathecal injection in a deformable CNS model.

Background: Traditionally, there is a widely held belief that drug dispersion after intrathecal (IT) delivery is confined locally near the injection site. We posit that high-volume infusions can overcome this perceived limitation of IT administration. Methods: To test our hypothesis, subject-specific deformable phantom models of the human central nervous system were manufactured so that tracer infusion could be realistically replicated in vitro over the entire physiological range of pulsating cerebrospinal fluid (CSF) amplitudes and frequencies. The distribution of IT injected tracers was studied systematically with high-speed optical methods to determine its dependence on injection parameters (infusion volume, flow rate, and catheter configurations) and natural CSF oscillations in a deformable model of the central nervous system (CNS). Results: Optical imaging analysis of high-volume infusion experiments showed that tracers spread quickly throughout the spinal subarachnoid space, reaching the cervical region in less than 10min. The experimentally observed biodispersion is much slower than suggested by the Taylor-Aris dispersion theory. Our experiments indicate that micro-mixing patterns induced by oscillatory CSF flow around microanatomical features such as nerve roots significantly accelerate solute transport. Strong micro-mixing effects due to anatomical features in the spinal subarachnoid space were found to be active in intrathecal drug administration but were not considered in prior dispersion theories. Their omission explains why prior models developed in the engineering community are poor predictors for IT delivery. Conclusion: Our experiments support the feasibility of targeting large sections of the neuroaxis or brain utilizing high-volume IT injection protocols. The experimental tracer dispersion profiles acquired with an anatomically accurate, deformable, and closed in vitro human CNS analog informed a new predictive model of tracer dispersion as a function of physiological CSF pulsations and adjustable infusion parameters. The ability to predict spatiotemporal dispersion patterns is an essential prerequisite for exploring new indications of IT drug delivery that targets specific regions in the CNS or the brain.

American Society of Biomechanics Journal of Biomechanics Award 2022: Computer models do not accurately predict human muscle passive muscle force and fiber length: Evaluating subject-specific modeling impact on musculoskeletal model predictions

Musculoskeletal models are valuable for studying and understanding the human body in a variety of clinical applications that include surgical planning, injury prevention, and prosthetic design. Subject-specific models have proven to be more accurate and useful compared to generic models. Nevertheless, it is important to validate all models when possible. To this end, gracilis muscle–tendon parameters were directly measured intraoperatively and used to test model predictions. The aim of this study was to evaluate the benefits and limitations of systematically incorporating subject-specific variables into muscle models used to predict passive force and fiber length. The results showed that incorporating subject-specific values generally reduced errors, although significant errors still existed. Optimization of the modeling parameter “tendon slack length” was explored in two cases: minimizing fiber length error and minimizing passive force error. The results showed that using all subject-specific values yielded the most favorable outcome in both models and optimization cases. However, the trade-off between fiber length error and passive force error will depend on the specific circumstances and research objectives due to significant individual errors. Notably, individual fiber length and passive force errors were as high as 20% and 37% respectively. Finally, the modeling parameter “tendon slack length” did not correlate with any real-world anatomical length.

Enhanced external counterpulsation treatment regulates blood flow and wall shear stress metrics in femoral artery: An in vivo study in healthy subjects

As a non-invasive assisted circulation therapy, enhanced external counterpulsation (EECP) has demonstrated potential in treatment of lower-extremity arterial disease (LEAD). However, the underlying hemodynamic mechanism remains unclear. This study aimed to conduct the first prospective investigation of the EECP-induced responses of blood flow behavior and wall shear stress (WSS) metrics in the femoral artery. Twelve healthy male volunteers were enrolled. A Doppler ultrasound-basedapproach was introduced for the in vivo determination of blood flow in the common femoral artery (CFA) and superficial femoral artery (SFA) during EECP intervention, with incremental treatment pressures ranging from 10 to 40 kPa. Three-dimensional subject-specific numerical models were developed in 6 subjects to quantitatively assess variations in WSS-derived hemodynamic metrics in the femoral bifurcation. A mesh-independence analysis was performed. Our results indicated that, compared to the pre-EECP condition, both the antegrade and retrograde blood flow volumes in the CFA and SFA were significantly augmented during EECP intervention, while the heart rate remained constant. The time average shear stress (TAWSS) over the entire femoral bifurcation increased by 32.41%, 121.30%, 178.24%, and 214.81% during EECP with treatment pressures of 10 kPa, 20 kPa, 30 kPa, and 40 kPa, respectively. The mean relative resident time (RRT) decreased by 24.53%, 61.01%, 69.81%, and 77.99%, respectively. The percentage of area with low TAWSS in the femoral artery dropped to nearly zero during EECP with a treatment pressure greater than or equal to 30 kPa. We suggest that EECP is an effective and non-invasive approach for regulating blood flow and WSS in lower extremity arteries.

Automatic generation of subject-specific finite element models of the spine from magnetic resonance images.

The generation of subject-specific finite element models of the spine is generally a time-consuming process based on computed tomography (CT) images, where scanning exposes subjects to harmful radiation. In this study, a method is presented for the automatic generation of spine finite element models using images from a single magnetic resonance (MR) sequence. The thoracic and lumbar spine of eight adult volunteers was imaged using a 3D multi-echo-gradient-echo sagittal MR sequence. A deep-learning method was used to generate synthetic CT images from the MR images. A pre-trained deep-learning network was used for the automatic segmentation of vertebrae from the synthetic CT images. Another deep-learning network was trained for the automatic segmentation of intervertebral discs from the MR images. The automatic segmentations were validated against manual segmentations for two subjects, one with scoliosis, and another with a spine implant. A template mesh of the spine was registered to the segmentations in three steps using a Bayesian coherent point drift algorithm. First, rigid registration was applied on the complete spine. Second, non-rigid registration was used for the individual discs and vertebrae. Third, the complete spine was non-rigidly registered to the individually registered discs and vertebrae. Comparison of the automatic and manual segmentations led to dice-scores of 0.93-0.96 for all vertebrae and discs. The lowest dice-score was in the disc at the height of the implant where artifacts led to under-segmentation. The mean distance between the morphed meshes and the segmentations was below 1mm. In conclusion, the presented method can be used to automatically generate accurate subject-specific spine models.

Influence of stem design parameters on periprosthetic femoral fractures examined by subject specific finite element analyses.

Due to the increasing number of periprosthetic femoral fractures (PFF), the optimisation of implant design gains importance. For the presented research a validated, subject specific finite element model of a human femur with an inlying total hip stem was used to compare the influence of different geometrical implant parameters on the development of PFF. The heterogeneous bone tissue was modelled on the basis of computed tomography scans. A ductile damage model with element deletion was applied to simulate bone fracture in a load case re-enacting a stumbling scenario. The results were compared in terms of fracture load, subsidence and fracture pattern to analyse the influence of friction at the implant-bone interface, implant size and stem length. The results showed that higher friction coefficients lead to an increase of fracture load. Also, the usage of an oversized implant has a negligible effect while an undersized implant reduces the fracture load by 48.9% for the investigated femur. Lastly, a higher fracture load was reached with an elongated stem, but the bending and change in fracture path indicate a more distal force transmission and subsequent stress shielding in the proximal femur.

Prediction of skull fractures in blunt force head traumas using finite element head models.

Traumatic head injuries remain a leading cause of death and disability worldwide. Although skull fractures are one of the most common head injuries, the fundamental mechanics of cranial bone and its impact tolerance are still uncertain. In the present study, a strain-rate-dependent material model for cranial bone has been proposed and implemented in subject-specific Finite Element (FE) head models in order to predict skull fractures in five real-world fall accidents. The subject-specific head models were developed following an established image-registration-based personalization pipeline. Head impact boundary conditions were derived from accident reconstructions using personalized human body models. The simulated fracture lines were compared to those visible in post-mortem CT scans of each subject. In result, the FE models did predict the actual occurrence and extent of skull fractures in all cases. In at least four out of five cases, predicted fracture patterns were comparable to ones from CT scans and autopsy reports. The tensile material model, which was tuned to represent rate-dependent tensile data of cortical skull bone from literature, was able to capture observed linear fractures in blunt indentation loading of a skullcap specimen. The FE model showed to be sensitive to modeling parameters, in particular to the constitutive parameters of the cortical tables. Nevertheless, this study provides a currently lacking strain-rate dependent material model of cranial bone that has the capacity to accurately predict linear fracture patterns. For the first time, a procedure to reconstruct occurrences of skull fractures using computational engineering techniques, capturing the all-in-all fracture initiation, propagation and final pattern, is presented.

Biodynamic Modeling and Analysis of Human-Exoskeleton Interactions During Assisted Manual Handling

The objective of this study was to investigate the effects of a back exoskeleton on joint kinematics and kinetics during assisted manual handling tasks using subject-specific musculoskeletal biodynamic models and model-based analyses. We constructed these musculoskeletal models using OpenSim (Delp et al., 2007), incorporating optical motion capture, ground reaction forces (GRFs) measurements, and humanexoskeleton interactive force input. Our long-term goal is to enable digital modeling and simulation that can aid in the design and development of more effective exoskeletons and safer manual handling practices.

The novel magnesium–titanium hybrid cannulated screws for the treatment of vertical femoral neck fractures: Biomechanical evaluation

BackgroundConventional cannulated screws are commonly used for internal fixation in the treatment of vertical femoral neck fractures. However, the noticeably high rates of undesirable outcomes such as nonunion, malunion, avascular necrosis, and fixation failure still troubled the patients and surgeons. It is urgent to develop new cannulated screws to improve the above clinical problems. The purpose of this study was to design a novel magnesium-titanium hybrid cannulated screw and to further evaluate its biomechanical performance for the treatment of vertical femoral neck fractures. MethodsA novel magnesium-titanium hybrid cannulated screw was designed, and the conventional titanium cannulated screw was also modeled. The finite element models for vertical femoral neck fractures with magnesium-titanium hybrid cannulated screws and conventional cannulated screws were respectively established. The hip joint contact force during walking gait calculated by a subject-specific musculoskeletal multibody dynamics model, was used as loads and boundary conditions for both finite element models. The stress and displacement distributions of the cannulated screws and the femur, the micromotion of the fracture surfaces of the femoral neck, and the overall stiffness were calculated and analyzed using finite element models. The biomechanical performance of the Magnesium–Titanium hybrid cannulated screws was evaluated. ResultsThe maximum stresses of the magnesium-titanium hybrid cannulated screws and the conventional cannulated screws were 451.5 ​MPa and 476.8 ​MPa, respectively. The maximum stresses of the femur with the above different cannulated screws were 140.3 ​MPa and 164.8 ​MPa, respectively. The maximum displacement of the femur with the hybrid cannulated screws was 6.260 ​mm, lower than the femur with the conventional cannulated screws, which was 7.125 ​mm. The tangential micromotions in the two orthogonal directions at the fracture surface of the femoral neck with the magnesium-titanium hybrid cannulated screws were comparable to those with the conventional cannulated screws. The overall stiffness of the magnesium-titanium hybrid cannulated screw system was 490.17 ​N/mm, higher than that of the conventional cannulated screw system, which was 433.92 ​N/mm. ConclusionThe magnesium-titanium hybrid cannulated screw had superior mechanical strength and fixation stability for the treatment of the vertical femoral neck fractures, compared with those of the conventional cannulated screw, indicating that the magnesium-titanium hybrid cannulated screw has great potential as a new fixation strategy in future clinical applications.The translational potential of this article: This study highlights an innovative design of the magnesium-titanium hybrid cannulated screw for the treatment of vertical femoral neck fractures. The novel magnesium-titanium hybrid cannulated screw not only to provide sufficient mechanical strength and fixation stability but also to contribute to the promotion of fracture healing, which could provide a better treatment for the vertical femoral neck fractures, beneficially reducing the incidence of nonunion and reoperation rates.

Knee instability caused by altered graft mechanical properties after anterior cruciate ligament reconstruction: the early onset of osteoarthritis?

Anterior cruciate ligament (ACL) rupture is a very common knee joint injury. Torn ACLs are currently reconstructed using tendon autografts. However, half of the patients develop osteoarthritis (OA) within 10 to 14 years postoperatively. Proposedly, this is caused by altered knee kine(ma)tics originating from changes in graft mechanical properties during the in vivo remodeling response. Therefore, the main aim was to use subject-specific finite element knee models and investigate the influence of decreasing graft stiffness and/or increasing graft laxity on knee kine(ma)tics and cartilage loading. In this research, 4 subject-specific knee geometries were used, and the material properties of the ACL were altered to either match currently used grafts or mimic in vivo graft remodeling, i.e., decreasing graft stiffness and/or increasing graft laxity. The results confirm that the in vivo graft remodeling process increases the knee range of motion, up to >300 percent, and relocates the cartilage contact pressures, up to 4.3mm. The effect of remodeling-induced graft mechanical properties on knee stability exceeded that of graft mechanical properties at the time of surgery. This indicates that altered mechanical properties of ACL grafts, caused by in vivo remodeling, can initiate the early onset of osteoarthritis, as observed in many patients clinically.

Human brain solute transport quantified by glymphatic MRI-informed biophysics during sleep and sleep deprivation.

Whether you are reading, running or sleeping, your brain and its fluid environment continuously interacts to distribute nutrients and clear metabolic waste. Yet, the precise mechanisms for solute transport within the human brain have remained hard to quantify using imaging techniques alone. From multi-modal human brain MRI data sets in sleeping and sleep-deprived subjects, we identify and quantify CSF tracer transport parameters using forward and inverse subject-specific computational modelling. Our findings support the notion that extracellular diffusion alone is not sufficient as a brain-wide tracer transport mechanism. Instead, we show that human MRI observations align well with transport by either by an effective diffusion coefficent 3.5[Formula: see text] that of extracellular diffusion in combination with local clearance rates corresponding to a tracer half-life of up to 5h, or by extracellular diffusion augmented by advection with brain-wide average flow speeds on the order of 1-9 [Formula: see text]m/min. Reduced advection fully explains reduced tracer clearance after sleep-deprivation, supporting the role of sleep and sleep deprivation on human brain clearance.

Sensitivity of subject-specific upper body musculoskeletal model predictions to mass scaling methods

Accurate predictions of spinal loads in subject-specific musculoskeletal models require precise body segment parameters, including segment mass and center of mass (CoM) locations. Existing upper body models often assume a constant percentage of total body mass to calculate segmental masses, disregarding inter-individual variability and limiting their predictive capacity. This study evaluated the sensitivity of subject-specific upper body musculoskeletal model predictions to body mass scaling methods. The upper body segmental masses and corresponding CoM of six male subjects with varying body mass indices were computed using two mass scaling methods: the constant-percentage-based (CPB) scaling method, commonly used in AnyBody software; and our recently developed body-shape-based (BSB) method. Subsequently, these values were used by a validated musculoskeletal model to predict the muscle and disc forces in upright and flexed postures. The discrepancies between the results of the two scaling methods were compared across subjects and postures. Maximum deviations in thorax masses reached up to 7.5% of total body weight (TBW) in overweight subjects, while maximum CoM location differences of up to 35 mm were observed in normal weight subjects. The root mean squared errors (RMSE) of the CPB results, calculated with the BSB results as baseline, showed that the muscle and shear forces of the two scaling methods were quite similar (<4.5% of TBW). Though, there were small to moderate differences in compressive forces (6.5–16.0% of TBW). Thus, the compressive forces predicted with CPB method should be used with caution, particularly for overweight and obese subjects.

A biomechanical evaluation of firefighters’ musculoskeletal loads when carrying self-contained breathing apparatus in walking and running

Introduction: Musculoskeletal loading data are needed to design ergonomic intervention for firefighters. This study aimed to quantify the firefighters’ musculoskeletal loads during self-contained breathing apparatus (SCBA) carriage and evaluate the effectiveness of shoulder strap length variation for the prevention of SCBA-related injuries. Method: Twelve firefighters (height: 174.6 ± 2.4 cm, mass: 67 ± 3.5 kg, BMI = 22 ± 1 kg/m2) participated the walking and running protocols with no SCBA equipped and three varying-strapped SCBAs conditions. Joint range of motion and surface electromyography (sEMG) were synchronously measured. Subsequently, joint kinematics was inputted for subject-specific musculoskeletal modeling to estimate muscle forces and joint reaction forces, while the sEMG was used to validate the model. Repeated measures analysis of variance was used for the main effects (p < 0.05). Independent samples t-test was performed to determine differences between walking and running. Results: Walking with SCBA increased the rectus femoris force and hip reaction force by 34.92% [F = 53.629; p < 0.001; η2 = 0.317] and 34.71% [F = 53.653; p < 0.001; η2 = 0.517], the growth rate was 54.2% [F = 76.487; p < 0.001; η2 = 0.418] and 51.19% [F = 69.201; p < 0.001; η2 = 0.652] during running, respectively. Running with SCBA significantly increased the knee reaction force by 63.04% [F = 83.960; p < 0.001; η2 = 0.797], while only 18.49% increase during walking. Adjusting SCBA shoulder strap length significantly altered the rectus abdominis force and L4/L5 reaction force during walking and running. Conclusions: Results revealed that rectus femoris activity, hip and knee exertion was sensitive to SCBA carriage. The variation of shoulder strap length has potential to influence the risk of low back pain (LBP). Practical applications: The findings suggest that fire services promote targeting physical training at firefighters’ hip and knee regions. Test firefighters in this study were not advisable to adjust their shoulder strap at loose-fitting condition. The compatibility design of the trunk morphology and SCBA back-mounted frame was suggested for the management of LBP.

MRI Derived Simulations of Flow Patterns in the Stomach.

A framework to simulate the flow in the stomach using subject-specific motility patterns and geometries was developed. Dynamic 2D magnetic resonance images (MRIs) were obtained. Motility parameters such as contraction speed and occlusion were quantified, and 3D stomach geometries were reconstructed using a semi-automated approach. Computational fluid dynamics (CFD) simulations were performed, and flow patterns were investigated. The stomach of both subjects had distinct anatomical features with computed volumes of 789 mL and 619 mL. For the one subject, the occlusion (i.e., normalized contraction size) was 12% while it was around 25% for the other subject. Contraction speeds were also different (1.9-2.8 mm/s vs 3.0-5.1 mm/s) for each subject. CFD simulations resulted in unsteady laminar flow for both subjects with average velocities of 2.1 and 3.2 mm/s. While antegrade flow was mainly observed in the simulations, a retropulsive jet was also present in both stomachs. The versatile framework developed within this study would allow the generation of CFD models of gastric motility from dynamic MRIs.Clinical Relevance- Subject-specific models of flow patterns informed by gastric motility features can elucidate the impact of contractions and anatomical variations on digestion. Such models can inform therapies to treat gastric dysfunctions and improve their efficacy.

Haemodynamic effects of hypertension and type 2 diabetes: Insights from a 4D flow MRI-based personalized cardiovascular mathematical model.

Type 2 diabetes (T2D) and hypertension increase the risk of cardiovascular diseases mediated by whole-body changes to metabolism, cardiovascular structure and haemodynamics. The haemodynamic changes related to hypertension and T2D are complex and subject-specific, however, and not fully understood. We aimed to investigate the haemodynamic mechanisms in T2D and hypertension by comparing the haemodynamics between healthy controls and subjects with T2D, hypertension, or both. For all subjects, we combined 4D flow magnetic resonance imaging data, brachial blood pressure and a cardiovascular mathematical model to create a comprehensive subject-specific analysis of central haemodynamics. When comparing the subject-specific haemodynamic parameters between the four groups, the predominant haemodynamic difference is impaired left ventricular relaxation in subjects with both T2D and hypertension compared to subjects with only T2D, only hypertension and controls. The impaired relaxation indicates that, in this cohort, the long-term changes in haemodynamic load of co-existing T2D and hypertension cause diastolic dysfunction demonstrable at rest, whereas either disease on its own does not. However, through subject-specific predictions of impaired relaxation, we show that altered relaxation alone is not enough to explain the subject-specific and group-related differences; instead, a combination of parameters is affected in T2D and hypertension. These results confirm previous studies that reported more adverse effects from the combination of T2D and hypertension compared to either disease on its own. Furthermore, this shows the potential of personalized cardiovascular models in providing haemodynamic mechanistic insights and subject-specific predictions that could aid in the understanding and treatment planning of patients with T2D and hypertension. KEY POINTS: The combination of 4D flow magnetic resonance imaging data and a cardiovascular mathematical model allows for a comprehensive analysis of subject-specific haemodynamic parameters that otherwise cannot be derived non-invasively. Using this combination, we show that diastolic dysfunction in subjects with both type 2 diabetes (T2D) and hypertension is the main group-level difference between controls, subjects with T2D, subjects with hypertension, and subjects with both T2D and hypertension. These results suggest that, in this relatively healthy population, the additional load of both hypertension and T2D affects the haemodynamic function of the left ventricle, whereas each disease on its own is not enough to cause significant effects under resting conditions. Finally, using the subject-specific model, we show that the haemodynamic effects of diastolic dysfunction alone are not sufficient to explain all the observed haemodynamic differences. Instead, additional subject-specific variations in cardiac and vascular function combine to explain the complex haemodynamics of subjects affected by hypertension and/or T2D.

Mechanical upside of PAO mainstream fixations: co-simulation based on early postoperative gait characteristics of DDH patients.

Purpose: To investigate the early postoperative gait characteristics of patients who underwent periacetabular osteotomy (PAO) and predict the biomechanical performance of two commonly used PAO fixation methods: iliac screw (IS) and transverse screw (TS). Methods: A total of 12 patients with unilateral developmental dysplasia of the hip (DDH) (mean age 27.81 ± 4.64years, 42% male) that were scheduled to undergo PAO surgery were included in this study. Their preoperative CT images and pre- and postoperative gait data were used to create subject-specific musculoskeletal models and complete the inverse dynamics analysis (IDA). Two patients with typical gait characteristics were selected using clustering analysis, and their IDA data were incorporated into finite element (FE) models of IS and TS fixations. Failure simulation was performed by applying iterative steps with increasing gait load to predict yield load. Stress results and yield loads were calculated for each FE model at different phases of the gait cycle. Results: Postoperative gait showed improvement compared to preoperative gait but remained inferior to that of healthy individuals. Postoperative gait was characterized by a lower hip range of motion, lower peri-ilium muscle forces, particularly in the abductors, and a sharper initial peak and flatter second peak of hip joint reaction force (HRF). Finite element analysis (FEA) showed a trend of increasing stress during the second-fourth phases of the gait cycle, with lower stress levels in other phases. At high-stress gait phases, the mean stress of maximum differed significantly between IS and TS (p < 0.05) and between coupled and uncoupled muscle forces (p < 0.05). Failure analysis predicted a slightly larger yield load for TS configurations (6.21*BW) than that for IS (6.16*BW), but both were well above the gait load. Coupled and uncoupled groups showed similar results, but uncoupled groups had lower yield loads (5.9*BW). Conclusion: PAO early postoperative gait shows a normalized trend, but abnormalities persist. IS and TS are both capable of resisting mechanical strain failure, with no significant mechanical advantage found for transverse screw fixation during PAO early postoperative gait. Additionally, it is important to note that the TS may have a higher risk of cyclic fatigue failure due to the localized greater stress concentration. Furthermore, the most medial screw is crucial for pelvic stability.

Real-time lumbosacral joint loading estimation in exoskeleton-assisted lifting conditions via electromyography-driven musculoskeletal models

Lumbar joint compression forces have been linked to the development of chronic low back pain, which is specially present in occupational environments. Offline methodologies for lumbosacral joint compression force estimation are not commonly integrated in occupational or medical applications due to the highly time-consuming and complex post-processing procedures. Hence, applications such as real-time adjustment of assistive devices (i.e., back-support exoskeletons) for optimal modulation of compression forces remains unfeasible. Here, we present a real-time electromyography (EMG)-driven musculoskeletal model, capable of estimating accurate lumbosacral joint moments and plausible compression forces. Ten participants performed box-lifting tasks (5 and 15 kg) with and without the Laevo Flex back-support exoskeleton using squat and stoop lifting techniques. Lumbosacral kinematics and EMGs from abdominal and thoracolumbar muscles were used to drive, in real-time, subject-specific EMG-driven models, and estimate lumbosacral joint moments and compression forces. Real-time EMG-model derived moments showed high correlations (R2 = 0.76 - 0.83) and estimation errors below 30% with respect to reference inverse dynamic moments. Compared to unassisted lifting conditions, exoskeleton liftings showed mean lumbosacral joint moments and compression forces reductions of 11.9 - 18.7 Nm (6 - 12% of peak moment) and 300 - 450 N (5 - 10%), respectively. Our modelling framework was capable of estimating in real-time, valid lumbosacral joint moments and compression forces in line with in vivo experimental data, as well as detecting the biomechanical effects of a passive back-support exoskeleton. Our presented technology may lead to a new class of bio-protective robots in which personalized assistance profiles are provided based on subject-specific musculoskeletal variables.

A Morphologically Individualized Deep Learning Brain Injury Model.

The brain injury modeling community has recommended improving model subject specificity and simulation efficiency. Here, we extend an instantaneous (< 1 sec) convolutional neural network (CNN) brain model based on the anisotropic Worcester Head Injury Model (WHIM) V1.0 to account for strain differences due to individual morphological variations. Linear scaling factors relative to the generic WHIM along the three anatomical axes are used as additional CNN inputs. To generate training samples, the WHIM is randomly scaled to pair with augmented head impacts randomly generated from real-world data for simulation. An estimation of voxelized peak maximum principal strain of the whole-brain is said to be successful when the linear regression slope and Pearson's correlation coefficient relative to directly simulated do not deviate from 1.0 (when identical) by more than 0.1. Despite a modest training dataset (N = 1363 vs. ∼5.7 k previously), the individualized CNN achieves a success rate of 86.2% in cross-validation for scaled model responses, and 92.1% for independent generic model testing for impacts considered as complete capture of kinematic events. Using 11 scaled subject-specific models (with scaling factors determined from pre-established regression models based on head dimensions and sex and age information, and notably, without neuroimages), the morphologically individualized CNN remains accurate for impacts that also yield successful estimations for the generic WHIM. The individualized CNN instantly estimates subject-specific and spatially detailed peak strains of the entire brain and thus, supersedes others that report a scalar peak strain value incapable of informing the location of occurrence. This tool could be especially useful for youths and females due to their anticipated greater morphological differences relative to the generic model, even without the need for individual neuroimages. It has potential for a wide range of applications for injury mitigation purposes and the design of head protective gears. The voxelized strains also allow for convenient data sharing and promote collaboration among research groups.

Automated bidirectional coupling of multiscale models of aerosol dosimetry: Validation with subject-specific deposition data

Assessing the toxicity of airborne particulate matter or the efficacy of inhaled drug depends upon accurate estimates of deposited fraction of inhaled materials. In silico approaches can provide important insights into site- or airway-specific deposition of inhaled aerosols in the respiratory system. In this study, we improved on our recently developed 3D/1D model that simulate aerosol transport and deposition in the whole lung over multiple breath cycles (J. Aerosol Sci 151:105647). A subject-specific multiscale lung model of a healthy male subject using computational fluid-particle dynamics (CFPD) in a 3D model of the oral cavity through the large bronchial airways entering each lobe was bidirectionally coupled with a recently improved Multiple Path Particle Dosimetry (MPPD) model to predict aerosol deposition over the entire respiratory tract over multiple breaths for four conditions matching experimental aerosol exposures in the same subject from which the model was developed. These include two particle sizes (1 and 2.9 μm) and two subject-specific breathing rates of ∼300 ml/s (slow breathing) and ∼750 ml/s (fast breathing) at a target tidal volume of 1 L. In silico predictions of retained fraction were 0.31 and 0.29 for 1 μm and 0.66 and 0.62 for 2.9 μm during slow and fast breathing, respectively, and compared well with experimental data (1 μm: 0.31 ± 0.01 (slow) and 0.27 ± 0.01 (fast), 2.9 μm: 0.63 ± 0.03 (slow) and 0.68 ± 0.02 (fast)). These results provide a great deal of confidence in the validity and reliability of our approach.