Evaluation of a Novel Statistical Method for EMG-to-Moment Estimation During Gait

2012 ◽  
Author(s):  
Andrew J. Meyer ◽  
Carolynn Patten ◽  
Benjamin J. Fregly
Keyword(s):  
Neural Control ◽  
Patient Specific ◽  
Modeling Methods ◽  
Moment Estimation ◽  
Neurological Conditions ◽  
Intervention Planning ◽  
Parameter Values ◽  
Improved Methods ◽  
Clinical Problems ◽  
Model Customization

Neuromusculoskeletal models have the potential to improve the design of clinical interventions for disorders such as stroke and osteoarthritis that affect walking function. Application of such models to clinical problems will likely require model customization to the unique anatomical and neurological conditions of each patient. Unfortunately, current modeling methods make model customization to patient data difficult, especially for model parameter values related to muscle-tendon models and musculoskeletal geometry [1]. Improved methods are needed so that patient-specific neural control capabilities and limitations can be easily incorporated into predictive gait optimizations to be used for intervention planning purposes.

scholarly journals Systems biology informed neural networks (SBINN) predict response and novel combinations for PD-1 checkpoint blockade

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Michelle Przedborski ◽  
Munisha Smalley ◽  
Saravanan Thiyagarajan ◽  
Aaron Goldman ◽  
Mohammad Kohandel
Keyword(s):  
Neural Networks ◽  
Systems Biology ◽  
Clinical Data ◽  
Treatment Protocol ◽  
Response Prediction ◽  
Clinical Findings ◽  
Patient Specific ◽  
Patient Response ◽  
Triple Combination Therapy ◽  
Parameter Values

AbstractAnti-PD-1 immunotherapy has recently shown tremendous success for the treatment of several aggressive cancers. However, variability and unpredictability in treatment outcome have been observed, and are thought to be driven by patient-specific biology and interactions of the patient’s immune system with the tumor. Here we develop an integrative systems biology and machine learning approach, built around clinical data, to predict patient response to anti-PD-1 immunotherapy and to improve the response rate. Using this approach, we determine biomarkers of patient response and identify potential mechanisms of drug resistance. We develop systems biology informed neural networks (SBINN) to calculate patient-specific kinetic parameter values and to predict clinical outcome. We show how transfer learning can be leveraged with simulated clinical data to significantly improve the response prediction accuracy of the SBINN. Further, we identify novel drug combinations and optimize the treatment protocol for triple combination therapy consisting of IL-6 inhibition, recombinant IL-12, and anti-PD-1 immunotherapy in order to maximize patient response. We also find unexpected differences in protein expression levels between response phenotypes which complement recent clinical findings. Our approach has the potential to aid in the development of targeted experiments for patient drug screening as well as identify novel therapeutic targets.

A Feature-Based Morphing Methodology for Biological Structures Applied to the Spatial Organization of Cardiomyocytes in the Left Atrium

2011 ◽  
Author(s):  
Alessandro Satriano ◽  
Edward J. Vigmond ◽  
Elena S. Di Martino
Keyword(s):  
Spatial Organization ◽  
Experimental Tests ◽  
Smooth Transition ◽  
Patient Specific ◽  
Clinical Environment ◽  
Individual Values ◽  
Tissue Properties ◽  
Biological Structures ◽  
Critical Issues ◽  
Parameter Values

When complex biological structures are modeled, one of the most critical issues is the assignment of geometrical, mechanical and electrical properties to the meshed surfaces. Properties of interest are commonly obtained from diagnostic imaging, experimental tests or anatomical observation. These parameters are usually lumped into individual values assigned to a specific region after subdividing the structure in sub-regions. This practice simplifies the problem avoiding the cumbersome assignment of parameter values to each element. However, sub-regions may not adequately represent the smooth transition between regions thus resulting in artificial discontinuities. In addition, some parameters, such as for example the organization of cardiomyocytes, which is the objective of our research, may be obtained through destructive tests or through sophisticated methods that can only be performed on a limited number of samples. Or else, data structure obtained for one animal species could be applied on a different species. Furthermore, in a clinical environment the need for fast turnout of patient-specific models would benefit from the assignment of tissue properties in a semi-automatic manner.

scholarly journals Cardiovascular Modeling With Adapted Parametric Inference

2018 ◽  
Vol 62 ◽  
pp. 91-107 ◽  
Author(s):  
Didier Lucor ◽  
Olivier P. Le Maître
Keyword(s):  
Approximation Error ◽  
Surrogate Models ◽  
Patient Specific ◽  
System Response ◽  
Cardiovascular Parameter ◽  
Monte Carlo Algorithms ◽  
Statistical Framework ◽  
Posterior Sampling ◽  
Powerful Approach ◽  
Parameter Values

Computational modeling of the cardiovascular system, promoted by the advance of fluid-structure interaction numerical methods, has made great progress towards the development of patient-specific numerical aids to diagnosis, risk prediction, intervention and clinical treatment. Nevertheless, the reliability of these models is inevitably impacted by rough modeling assumptions. A strong in-tegration of patient-specific data into numerical modeling is therefore needed in order to improve the accuracy of the predictions through the calibration of important physiological parameters. The Bayesian statistical framework to inverse problems is a powerful approach that relies on posterior sampling techniques, such as Markov chain Monte Carlo algorithms. The generation of samples re-quires many evaluations of the cardiovascular parameter-to-observable model. In practice, the use of a full cardiovascular numerical model is prohibitively expensive and a computational strategy based on approximations of the system response, or surrogate models, is needed to perform the data as-similation. As the support of the parameters distribution typically concentrates on a small fraction of the initial prior distribution, a worthy improvement consists in gradually adapting the surrogate model to minimize the approximation error for parameter values corresponding to high posterior den-sity. We introduce a novel numerical pathway to construct a series of polynomial surrogate models, by regression, using samples drawn from a sequence of distributions likely to converge to the posterior distribution. The approach yields substantial gains in efficiency and accuracy over direct prior-based surrogate models, as demonstrated via application to pulse wave velocities identification in a human lower limb arterial network.

Muscle Synergy Constraints Improve Prediction of Knee Contact Force During Gait

2013 ◽  
Author(s):  
Benjamin J. Fregly ◽  
Jonathan P. Walter ◽  
Allison L. Kinney ◽  
Scott A. Banks ◽  
Darryl D. D’Lima ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Neural Control ◽  
Optimization Methods ◽  
Internal Force ◽  
Contact Forces ◽  
Muscle Synergy ◽  
Patient Specific ◽  
Joint Contact ◽  
Specific Muscle ◽  
Clinical Conditions

Knowledge of patient-specific muscle and joint contact forces during activities of daily living could improve the treatment of movement-related disorders (e.g., osteoarthritis, stroke, cerebral palsy, Parkinson’s disease). Unfortunately, it is currently impossible to measure these quantities directly under common clinical conditions, and calculation of these quantities using computer models is limited by the redundant nature of human neural control (i.e., more muscles than theoretically necessary to actuate the available degrees of freedom in the skeleton). Walking is a particularly important task to understand, since loss of mobility is associated with increased morbidity and decreased quality of life [1]. Though numerous musculoskeletal computer modeling studies have used optimization methods to resolve the neural control redundancy problem, these estimates remain unvalidated due to the lack of internal force measurements that can be used for validation purposes.

scholarly journals Vércukor-önellenőrzési adatok intelligens értelmezése

2015 ◽  
Vol 156 (29) ◽  
pp. 1165-1173 ◽  
Author(s):  
Mihály Dió ◽  
Tibor Deutsch ◽  
Tímea Biczók ◽  
Judit Mészáros
Keyword(s):  
Blood Glucose ◽  
Diabetes Management ◽  
Glucose Variability ◽  
Self Monitoring ◽  
Systematic Methodology ◽  
Patients With Diabetes ◽  
Recorded Data ◽  
Improved Methods ◽  
Clinical Problems

Self monitoring of blood glucose is the cornerstone of diabetes management. However, the data obtained by self monitoring of blood glucose have rarely been used with the highest advantage. Few physicians routinely download data from memory-equipped glucose meters and analyse these data systematically at the time of patient visits. There is a need for improved methods for the display and analysis of blood glucose data along with a modular approach for identification of clinical problems. The authors present a systematic methodology for the analysis and interpretation of self monitoring blood glucose data in order to assist the management of patients with diabetes. This approach utilizes the followings 1) overall quality of glycemic control; 2) severity and timing of hypoglycemia and hyperglycemia; 3) variability of blood glucose readings; 4) various temporal patterns extracted from recorded data and 5) adequacy of self monitoring blood glucose data. Based on reliable measures of the quality of glycaemic control and glucose variability, a prioritized problem list is derived along with the probable causes of the detected problems. Finally, problems and their interpretation are used to guide clinicians to choose therepeutic actions and/or recommend behaviour change in order to solve the problems that have been identified. Orv. Hetil., 2015, 156(29), 1165–1173.

Population Pharmacokinetics: Development of a Medical Intensive Care Unit-Specific Gentamicin Dosing Nomogram

1993 ◽  
Vol 27 (2) ◽  
pp. 151-154 ◽  
Author(s):  
Sharon M. Watling ◽  
David F. Kisor
Keyword(s):  
Pharmacokinetic Parameter ◽  
Patient Specific ◽  
Population Pharmacokinetic ◽  
Population Parameter ◽  
Population Pharmacokinetic Parameter ◽  
Actual Patient ◽  
Medical Intensive Care ◽  
Gentamicin Therapy ◽  
Parameter Values ◽  
Dosing Nomogram

Objective This study was designed to develop a population-specific dosing nomogram for gentamicin in medical intensive care unit (MICU) patients using the population pharmacokinetic program nonparametric expectation maximization (NPEM). Design Observational clinical gentamicin dosing data were collected, entered into the USC*PACK database program PASTRX, and downloaded into the population pharmacokinetic program NPEM. NPEM generated population pharmacokinetic parameter values that were used to develop a gentamicin dosing nomogram. The nomogram was tested in the next 15 patients admitted to MICU to determine accuracy. Doses given per the MICU and the Hull-Sarubbi nomograms were compared with doses based on actual patient-specific pharmacokinetic parameter values. Reliability coefficients (intraclass correlation coefficients) were calculated to assess the agreement between observations. Setting Data were gathered from patients receiving gentamicin therapy in the MICU, Presbyterian University Hospital, Pittsburgh. Patients Baseline population pharmacokinetic parameter values were determined in 36 MICU patients receiving gentamicin therapy. Patients with renal failure receiving hemodialysis or another mechanical method of blood clearance or fluid removal were excluded. The population parameter values in the form of a dosing nomogram were then used prospectively to dose gentamicin in 15 patients. Results NPEM generated population parameter values similar to those previously published using the Sawchuk-Zaske method in ICU patients. The mean volume of distribution generated using NPEM was 0.34 ± 0.12 L/kg. The relationship between creatinine clearance (Clcr) and elimination rate constant (Ke) was: Ke = 0.00218 x Clcr + 0.007. The nomogram-derived doses correlated with doses determined by using actual patient-specific pharmacokinetic values (p<0.05). The Hull-Sarubbi derived doses, however, did not correlate with patient-specific doses (p>0.05). Only one patient had a peak concentration <6 mg/L. Two of 15 patients had trough concentrations prior to the first maintenance dose >2 mg/L. Conclusions The use of NPEM to generate population-specific pharmacokinetic parameter values has been previously described. Application of population-specific dosing nomograms can improve initial dosing regimens such that conventional therapeutic concentrations can be achieved early in therapy. This nomogram, however, does not preclude follow-up patient-specific pharmacokinetic analysis.

scholarly journals Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking

2014 ◽  
Vol 136 (2) ◽  
Author(s):  
Jonathan P. Walter ◽  
Allison L. Kinney ◽  
Scott A. Banks ◽  
Darryl D. D'Lima ◽  
Thor F. Besier ◽  
...  
Keyword(s):  
Contact Force ◽  
Contact Forces ◽  
Muscle Model ◽  
Patient Specific ◽  
Force Prediction ◽  
Model Parameter ◽  
Subject Specific ◽  
Rms Error ◽  
Parameter Values ◽  
Shape Tracking

The ability to predict patient-specific joint contact and muscle forces accurately could improve the treatment of walking-related disorders. Muscle synergy analysis, which decomposes a large number of muscle electromyographic (EMG) signals into a small number of synergy control signals, could reduce the dimensionality and thus redundancy of the muscle and contact force prediction process. This study investigated whether use of subject-specific synergy controls can improve optimization prediction of knee contact forces during walking. To generate the predictions, we performed mixed dynamic muscle force optimizations (i.e., inverse skeletal dynamics with forward muscle activation and contraction dynamics) using data collected from a subject implanted with a force-measuring knee replacement. Twelve optimization problems (three cases with four subcases each) that minimized the sum of squares of muscle excitations were formulated to investigate how synergy controls affect knee contact force predictions. The three cases were: (1) Calibrate+Match where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously matched, (2) Precalibrate+Predict where experimental knee contact forces were predicted using precalibrated muscle model parameters values from the first case, and (3) Calibrate+Predict where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously predicted, all while matching inverse dynamic loads at the hip, knee, and ankle. The four subcases used either 44 independent controls or five synergy controls with and without EMG shape tracking. For the Calibrate+Match case, all four subcases closely reproduced the measured medial and lateral knee contact forces (R2 ≥ 0.94, root-mean-square (RMS) error < 66 N), indicating sufficient model fidelity for contact force prediction. For the Precalibrate+Predict and Calibrate+Predict cases, synergy controls yielded better contact force predictions (0.61 < R2 < 0.90, 83 N < RMS error < 161 N) than did independent controls (-0.15 < R2 < 0.79, 124 N < RMS error < 343 N) for corresponding subcases. For independent controls, contact force predictions improved when precalibrated model parameter values or EMG shape tracking was used. For synergy controls, contact force predictions were relatively insensitive to how model parameter values were calibrated, while EMG shape tracking made lateral (but not medial) contact force predictions worse. For the subject and optimization cost function analyzed in this study, use of subject-specific synergy controls improved the accuracy of knee contact force predictions, especially for lateral contact force when EMG shape tracking was omitted, and reduced prediction sensitivity to uncertainties in muscle model parameter values.

scholarly journals Neuromusculoskeletal Model Calibration Significantly Affects Predicted Knee Contact Forces for Walking

2016 ◽  
Vol 138 (8) ◽  
Author(s):  
Gil Serrancolí ◽  
Allison L. Kinney ◽  
Benjamin J. Fregly ◽  
Josep M. Font-Llagunes
Keyword(s):  
Contact Force ◽  
Muscle Fiber ◽  
Model Calibration ◽  
Contact Forces ◽  
Patient Specific ◽  
Muscle Forces ◽  
Model Parameter ◽  
Neuromusculoskeletal Model ◽  
Leg Muscle ◽  
Parameter Values

Though walking impairments are prevalent in society, clinical treatments are often ineffective at restoring lost function. For this reason, researchers have begun to explore the use of patient-specific computational walking models to develop more effective treatments. However, the accuracy with which models can predict internal body forces in muscles and across joints depends on how well relevant model parameter values can be calibrated for the patient. This study investigated how knowledge of internal knee contact forces affects calibration of neuromusculoskeletal model parameter values and subsequent prediction of internal knee contact and leg muscle forces during walking. Model calibration was performed using a novel two-level optimization procedure applied to six normal walking trials from the Fourth Grand Challenge Competition to Predict In Vivo Knee Loads. The outer-level optimization adjusted time-invariant model parameter values to minimize passive muscle forces, reserve actuator moments, and model parameter value changes with (Approach A) and without (Approach B) tracking of experimental knee contact forces. Using the current guess for model parameter values but no knee contact force information, the inner-level optimization predicted time-varying muscle activations that were close to experimental muscle synergy patterns and consistent with the experimental inverse dynamic loads (both approaches). For all the six gait trials, Approach A predicted knee contact forces with high accuracy for both compartments (average correlation coefficient r = 0.99 and root mean square error (RMSE) = 52.6 N medial; average r = 0.95 and RMSE = 56.6 N lateral). In contrast, Approach B overpredicted contact force magnitude for both compartments (average RMSE = 323 N medial and 348 N lateral) and poorly matched contact force shape for the lateral compartment (average r = 0.90 medial and −0.10 lateral). Approach B had statistically higher lateral muscle forces and lateral optimal muscle fiber lengths but lower medial, central, and lateral normalized muscle fiber lengths compared to Approach A. These findings suggest that poorly calibrated model parameter values may be a major factor limiting the ability of neuromusculoskeletal models to predict knee contact and leg muscle forces accurately for walking.

scholarly journals Bayesian Information-Theoretic Calibration of Radiotherapy Sensitivity Parameters for Informing Effective Scanning Protocols in Cancer

2020 ◽  
Vol 9 (10) ◽  
pp. 3208
Author(s):  
Heyrim Cho ◽  
Allison L. Lewis ◽  
Kathleen M. Storey
Keyword(s):  
Data Collection ◽  
Tumor Growth ◽  
Information Gain ◽  
Patient Specific ◽  
Treatment Protocols ◽  
Large Variability ◽  
Maximum Information ◽  
Information Theoretic ◽  
Parameter Values ◽  
Fidelity Model

With new advancements in technology, it is now possible to collect data for a variety of different metrics describing tumor growth, including tumor volume, composition, and vascularity, among others. For any proposed model of tumor growth and treatment, we observe large variability among individual patients’ parameter values, particularly those relating to treatment response; thus, exploiting the use of these various metrics for model calibration can be helpful to infer such patient-specific parameters both accurately and early, so that treatment protocols can be adjusted mid-course for maximum efficacy. However, taking measurements can be costly and invasive, limiting clinicians to a sparse collection schedule. As such, the determination of optimal times and metrics for which to collect data in order to best inform proper treatment protocols could be of great assistance to clinicians. In this investigation, we employ a Bayesian information-theoretic calibration protocol for experimental design in order to identify the optimal times at which to collect data for informing treatment parameters. Within this procedure, data collection times are chosen sequentially to maximize the reduction in parameter uncertainty with each added measurement, ensuring that a budget of n high-fidelity experimental measurements results in maximum information gain about the low-fidelity model parameter values. In addition to investigating the optimal temporal pattern for data collection, we also develop a framework for deciding which metrics should be utilized at each data collection point. We illustrate this framework with a variety of toy examples, each utilizing a radiotherapy treatment regimen. For each scenario, we analyze the dependence of the predictive power of the low-fidelity model upon the measurement budget.

scholarly journals Using In Vivo Cine and 3D Multi-Contrast MRI to Determine Human Atherosclerotic Carotid Artery Material Properties and Circumferential Shrinkage Rate and Their Impact on Stress/Strain Predictions

2012 ◽  
Vol 134 (1) ◽  
Author(s):  
Haofei Liu ◽  
Gador Canton ◽  
Chun Yuan ◽  
Chun Yang ◽  
Kristen Billiar ◽  
...  
Keyword(s):  
Carotid Artery ◽  
Material Properties ◽  
Shrinkage Rate ◽  
Patient Specific ◽  
Stress Strain ◽  
Lipid Core ◽  
Material Stiffness ◽  
Stiff Material ◽  
Parameter Values

In vivo magnetic resonance image (MRI)-based computational models have been introduced to calculate atherosclerotic plaque stress and strain conditions for possible rupture predictions. However, patient-specific vessel material properties are lacking in those models, which affects the accuracy of their stress/strain predictions. A noninvasive approach of combining in vivo Cine MRI, multicontrast 3D MRI, and computational modeling was introduced to quantify patient-specific carotid artery material properties and the circumferential shrinkage rate between vessel in vivo and zero-pressure geometries. In vivo Cine and 3D multicontrast MRI carotid plaque data were acquired from 12 patients after informed consent. For each patient, one nearly-circular slice and an iterative procedure were used to quantify parameter values in the modified Mooney-Rivlin model for the vessel and the vessel circumferential shrinkage rate. A sample artery slice with and without a lipid core and three material parameter sets representing stiff, median, and soft materials from our patient data were used to demonstrate the effect of material stiffness and circumferential shrinkage process on stress/strain predictions. Parameter values of the Mooney-Rivlin models for the 12 patients were quantified. The effective Young’s modulus (YM, unit: kPa) values varied from 137 (soft), 431 (median), to 1435 (stiff), and corresponding circumferential shrinkages were 32%, 12.6%, and 6%, respectively. Using the sample slice without the lipid core, the maximum plaque stress values (unit: kPa) from the soft and median materials were 153.3 and 96.2, which are 67.7% and 5% higher than that (91.4) from the stiff material, while the maximum plaque strain values from the soft and median materials were 0.71 and 0.293, which are about 700% and 230% higher than that (0.089) from the stiff material, respectively. Without circumferential shrinkages, the maximum plaque stress values (unit: kPa) from the soft, median, and stiff models were inflated to 330.7, 159.2, and 103.6, which were 116%, 65%, and 13% higher than those from models with proper shrinkage. The effective Young’s modulus from the 12 human carotid arteries studied varied from 137 kPa to 1435 kPa. The vessel circumferential shrinkage to the zero-pressure condition varied from 6% to 32%. The inclusion of proper shrinkage in models based on in vivo geometry is necessary to avoid over-estimating the stresses and strains by up 100%. Material stiffness had a greater impact on strain (up to 700%) than on stress (up to 70%) predictions. Accurate patient-specific material properties and circumferential shrinkage could considerably improve the accuracy of in vivo MRI-based computational stress/strain predictions.

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