Interpretable Deep-Learning Approaches for Osteoporosis Risk Screening and Individualized Feature Analysis Using Large Population-Based Data: Model Development and Performance Evaluation.

BACKGROUND: Osteoporosis is one of the diseases that requires early screening and detection for its management. Common clinical tools and machine-learning (ML) models for screening osteoporosis have been developed, but they show limitations such as low accuracy. Moreover, these methods are confined to limited risk factors and lack individualized explanation. OBJECTIVE: The aim of this study was to develop an interpretable deep-learning (DL) model for osteoporosis risk screening with clinical features. Clinical interpretation with individual explanations of feature contributions is provided using an explainable artificial intelligence (XAI) technique. METHODS: We used two separate data sets: the National Health and Nutrition Examination Survey data sets from the United States (NHANES) and South Korea (KNHANES) with 8274 and 8680 respondents, respectively. The study population was classified according to the T-score of bone mineral density at the femoral neck or total femur. A DL model for osteoporosis diagnosis was trained on the data sets and significant risk factors were investigated with local interpretable model-agnostic explanations (LIME). The performance of the DL model was compared with that of ML models and conventional clinical tools. Additionally, contribution ranking of risk factors and individualized explanation of feature contribution were examined. RESULTS: Our DL model showed area under the curve (AUC) values of 0.851 (95% CI 0.844-0.858) and 0.922 (95% CI 0.916-0.928) for the femoral neck and total femur bone mineral density, respectively, using the NHANES data set. The corresponding AUC values for the KNHANES data set were 0.827 (95% CI 0.821-0.833) and 0.912 (95% CI 0.898-0.927), respectively. Through the LIME method, significant features were induced, and each feature's integrated contribution and interpretation for individual risk were determined. CONCLUSIONS: The developed DL model significantly outperforms conventional ML models and clinical tools. Our XAI model produces high-ranked features along with the integrated contributions of each feature, which facilitates the interpretation of individual risk. In summary, our interpretable model for osteoporosis risk screening outperformed state-of-the-art methods.

A warning system for urolithiasis via retrograde intrarenal surgery using machine learning: an experimental study.

BACKGROUND: To develop a warning system that can prevent or minimize laser exposure resulting in kidney and ureter damage during retrograde intrarenal surgery (RIRS) for urolithiasis. Our study builds on the hypothesis that shock waves of different degrees are delivered to the hand of the surgeon depending on whether the laser hits the stone or tissue. METHODS: A surgical environment was simulated for RIRS by filling the body of a raw whole chicken with water and stones from the human body. We developed an acceleration measurement system that recorded the power signal data for a number of hours, yielding distinguishable characteristics among three different states (idle state, stones, and tissue-laser interface) by conducting fast Fourier transform (FFT) analysis. A discrete wavelet transform (DWT) was used for feature extraction, and a random forest classification algorithm was applied to classify the current state of the laser-tissue interface. RESULTS: The result of the FFT showed that the magnitude spectrum is different within the frequency range of < 2500 Hz, indicating that the different states are distinguishable. Each recorded signal was cut in only 0.5-s increments and transformed using the DWT. The transformed data were entered into a random forest classifier to train the model. The test result was only measured with the dataset that was isolated from the training dataset. The maximum average test accuracy was > 95%. The procedure was repeated with random signal dummy data, resulting in an average accuracy of 33.33% and proving that the proposed method caused no bias. CONCLUSIONS: Our monitoring system receives the shockwave signals generated from the RIRS urolithiasis treatment procedure and generates the laser irradiance status by rapidly recognizing (in 0.5 s) the current laser exposure state with high accuracy (95%). We postulate that this can significantly minimize surgeon error during RIRS.

Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks.

BACKGROUND: Despite the integral role of cephalometric analysis in orthodontics, there have been limitations regarding the reliability, accuracy, etc. of cephalometric landmarks tracing. Attempts on developing automatic plotting systems have continuously been made but they are insufficient for clinical applications due to low reliability of specific landmarks. In this study, we aimed to develop a novel framework for locating cephalometric landmarks with confidence regions using Bayesian Convolutional Neural Networks (BCNN). METHODS: We have trained our model with the dataset from the ISBI 2015 grand challenge in dental X-ray image analysis. The overall algorithm consisted of a region of interest (ROI) extraction of landmarks and landmarks estimation considering uncertainty. Prediction data produced from the Bayesian model has been dealt with post-processing methods with respect to pixel probabilities and uncertainties. RESULTS: Our framework showed a mean landmark error (LE) of 1.53 ± 1.74 mm and achieved a successful detection rate (SDR) of 82.11, 92.28 and 95.95%, respectively, in the 2, 3, and 4 mm range. Especially, the most erroneous point in preceding studies, Gonion, reduced nearly halves of its error compared to the others. Additionally, our results demonstrated significantly higher performance in identifying anatomical abnormalities. By providing confidence regions (95%) that consider uncertainty, our framework can provide clinical convenience and contribute to making better decisions. CONCLUSION: Our framework provides cephalometric landmarks and their confidence regions, which could be used as a computer-aided diagnosis tool and education.

Selecting Sensitive Parameter Subsets in Dynamical Models With Application to Biomechanical System Identification.

Estimating many parameters of biomechanical systems with limited data may achieve good fit but may also increase 95% confidence intervals in parameter estimates. This results in poor identifiability in the estimation problem. Therefore, we propose a novel method to select sensitive biomechanical model parameters that should be estimated, while fixing the remaining parameters to values obtained from preliminary estimation. Our method relies on identifying the parameters to which the measurement output is most sensitive. The proposed method is based on the Fisher information matrix (FIM). It was compared against the nonlinear least absolute shrinkage and selection operator (LASSO) method to guide modelers on the pros and cons of our FIM method. We present an application identifying a biomechanical parametric model of a head position-tracking task for ten human subjects. Using measured data, our method (1) reduced model complexity by only requiring five out of twelve parameters to be estimated, (2) significantly reduced parameter 95% confidence intervals by up to 89% of the original confidence interval, (3) maintained goodness of fit measured by variance accounted for (VAF) at 82%, (4) reduced computation time, where our FIM method was 164 times faster than the LASSO method, and (5) selected similar sensitive parameters to the LASSO method, where three out of five selected sensitive parameters were shared by FIM and LASSO methods.

Fully Bayesian Prediction Algorithms for Mobile Robotic Sensors under Uncertain Localization Using Gaussian Markov Random Fields.

In this paper, we present algorithms for predicting a spatio-temporal random field measured by mobile robotic sensors under uncertainties in localization and measurements. The spatio-temporal field of interest is modeled by a sum of a time-varying mean function and a Gaussian Markov random field (GMRF) with unknown hyperparameters. We first derive the exact Bayesian solution to the problem of computing the predictive inference of the random field, taking into account observations, uncertain hyperparameters, measurement noise, and uncertain localization in a fully Bayesian point of view. We show that the exact solution for uncertain localization is not scalable as the number of observations increases. To cope with this exponentially increasing complexity and to be usable for mobile sensor networks with limited resources, we propose a scalable approximation with a controllable trade-off between approximation error and complexity to the exact solution. The effectiveness of the proposed algorithms is demonstrated by simulation and experimental results.

Prior Distributions of Material Parameters for Bayesian Calibration of Growth and Remodeling Computational Model of Abdominal Aortic Wall.

For the accurate prediction of the vascular disease progression, there is a crucial need for developing a systematic tool aimed toward patient-specific modeling. Considering the interpatient variations, a prior distribution of model parameters has a strong influence on computational results for arterial mechanics. One crucial step toward patient-specific computational modeling is to identify parameters of prior distributions that reflect existing knowledge. In this paper, we present a new systematic method to estimate the prior distribution for the parameters of a constrained mixture model using previous biaxial tests of healthy abdominal aortas (AAs). We investigate the correlation between the estimated parameters for each constituent and the patient's age and gender; however, the results indicate that the parameters are correlated with age only. The parameters are classified into two groups: Group-I in which the parameters ce, ck1, ck2, cm2,Ghc, and ϕe are correlated with age, and Group-II in which the parameters cm1, Ghm, G1e, G2e, and α are not correlated with age. For the parameters in Group-I, we used regression associated with age via linear or inverse relations, in which their prior distributions provide conditional distributions with confidence intervals. For Group-II, the parameter estimated values were subjected to multiple transformations and chosen if the transformed data had a better fit to the normal distribution than the original. This information improves the prior distribution of a subject-specific model by specifying parameters that are correlated with age and their transformed distributions. Therefore, this study is a necessary first step in our group's approach toward a Bayesian calibration of an aortic model. The results from this study will be used as the prior information necessary for the initialization of Bayesian calibration of a computational model for future applications.

Computational Growth and Remodeling of Abdominal Aortic Aneurysms Constrained by the Spine.

Abdominal aortic aneurysms (AAAs) evolve over time, and the vertebral column, which acts as an external barrier, affects their biomechanical properties. Mechanical interaction between AAAs and the spine is believed to alter the geometry, wall stress distribution, and blood flow, although the degree of this interaction may depend on AAAs specific configurations. In this study, we use a growth and remodeling (G&R) model, which is able to trace alterations of the geometry, thus allowing us to computationally investigate the effect of the spine for progression of the AAA. Medical image-based geometry of an aorta is constructed along with the spine surface, which is incorporated into the computational model as a cloud of points. The G&R simulation is initiated by local elastin degradation with different spatial distributions. The AAA-spine interaction is accounted for using a penalty method when the AAA surface meets the spine surface. The simulation results show that, while the radial growth of the AAA wall is prevented on the posterior side due to the spine acting as a constraint, the AAA expands faster on the anterior side, leading to higher curvature and asymmetry in the AAA configuration compared to the simulation excluding the spine. Accordingly, the AAA wall stress increases on the lateral, posterolateral, and the shoulder regions of the anterior side due to the AAA-spine contact. In addition, more collagen is deposited on the regions with a maximum diameter. We show that an image-based computational G&R model not only enhances the prediction of the geometry, wall stress, and strength distributions of AAAs but also provides a framework to account for the interactions between an enlarging AAA and the spine for a better rupture potential assessment and management of AAA patients.

Solutions to the Inverse LQR Problem with Application to Biological Systems Analysis.

In this paper, we present a set of techniques for finding a cost function to the time-invariant Linear Quadratic Regulator (LQR) problem in both continuous- and discrete-time cases. Our methodology is based on the solution to the inverse LQR problem, which can be stated as: does a given controller K describe the solution to a time-invariant LQR problem, and if so, what weights Q and R produce K as the optimal solution? Our motivation for investigating this problem is the analysis of motion goals in biological systems. We first describe an efficient Linear Matrix Inequality (LMI) method for determining a solution to the general case of this inverse LQR problem when both the weighting matrices Q and R are unknown. Our first LMI-based formulation provides a unique solution when it is feasible. Additionally, we propose a gradient-based, least-squares minimization method that can be applied to approximate a solution in cases when the LMIs are infeasible. This new method is very useful in practice since the estimated gain matrix K from the noisy experimental data could be perturbed by the estimation error, which may result in the infeasibility of the LMIs. We also provide an LMI minimization problem to find a good initial point for the minimization using the proposed gradient descent algorithm. We then provide a set of examples to illustrate how to apply our approaches to several different types of problems. An important result is the application of the technique to human subject posture control when seated on a moving robot. Results show that we can recover a cost function which may provide a useful insight on the human motor control goal.

Time-Domain Optimal Experimental Design in Human Seated Postural Control Testing.

We are developing a series of systems science-based clinical tools that will assist in modeling, diagnosing, and quantifying postural control deficits in human subjects. In line with this goal, we have designed and constructed a seated balance device and associated experimental task for identification of the human seated postural control system. In this work, we present a quadratic programming (QP) technique for optimizing a time-domain experimental input signal for this device. The goal of this optimization is to maximize the information present in the experiment, and therefore its ability to produce accurate estimates of several desired seated postural control parameters. To achieve this, we formulate the problem as a nonconvex QP and attempt to locally maximize a measure (T-optimality condition) of the experiment's Fisher information matrix (FIM) under several constraints. These constraints include limits on the input amplitude, physiological output magnitude, subject control amplitude, and input signal autocorrelation. Because the autocorrelation constraint takes the form of a quadratic constraint (QC), we replace it with a conservative linear relaxation about a nominal point, which is iteratively updated during the course of optimization. We show that this iterative descent algorithm generates a convergent suboptimal solution that guarantees monotonic nonincreasing of the cost function value while satisfying all constraints during iterations. Finally, we present successful experimental results using an optimized input sequence.

Clinical machine learning predicting best stroke rehabilitation responders to exoskeletal robotic gait rehabilitation.

BACKGROUND: Although clinical machine learning (ML) algorithms offer promising potential in forecasting optimal stroke rehabilitation outcomes, their specific capacity to ascertain favorable outcomes and identify responders to robotic-assisted gait training (RAGT) in individuals with hemiparetic stroke undergoing such intervention remains unexplored. OBJECTIVE: We aimed to determine the best predictive model based on the international classification of functioning impairment domain features (Fugl- Meyer assessment (FMA), Modified Barthel index related-gait scale (MBI), Berg balance scale (BBS)) and reveal their responsiveness to robotic assisted gait training (RAGT) in patients with subacute stroke. METHODS: Data from 187 people with subacute stroke who underwent a 12-week Walkbot RAGT intervention were obtained and analyzed. Overall, 18 potential predictors encompassed demographic characteristics and the baseline score of functional and structural features. Five predictive ML models, including decision tree, random forest, eXtreme Gradient Boosting, light gradient boosting machine, and categorical boosting, were used. RESULTS: The initial and final BBS, initial BBS, final Modified Ashworth scale, and initial MBI scores were important features, predicting functional improvements. eXtreme Gradient Boosting demonstrated superior performance compared to other models in predicting functional recovery after RAGT in patients with subacute stroke. CONCLUSION: eXtreme Gradient Boosting may be an invaluable prognostic tool, providing clinicians and caregivers with a robust framework to make precise clinical decisions regarding the identification of optimal responders and effectively pinpoint those who are most likely to derive maximum benefits from RAGT interventions.

AI-based dental caries and tooth number detection in intraoral photos: Model development and performance evaluation.

OBJECTIVES: In this study, we aimed to integrate tooth number recognition and caries detection in full intraoral photographic images using a cascade region-based deep convolutional neural network (R-CNN) model to facilitate the practical application of artificial intelligence (AI)-driven automatic caries detection in clinical practice. METHODS: Our dataset comprised 24,578 images, encompassing 4787 upper occlusal, 4347 lower occlusal, 5230 right lateral, 5010 left lateral, and 5204 frontal views. In each intraoral image, tooth numbers and, when present, dental caries, including their location and stage, were annotated using bounding boxes. A cascade R-CNN model was used for dental caries detection and tooth number recognition within intraoral images. RESULTS: For tooth number recognition, the model achieved an average mean average precision (mAP) score of 0.880. In the task of dental caries detection, the model's average mAP score was 0.769, with individual scores spanning from 0.695 to 0.893. CONCLUSIONS: The primary objective of integrating tooth number recognition and caries detection within full intraoral photographic images has been achieved by our deep learning model. The model's training on comprehensive intraoral datasets has demonstrated its potential for seamless clinical application. CLINICAL SIGNIFICANCE: This research holds clinical significance by achieving AI-driven automatic integration of tooth number recognition and caries detection in full intraoral images where multiple teeth are visible. It has the potential to promote the practical application of AI in real-life and clinical settings.

The effects of osteopathic manipulative treatment on pain and disability in patients with chronic low back pain: a single-blinded randomized controlled trial.

CONTEXT: The evidence for the efficacy of osteopathic manipulative treatment (OMT) in the management of low back pain (LBP) is considered weak by systematic reviews, because it is generally based on low-quality studies. Consequently, there is a need for more randomized controlled trials (RCTs) with a low risk of bias. OBJECTIVES: The objective of this study is to evaluate the efficacy of an OMT intervention for reducing pain and disability in patients with chronic LBP. METHODS: A single-blinded, crossover, RCT was conducted at a university-based health system. Participants were adults, 21-65 years old, with nonspecific LBP. Eligible participants (n=80) were randomized to two trial arms: an immediate OMT intervention group and a delayed OMT (waiting period) group. The intervention consisted of three to four OMT sessions over 4-6 weeks, after which the participants switched (crossed-over) groups. The primary clinical outcomes were average pain, current pain, Patient-Reported Outcomes Measurement Information System (PROMIS) 29 v1.0 pain interference and physical function, and modified Oswestry Disability Index (ODI). Secondary outcomes included the remaining PROMIS health domains and the Fear Avoidance Beliefs Questionnaire (FABQ). These measures were taken at baseline (T0), after one OMT session (T1), at the crossover point (T2), and at the end of the trial (T3). Due to the carryover effects of OMT intervention, only the outcomes obtained prior to T2 were evaluated utilizing mixed-effects models and after adjusting for baseline values. RESULTS: Totals of 35 and 36 participants with chronic LBP were available for the analysis at T1 in the immediate OMT and waiting period groups, respectively, whereas 31 and 33 participants were available for the analysis at T2 in the immediate OMT and waiting period groups, respectively. After one session of OMT (T1), the analysis showed a significant reduction in the secondary outcomes of sleep disturbance and anxiety compared to the waiting period group. Following the entire intervention period (T2), the immediate OMT group demonstrated a significantly better average pain outcome. The effect size was a 0.8 standard deviation (SD), rendering the reduction in pain clinically significant. Further, the improvement in anxiety remained statistically significant. No study-related serious adverse events (AEs) were reported. CONCLUSIONS: OMT intervention is safe and effective in reducing pain along with improving sleep and anxiety profiles in patients with chronic LBP.

A thermogenic secondary sexual character in male sea lamprey.

Secondary sexual characters in animals are exaggerated ornaments or weapons for intrasexual competition. Unexpectedly, we found that a male secondary sexual character in sea lamprey (Petromyzon marinus) is a thermogenic adipose tissue that instantly increases its heat production during sexual encounters. This secondary sexual character, developed in front of the anterior dorsal fin of mature males, is a swollen dorsal ridge known as the 'rope' tissue. It contains nerve bundles, multivacuolar adipocytes and interstitial cells packed with small lipid droplets and mitochondria with dense and highly organized cristae. The fatty acid composition of the rope tissue is rich in unsaturated fatty acids. The cytochrome c oxidase activity is high but the ATP concentration is very low in the mitochondria of the rope tissue compared with those of the gill and muscle tissues. The rope tissue temperature immediately rose up to 0.3°C when the male encountered a conspecific. Mature males generated more heat in the rope and muscle tissues when presented with a mature female than when presented with a male (paired t-test, P<0.05). On average, the rope generated 0.027±0.013 W cm(-3) more heat than the muscle in 10 min. Transcriptome analyses revealed that genes involved in fat cell differentiation are upregulated whereas those involved in oxidative-phosphorylation-coupled ATP synthesis are downregulated in the rope tissue compared with the gill and muscle tissues. Sexually mature male sea lamprey possess the only known thermogenic secondary sexual character that shows differential heat generation toward individual conspecifics.

Explainable deep learning-based clinical decision support engine for MRI-based automated diagnosis of temporomandibular joint anterior disk displacement.

BACKGROUND AND OBJECTIVE: MRI is considered the gold standard for diagnosing anterior disc displacement (ADD), the most common temporomandibular joint (TMJ) disorder. However, even highly trained clinicians find it difficult to integrate the dynamic nature of MRI with the complicated anatomical features of the TMJ. As the first validated study for MRI-based automatic TMJ ADD diagnosis, we propose a clinical decision support engine that diagnoses TMJ ADD using MR images and provides heat maps as the visualized rationale of diagnostic predictions using explainable artificial intelligence. METHODS: The engine builds on two deep learning models. The first deep learning model detects a region of interest (ROI) containing three TMJ components (i.e., temporal bone, disc, and condyle) in the entire sagittal MR image. The second deep learning model classifies TMJ ADD into three classes (i.e., normal, ADD without reduction, and ADD with reduction) within the detected ROI. In this retrospective study, the models were developed and tested on the dataset acquired between April 2005 to April 2020. The additional independent dataset acquired at a different hospital between January 2016 to February 2019 was used for the external test of the classification model. Detection performance was assessed by mean average precision (mAP). Classification performance was assessed by the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and Youden's index. 95% confidence intervals were calculated via non-parametric bootstrap to assess the statistical significance of model performances. RESULTS: The ROI detection model achieved mAP of 0.819 at 0.75 intersection over union (IoU) thresholds in the internal test. In internal and external tests, the ADD classification model achieved AUROC values of 0.985 and 0.960, sensitivities of 0.950 and 0.926, and specificities of 0.919 and 0.892, respectively. CONCLUSIONS: The proposed explainable deep learning-based engine provides clinicians with the predictive result and its visualized rationale. The clinicians can make the final diagnosis by integrating primary diagnostic prediction obtained from the proposed engine with the patient's clinical examination findings.

Multiplexed DNA-functionalized graphene sensor with artificial intelligence-based discrimination performance for analyzing chemical vapor compositions.

This study presents a new technology that can detect and discriminate individual chemical vapors to determine the chemical vapor composition of mixed chemical composition in situ based on a multiplexed DNA-functionalized graphene (MDFG) nanoelectrode without the need to condense the original vapor or target dilution. To the best of our knowledge, our artificial intelligence (AI)-operated arrayed electrodes were capable of identifying the compositions of mixed chemical gases with a mixed ratio in the early stage. This innovative technology comprised an optimized combination of nanodeposited arrayed electrodes and artificial intelligence techniques with advanced sensing capabilities that could operate within biological limits, resulting in the verification of mixed vapor chemical components. Highly selective sensors that are tolerant to high humidity levels provide a target for "breath chemovapor fingerprinting" for the early diagnosis of diseases. The feature selection analysis achieved recognition rates of 99% and above under low-humidity conditions and 98% and above under humid conditions for mixed chemical compositions. The 1D convolutional neural network analysis performed better, discriminating the compositional state of chemical vapor under low- and high-humidity conditions almost perfectly. This study provides a basis for the use of a multiplexed DNA-functionalized graphene gas sensor array and artificial intelligence-based discrimination of chemical vapor compositions in breath analysis applications.

Policy Design for an Ankle-Foot Orthosis Using Simulated Physical Human-Robot Interaction via Deep Reinforcement Learning.

This paper presents a novel approach for designing a robotic orthosis controller considering physical human-robot interaction (pHRI). Computer simulation for this human-robot system can be advantageous in terms of time and cost due to the laborious nature of designing a robot controller that effectively assists humans with the appropriate magnitude and phase. Therefore, we propose a two-stage policy training framework based on deep reinforcement learning (deep RL) to design a robot controller using human-robot dynamic simulation. In Stage 1, the optimal policy of generating human gaits is obtained from deep RL-based imitation learning on a healthy subject model using the musculoskeletal simulation in OpenSim-RL. In Stage 2, human models in which the right soleus muscle is weakened to a certain severity are created by modifying the human model obtained from Stage 1. A robotic orthosis is then attached to the right ankle of these models. The orthosis policy that assists walking with optimal torque is then trained on these models. Here, the elastic foundation model is used to predict the pHRI in the coupling part between the human and robotic orthosis. Comparative analysis of kinematic and kinetic simulation results with the experimental data shows that the derived human musculoskeletal model imitates a human walking. It also shows that the robotic orthosis policy obtained from two-stage policy training can assist the weakened soleus muscle. The proposed approach was validated by applying the learned policy to ankle orthosis, conducting a gait experiment, and comparing it with the simulation results.

The effects of osteopathic manipulative treatment on pain and disability in patients with chronic neck pain: A single-blinded randomized controlled trial.

BACKGROUND: Neck pain (NP) affects up to 70% of individuals at some point in their lives. Systematic reviews indicate that manual treatments can be moderately effective in the management of chronic, nonspecific NP. However, there is a paucity of studies specifically evaluating the efficacy of osteopathic manipulative treatment (OMT). OBJECTIVE: To evaluate the efficacy of OMT in reducing pain and disability in patients with chronic NP. DESIGN: Single-blinded, cross-over, randomized-controlled trial. SETTING: University-based, osteopathic manipulative medicine outpatient clinic. PARTICIPANTS: Ninety-seven participants, 21 to 65 years of age, with chronic, nonspecific NP. INTERVENTIONS: Participants were randomized to two trial arms: immediate OMT intervention or waiting period first. The intervention consisted of three to four OMT sessions over 4 to 6 weeks, after which the participants switched groups. MAIN OUTCOME MEASURES: Primary outcome measures were pain intensity (average and current) on the numerical rating scale and Neck Disability Index. Secondary outcomes included Patient-Reported Outcomes Measurement Information System-29 (PROMIS-29) health domains and Fear Avoidance Beliefs Questionnaire. Outcomes obtained prior to the cross-over allocation were evaluated using general linear models and after adjusting for baseline values. RESULTS: A total of 38 and 37 participants were available for the analysis in the OMT and waiting period groups, respectively. The results showed significantly better primary outcomes in the immediate OMT group for reductions in average pain (-1.02, 95% confidence interval [CI] -1.72, -0.32; p = .005), current pain (-1.02, 95% CI -1.75, -0.30; p = .006), disability (-5.30%, 95% CI -9.2%, -1.3%; p = .010) and improved secondary outcomes (PROMIS) related to sleep (-3.25, 95% CI -6.95, -1.54; p = .003), fatigue (-3.26, 95% CI -6.04, -0.48; p = .022), and depression (-2.59, 95% CI -4.73, -0.45; p = .018). The effect sizes were in the clinically meaningful range between 0.5 and 1 standard deviation. No study-related serious adverse events were reported. CONCLUSIONS: OMT is relatively safe and effective in reducing pain and disability along with improving sleep, fatigue, and depression in patients with chronic NP immediately following treatment delivered over approximately 4 to 6 weeks.

Adaptive sampling for learning gaussian processes using mobile sensor networks.

This paper presents a novel class of self-organizing sensing agents that adaptively learn an anisotropic, spatio-temporal gaussian process using noisy measurements and move in order to improve the quality of the estimated covariance function. This approach is based on a class of anisotropic covariance functions of gaussian processes introduced to model a broad range of spatio-temporal physical phenomena. The covariance function is assumed to be unknown a priori. Hence, it is estimated by the maximum a posteriori probability (MAP) estimator. The prediction of the field of interest is then obtained based on the MAP estimate of the covariance function. An optimal sampling strategy is proposed to minimize the information-theoretic cost function of the Fisher Information Matrix. Simulation results demonstrate the effectiveness and the adaptability of the proposed scheme.

Machine learning approaches to surrogate multifidelity Growth and Remodeling models for efficient abdominal aortic aneurysmal applications.

Computational Growth and Remodeling (G&R) models have been widely used to capture the pathological development of arterial diseases and have shown promise for aiding clinical diagnosis, prognosis prediction, and staging classification. However, due to the high complexity of the arterial adaptation mechanism, high-fidelity arterial G&R simulation usually takes hours or even days, which hinders its application in clinical practice. To remedy this problem, we develop a computationally efficient arterial G&R simulation framework that comprehensively combines the physics-based G&R simulations and data-driven machine learning approaches. The proposed framework greatly enhances the computational efficiency of arterial G&R simulations, thereby enabling more time-consuming arterial applications, including personalized parameter estimation and arterial disease progression prediction. In particular, we achieve significant computational cost reduction mainly through two methods: (1) constructing a Multifidelity Surrogate (MFS) to approximate multifidelity G&R simulations by using a cokriging approach and (2) developing a novel iterative optimization algorithm for personalized parameter estimation. The proposed framework is demonstrated by estimating G&R model parameters and predicting individual aneurysm growth using follow-up CT images of Abdominal Aortic Aneurysms (AAAs) from 21 patients. Results show that the personalized parameters are satisfactorily estimated and the growth of AAAs is predicted within the clinically relevant time frame, i.e., less than 2 h, without a loss of accuracy.

Large-Area, Crosstalk-Free, Flexible Tactile Sensor Matrix Pixelated by Mesh Layers.

Tactile sensor arrays have attracted considerable attention for their use in diverse applications, such as advanced robotics and interactive human-machine interfaces. However, conventional tactile sensor arrays suffer from electrical crosstalk caused by current leakages between the tactile cells. The approaches that have been proposed thus far to overcome this issue require complex rectifier circuits or a serial fabrication process. This article reports a flexible tactile sensor array fabricated through a batch process using a mesh. A carbon nanotube-polydimethylsiloxane composite is used to form an array of sensing cells in the mesh through a simple "dip-coating" process and is cured into a concave shape. The contact area between the electrode and the composite changes significantly under pressure, resulting in an excellent sensitivity (5.61 kPa-1) over a wide range of pressure up to 600 kPa. The mesh separates the composite into the arranged sensing cells to prevent the electrical connection between adjacent cells and simultaneously connects each cell mechanically. Additionally, the sensor shows superior durability compared with previously reported tactile sensors because the mesh acts as a support beam. Furthermore, the tactile sensor array is successfully utilized as a Braille reader via information processing based on machine learning.