Low Discrepancy Research Articles

Particle Swarm Optimization with New Initializing Technique to Solve Global Optimization Problems

Particle Swarm Optimization (PSO) is a well-known extensively utilized algorithm for a distinct type of optimization problem. In meta-heuristic algorithms, population initialization plays a vital role in solving the classical problems of optimization. The population’s initialization in meta-heuristic algorithms urges the convergence rate and diversity, besides this, it is remarkably beneficial for finding the efficient and effective optimal solution. In this study, we proposed an enhanced variation of the PSO algorithm by using a quasi-random sequence (QRS) for population initialization to improve the convergence rate and diversity. Furthermore, this study represents a new approach for population initialization by incorporating the torus sequence with PSO known as TO-PSO. The torus sequence belongs to the family of low discrepancy sequence and it is utilized in the proposed variant of PSO for the initialization of swarm. The proposed strategy of population’s initialization has been observed with the fifteen most famous unimodal and multimodal benchmark test problems. The outcomes of our proposed technique display outstanding performance as compared with the traditional PSO, PSO initialized with Sobol Sequence (SO-PSO) and Halton sequence (HO-PSO). The exhaustive experimental results conclude that the proposed algorithm remarkably superior to the other classical approaches. Additionally, the outcomes produced from our proposed work exhibits anticipation that how immensely the proposed approach highly influences the value of cost function, convergence rate, and diversity.

Systematic Online Tuning of Multirotor UAVs for Accurate Trajectory Tracking Under Wind Disturbances and In-Flight Dynamics Changes

The demand for accurate and fast trajectory tracking for multirotor Unmanned Aerial Vehicles (UAVs) have grown recently due to advances in UAV avionics technology and application domains. In many applications, the multirotor UAV is required to accurately perform aggressive maneuvers in challenging scenarios like the presence of external wind disturbances or in-flight payload changes. In this paper, we propose a systematic controller tuning approach based on identification results obtained by a recently developed Deep Neural Networks with the Modified Relay Feedback Test (DNN-MRFT) algorithm. We formulate a linear equivalent representation suitable for DNN-MRFT using feedback linearization. This representation enables the analytical investigation of different controller structures and tuning settings, and captures the non-linearity trends of the system. With this approach, the trade-off between performance and robustness in design was made possible which is convenient for the design of controllers of UAVs operating in uncertain environments. We demonstrate that our approach is adaptive and robust through a set of experiments, where accurate trajectory tracking is maintained despite significant changes to the UAV aerodynamic characteristics and the application of wind disturbance. Due to the model-based system design, it was possible to obtain low discrepancy between simulation and experimental results which is beneficial for potential use of the proposed approach for real-time model-based planning and fault detection tasks. We obtained RMSE of 3.59 cm when tracking aggressive trajectories in the presence of strong wind, which is on par with state-of-the-art.

Uncertainty quantification for aerodynamic pressure probes, using adaptive quasi-Monte Carlo

In the present study, an adaptive randomized Quasi Monte Carlo methodology is presented, combining Stein’s two-stage adaptive scheme and Low Discrepancy Sobol sequences. The method is used for the propagation and calculation of uncertainties related to aerodynamic pneumatic probes and high frequency fast response aerodynamic probes (FRAP) in the subsonic flow regime, . The use of the proposed methodology allows the fast and accurate, in a probabilistic sense, calculation of uncertainties, ensuring that the total number of Monte Carlo (MC) trials is kept low based on the desired numerical accuracy. Thus, the method is well-suited for aerodynamic pressure probes, where multiple points need to be evaluated in their calibration space. Complete and detailed measurement models are presented for both a pneumatic probe and FRAP. The models are segregated in sub-problems that allow the evaluation and inspection of intermediate steps of MC in a transparent manner, also enabling the calculation of the relative contributions of the elemental uncertainties on the measured quantities. The adaptive scheme is implemented with a block-to-block evaluation, which accelerates the calculation of statistical parameters. Various, commonly-used sampling techniques for MC simulation and different adaptive MC schemes are compared, using both theoretical toy distributions and actual examples from aerodynamic probes’ measurement models. The robustness of Stein’s two-stage scheme is demonstrated even in cases when significant deviation from normality is observed in the underlying distribution of the output of the MC. With regards to FRAP, two issues related to piezo-resistive sensors are addressed, namely temperature dependent pressure hysteresis and temporal sensor drift, and their uncertainties are accounted for in the measurement model. These effects are the most dominant factors, affecting all flow quantities’ uncertainties, with significance that varies mainly with M and operating temperature. This work highlights the need to construct accurate and detailed measurement models for aerodynamic probes, that otherwise will result in significant underestimation (routinely in excess of 50) of the final uncertainties.

3D crack identification using the Nelder-Mead Simplex algorithm combined with a random generation of crack positions

In this paper, we present a scheme for cracks identification in three-dimensional linear elastic mechanical components. The scheme uses a boundary element method for solving the forward problem and the Nelder-Mead simplex numerical optimization algorithm coupled with a low discrepancy sequence in order to identify an embedded crack. The crack detection process is achieved through minimizing an objective function defined as the difference between measured strains and computed ones, at some specific sensors on the domain boundaries. Through the optimization procedure, the crack surface is modelled by geometrical parameters, which serve as identity variables. Numerical simulations are conducted to determine the identity parameters of an embedded elliptical crack, with measures randomly perturbed and the residual norm regularized in order to provide an efficient and numerically stable solution to measurement noise. The accuracy of this method is investigated in the identification of cracks over two examples. Through the treated examples, we showed that the method exhibits good stability with respect to measurement noise and convergent results could be achieved without restrictions on the selected initial values of the crack parameters.

The Importance of Information: Prenatal Education Surrounding Birth-Related Pelvic Floor Trauma Mitigates Symptom-Related Distress

Background: Birth-related pelvic floor trauma and its sequelae (PFTS) are associated with higher rates of perinatal mood and anxiety disorders in the fourth trimester. The fourth trimester is a critical window for long-term health outcomes, and identifying factors that buffer the impact of PFTS is an important research priority. Objectives: We hypothesized that lack of prenatal pelvic floor-focused education may be associated with greater distress following PFTS. Study Design: Hypotheses were investigated using a repeated-measures (study 1; n = 36) and cross-sectional (study 2; n = 226) design among participants reporting PFTS. Questionnaires were completed online, and included qualitative and quantitative responses. Methods: Study 1 participants completed a questionnaire within the first 8 weeks and approximately 3 months postpartum. Study 2 participants (≤5 years postpartum) completed a 1-time questionnaire. We examined associations among pelvic floor education, pelvic floor symptoms, and indices of mental health. Moderation analyses were conducted (study 2) to test whether the association between pelvic floor and mental health symptoms varied depending on level of discrepancy between postpartum expectation and experience. Results: In both studies, a high percentage of participants reported a lack of pelvic floor education, and significant associations were seen between pelvic floor and psychological symptoms. In study 2, individuals who reported high discrepancy between expectations and experience had the strongest associations between pelvic floor and psychological symptoms; for those reporting low discrepancy, the association was nonsignificant. Conclusion: Prenatal pelvic floor education is a key aspect not just in reducing risk of PFTS, but mitigating distress if it does occur.

Quantifying Uncertainty of Damage in Composites Using a Quasi Monte Carlo Technique

Abstract Property variations in a structure strongly impact the macroscopic mechanical performance as regions with lower strength will be prone to damage initiation or acceleration. Consideration of the variability in material property is critical for high-resolution simulations of damage initiation and propagation. While the recent progressive damage analyses consider randomness in property fields, accurately quantifying the uncertainty in damage measures remains computationally expensive. Stochastic damage analyses require extensive sampling of random property fields and numerous replications of the underlying nonlinear deterministic simulations. This paper demonstrates that a Quasi-Monte Carlo (QMC) method, which uses a multidimensional low discrepancy sobol sequence, is a computationally economical way to obtain the mean and standard deviations in cracks evolving in composites. An extended finite element method (XFEM) method with spatially random strength fields simulates the damage initiation and evolution in a model composite. We compared the number of simulations required for Monte Carlo (MC) and QMC techniques to measure the influence of input variability on the mean crack-length in an open-hole angle-ply tensile test. We conclude that the low discrepancy sampling and QMC technique converges substantially faster than traditional MC methods.

Evaluation of several initialization methods on arithmetic optimization algorithm performance

Abstract Arithmetic optimization algorithm (AOA) is one of the recently proposed population-based metaheuristic algorithms. The algorithmic design concept of the AOA is based on the distributive behavior of arithmetic operators, namely, multiplication (M), division (D), subtraction (S), and addition (A). Being a new metaheuristic algorithm, the need for a performance evaluation of AOA is significant to the global optimization research community and specifically to nature-inspired metaheuristic enthusiasts. This article aims to evaluate the influence of the algorithm control parameters, namely, population size and the number of iterations, on the performance of the newly proposed AOA. In addition, we also investigated and validated the influence of different initialization schemes available in the literature on the performance of the AOA. Experiments were conducted using different initialization scenarios and the first is where the population size is large and the number of iterations is low. The second scenario is when the number of iterations is high, and the population size is small. Finally, when the population size and the number of iterations are similar. The numerical results from the conducted experiments showed that AOA is sensitive to the population size and requires a large population size for optimal performance. Afterward, we initialized AOA with six initialization schemes, and their performances were tested on the classical functions and the functions defined in the CEC 2020 suite. The results were presented, and their implications were discussed. Our results showed that the performance of AOA could be influenced when the solution is initialized with schemes other than default random numbers. The Beta distribution outperformed the random number distribution in all cases for both the classical and CEC 2020 functions. The performance of uniform distribution, Rayleigh distribution, Latin hypercube sampling, and Sobol low discrepancy sequence are relatively competitive with the Random number. On the basis of our experiments’ results, we recommend that a solution size of 6,000, the number of iterations of 100, and initializing the solutions with Beta distribution will lead to AOA performing optimally for scenarios considered in our experiments.

Population initialization techniques for evolutionary algorithms for single-objective constrained optimization problems: Deterministic vs. stochastic techniques

The population initialization step is a common step in the majority (or even all) of evolutionary algorithms (EAs). There are many population initialization techniques. Due to the limited population size and the high dimensionality of many problems, there is little chance to cover the promising regions in the search space. From different perspectives, this paper compares the stochastic and deterministic population initialization techniques through comparing five of the well-known population initializers: Random number generator (RNG), Latin Hypercube, Sobol, Halton, and Kronecker. Due to the presence of many constraints in real-world applications, in this paper, we are focusing only on single-objective constrained optimization problems. Specifically, the goal is to investigate if there is a significant difference between these population initialization methods. In this paper, we explain theoretically and mathematically these different population initialization techniques. Moreover, different illustrative examples and visualizations are introduced to explain the behavior of each technique and compare different techniques from different perspectives. The results show that due to the high uniformity of the low-discrepancy sequences such as the Halton and Sobol sequences, the generated points using these sequences are more evenly distributed over the space than RNG, which is the commonly used technique for initializing the populations in EAs. Practically, using a set of benchmark functions, we investigate the use of each population initialization technique for initializing different population-based evolutionary algorithms. The results of our experiments prove that with sufficient numbers of iterations, the EAs are not sensitive to the initialization methods and there are no significant differences between the mentioned population initialization methods. Further, the low discrepancy methods enhance the exploration ability of EAs in early iterations.

Cascaded Sobol' sampling

Rendering quality is largely influenced by the samplers used in Monte Carlo integration. Important factors include sample uniformity (e.g., low discrepancy) in the high-dimensional integration domain, sample uniformity in lower-dimensional projections, and lack of dominant structures that could result in aliasing artifacts. A widely used and successful construction is the Sobol' sequence that guarantees good high-dimensional uniformity and consequently results in faster convergence of quasi-Monte Carlo integration. We show that this sequence exhibits low uniformity and dominant structures in low-dimensional projections. These structures impair quality in the context of rendering, as they precisely occur in the 2-dimensional projections used for sampling light sources, reflectance functions, or the camera lens or sensor. We propose a new cascaded construction, which, despite dropping the sequential aspect of Sobol' samples, produces point sets exhibiting provably perfect dyadic partitioning (and therefore, excellent uniformity) in consecutive 2-dimensional projections, while preserving good high-dimensional uniformity. By optimizing the initialization parameters and performing Owen scrambling at finer levels of binary representations, we further improve over Sobol's integration convergence rate. Our method does not incur any overhead as compared to the generation of the Sobol' sequence, is compatible with Owen scrambling and can be used in rendering applications.

On approximating the shape of one-dimensional posterior marginals using the low discrepancy points

A method to approximate Bayesian posterior by evaluating it on a low discrepancy sequence (LDS) point set has recently been proposed. However, this method does not focus on finding the posterior marginals. Finding posterior marginals when the posterior approximation is obtained using LDS is not straightforward, and as yet, there is no method to approximate one dimensional marginals using an LDS. We propose an approximation method for this problem. This method is based on an s-dimensional integration rule together with fitting a polynomial smoothing function. We state and prove results showing conditions under which this polynomial smoothing function will converge to the true one-dimensional function. We also demonstrate the computational efficiency of the new approach compared to a grid based approach.

Low Discrepancy Sparse Phased Array Antennas.

Sparse arrays have grating lobes in the far field pattern due to the large spacing of elements residing in a rectangular or triangular grid. Random element spacing removes the grating lobes but produces large variations in element density across the aperture. In fact, some areas are so dense that the elements overlap. This paper introduces a low discrepancy sequence (LDS) for generating the element locations in sparse planar arrays without grating lobes. This nonrandom alternative finds an element layout that reduces the grating lobes while keeping the elements far enough apart for practical construction. Our studies consider uniform sparse LDS arrays with 86% less elements than a fully populated array, and numerical results are presented that show these sampling techniques are capable of completely removing the grating lobes of sparse arrays. We present the mathematical formulation for implementing an LDS generated element lattice for sparse planar arrays, and present numerical results on their performance. Multiple array configurations are studied, and we show that these LDS techniques are not impacted by the type/shape of the planar array. Moreover, in comparison between the LDS techniques, we show that the Poisson disk sampling technique outperforms all other approaches and is the recommended LDS technique for sparse arrays.

Integrating operations and marketing decisions to manage perishability risks with target minimum remaining shelf-life available to consumers

We study an aggregate level planning problem at the bottleneck packaging stage of a Make-and-Pack production system of perishable products and integrate sequencing decisions. The perishability risks are accounted for by producing to order with remaining shelf-life (RSL) dependent demand and age-dependent expected product holding cost till dispatch. Products dispatch time determines the Maximum-RSL delivered. Unlike past studies, we examine a target Minimum-RSL that marketing aims to provide. The product demand decreases from its Maximum-RSL to the Minimum-RSL. The problem is modeled as a two-stage stochastic 0–1 integer non-linear programming problem. The first-stage decisions are the inventory of intermediate products, packaging sequence, capacity allocation, Maximum-RSL, and Minimum-RSL. In the second stage, market demand is realized and has a stochastic error component. The stochastic problem is converted into its deterministic equivalent using the Sample-Average-Approximation method. Representing the demand scenarios using Low Discrepancy Sequences and reducing variance using the Antithetic Variates technique provide stable solutions. Sensitivity analysis demonstrates that the integrated-firm improves freshness criteria (Maximum-RSL and Minimum-RSL) by reducing setup time/demand variability/demand satisfaction requirement (shortage cost) or increasing importance of customer service in product freshness (product holding cost). Maximum-RSL increases with increased packaging rate. High demand variability, shortage cost, product holding cost, and low packaging rate promote integrated sequencing-capacity allocation. An efficient heuristic based on the Generalized Benders’ Decomposition method is proposed to solve large-size problems. The heuristic provides a better solution than the DICOPT solver, solves far fewer non-linear programming problems than the Simple Branch-and-Bound solver, with less computational effort.

Investigating the AX6 inertial-based wearable for instrumented physical capability assessment of young adults in a low-resource setting

Instrumented balance and gait test are an important component of physical capability assessment in clinical examinations. This study provides insight to a new generation Open Movement inertial-based wearable (AX6, Axivity, UK) compared to a previously validated reference (AX3). The AX6 was assessed for its ability to quantify a battery of tasks that represent a composite physical capability assessment. Participants wore both wearables on the lower back (5th lumbar vertebrae) continuously throughout the testing period. No significant differences (p < 0.05) were found between the AX6 and reference wearable (AX3) for time taken to complete the following tasks: 4-m walk (p = 0.18), sit to stand (p = 0.05), and timed up and go (p = 0.55). Bland-Altman analysis plots suggest good to excellent agreement between the AX6 and reference (AX3) device with low discrepancy in mean differences and narrow limits of agreement. Significant differences were found between the AX6 and manual recorded times in the 4-m walk test and 2-min walk test, and no significant differences in sit-to-stand and timed-up-and- go were observed. Temporal data for both wearables were compared with no differences in step time, stride time, swing time and stance time. Differences were observed for spatial digital biomarker/characteristic (step length). This study shows the AX6 to be a reliable device for objectively quantifying data from physical capability tasks. These findings also reinforce the advantages of using open source, instrumented testing and methods for physical capability and disease monitoring. • Explores the accuracy of a new Open Movement wearable for physical functioning. • A test battery of physical functioning tasks are used. • Findings imply the AX6 is a suitable digital tool for use in low-resource settings. • Open Movement wearables may be useful as transparent tools for smart digital health.

Functional Electrical Stimulation System for Drop Foot Correction Using a Dynamic NARX Neural Network

Neurological diseases may reduce Tibialis Anterior (TA) muscle recruitment capacity causing gait disorders, such as drop foot (DF). The majority of DF patients still retain excitable nerves and muscles which makes Functional Electrical Stimulation (FES) an adequate technique to restore lost mobility. Recent studies suggest the need for developing personalized and assist-as-needed control strategies for wearable FES in order to promote natural and functional movements while reducing the early onset of fatigue. This study contributes to a real-time implementation of a trajectory tracking FES control strategy for personalized DF correction. This strategy combines a feedforward Non-Linear Autoregressive Neural Network with Exogenous inputs (NARXNN) with a feedback PD controller. This control strategy advances with a user-specific TA muscle model achieved by the NARXNN’s ability to model dynamic systems relying on the foot angle and angular velocity as inputs. A closed-loop, fully wearable stimulation system was achieved using an ISTim stimulator and wearable inertial sensor for electrical stimulation and user’s kinematic gait sensing, respectively. Results showed that the NARXNN architecture with 2 hidden layers and 10 neurons provided the highest performance for modelling the kinematic behaviour of the TA muscle. The proposed trajectory tracking control revealed a low discrepancy between real and reference foot trajectories (goodness of fit = 77.87%) and time-effectiveness for correctly stimulating the TA muscle towards a natural gait and DF correction.

Studying the Impact of Initialization for Population-Based Algorithms with Low-Discrepancy Sequences

To solve different kinds of optimization challenges, meta-heuristic algorithms have been extensively used. Population initialization plays a prominent role in meta-heuristic algorithms for the problem of optimization. These algorithms can affect convergence to identify a robust optimum solution. To investigate the effectiveness of diversity, many scholars have a focus on the reliability and quality of meta-heuristic algorithms for enhancement. To initialize the population in the search space, this dissertation proposes three new low discrepancy sequences for population initialization instead of uniform distribution called the WELL sequence, Knuth sequence, and Torus sequence. This paper also introduces a detailed survey of the different initialization methods of PSO and DE based on quasi-random sequence families such as the Sobol sequence, Halton sequence, and uniform random distribution. For well-known benchmark test problems and learning of artificial neural network, the proposed methods for PSO (TO-PSO, KN-PSO, and WE-PSO), BA (BA-TO, BA-WE, and BA-KN), and DE (DE-TO, DE-WE, and DE-KN) have been evaluated. The synthesis of our strategies demonstrates promising success over uniform random numbers using low discrepancy sequences. The experimental findings indicate that the initialization based on low discrepancy sequences is exceptionally stronger than the uniform random number. Furthermore, our work outlines the profound effects on convergence and heterogeneity of the proposed methodology. It is expected that a comparative simulation survey of the low discrepancy sequence would be beneficial for the investigator to analyze the meta-heuristic algorithms in detail.

A Low Discrepancy Sequence on Graphs

Many applications such as election forecasting, environmental monitoring, health policy, and graph based machine learning require taking expectation of functions defined on the vertices of a graph. We describe a construction of a sampling scheme analogous to the so called Leja points in complex potential theory that can be proved to give low discrepancy estimates for the approximation of the expected value by the impirical expected value based on these points. In contrast to classical potential theory where the kernel is fixed and the equilibrium distribution depends upon the kernel, we fix a probability distribution and construct a kernel (which represents the graph structure) for which the equilibrium distribution is the given probability distribution. Our estimates do not depend upon the size of the graph.

Comparative Analysis of Low Discrepancy Sequence-Based Initialization Approaches Using Population-Based Algorithms for Solving the Global Optimization Problems

Metaheuristic algorithms have been widely used to solve diverse kinds of optimization problems. For an optimization problem, population initialization plays a significant role in metaheuristic algorithms. These algorithms can influence the convergence to find an efficient optimal solution. Mainly, for recognizing the importance of diversity, several researchers have worked on the performance for the improvement of metaheuristic algorithms. Population initialization is a vital factor in metaheuristic algorithms such as PSO and DE. Instead of applying the random distribution for the initialization of the population, quasirandom sequences are more useful for the improvement the diversity and convergence factors. This study presents three new low-discrepancy sequences named WELL sequence, Knuth sequence, and Torus sequence to initialize the population in the search space. This paper also gives a comprehensive survey of the various PSO and DE initialization approaches based on the family of quasirandom sequences such as Sobol sequence, Halton sequence, and uniform random distribution. The proposed methods for PSO (TO-PSO, KN-PSO, and WE-PSO) and DE (DE-TO, DE-WE, and DE-KN) have been examined for well-known benchmark test problems and training of the artificial neural network. The finding of our techniques shows promising performance using the family of low-discrepancy sequences over uniform random numbers. For a fair comparison, the approaches using low-discrepancy sequences for PSO and DE are compared with the other family of low-discrepancy sequences and uniform random number and depict the superior results. The experimental results show that the low-discrepancy sequences-based initialization performed exceptionally better than a uniform random number. Moreover, the outcome of our work presents a foresight on how the proposed technique profoundly impacts convergence and diversity. It is anticipated that this low-discrepancy sequence comparative simulation survey would be helpful for studying the metaheuristic algorithm in detail for the researcher.

Absolute dose measurements for lung gated delivery stereotactic body radiation therapy

PurposeTo evaluate absolute dose between phase- and amplitude-gated modes for lung stereotactic body radiation therapy (SBRT), using the moving targets developed in-house. MethodsThe moving targets were developed in-house, allowing measurements of absolute dose using Semiflex ion chamber, Pinpoint ion chamber and microDiamond detector. 24 plans were generated in the Pinnacle 16.2 treatment planning system (TPS). 720 partial dynamic conformal arcs (DCA), 120 for phase- and 120 for amplitude-gated mode per detector, were performed to compare dose discrepancy (planned vs measured doses), for all detectors. Single fraction of 15 Gy was delivered with TrueBeam Novalis STx, triggered on the stable phases (phase-gated mode) or near the end-exhalation (amplitude-gated mode), for a breathing period ranging from 2 s to 7 s, using flattened filter free beams (FFF). ResultsThe lowest discrepancy between measured and planned dose was obtained using the microDiamond detector in the phase-gated −0.1 (−0.2 to 0) % and −0.3 (−0.4 to −0.1) % in amplitude-gated mode (p < 0.001). Using PinPoint ion chamber, greater discrepancy was obtained in phase-gated −0.5 (−0.7 to −0.3) %, comparing to the amplitude-gated mode −0.1 (−0.2 to 0) % (p < 0.001). The greatest discrepancy was observed using the Semiflex ion chamber, with lesser differences between the phase-gated 1.5 (1.2–1.7) % and amplitude-gated mode 1.4 (1.2–1.5) (p = 0.134). ConclusionDetectors with low discrepancy, PinPoint ion chamber and microDiamond detectors, can be used in routine quality assurance for lung gated delivery in SBRT treatment, for commercial phantom as for phantom developed in-house.

P139. Successful criteria for indirect decompression with lateral lumbar interbody fusion (LLIF)

<h3>BACKGROUND CONTEXT</h3> Lateral lumbar interbody fusion (LLIF) is the procedure that could achieve both fusion and indirect decompression of the neural element. However, no consensus criteria have been established to select ideal candidates for this procedure. Previous studies have reported that additional direct decompression procedures were needed in many cases. In our institution, we developed the criteria to determine whether the patient is suitable for the LLIF with indirect decompression alone. <h3>PURPOSE</h3> The purposes of the study are to (1) determine the success rate following indirect decompression by LLIF with the proposed criteria applied and (2) evaluate risk factors resulting in failure (defined as persistent symptoms requiring subsequent direct decompression of the index level within 6 months after the surgery). <h3>STUDY DESIGN/SETTING</h3> Retrospective cohort study. <h3>PATIENT SAMPLE</h3> Data from 191 consecutive patients (235 fusion levels) who underwent indirect decompression with LLIF from April 2014 to June 2020 was reviewed. Inclusion criteria were patients with lumbar degenerative diseases who underwent 1-2 levels of indirect decompression with LLIF. Exclusion criteria were patients who were preoperatively planned for a combined direct and indirect decompression. <h3>OUTCOME MEASURES</h3> Patients were scheduled for a follow-up examination at 1, 3, 6, 12 and 24 months postoperatively for clinical and radiographic assessment. Demographic data including age, body mass index (BMI), bone mineral density (BMD), preoperative functional and radiographic parameters was collected. Primary outcome is the success rate following indirect decompression with LLIF without need for subsequent direct decompression. Secondary outcomes are clinical outcomes including Oswestry Disability Index (ODI), visual analog scale (VAS) of back and leg pain and radiographic outcomes including central canal diameter, foraminal height, disc height, spinal alignment and cage subsidence. <h3>METHODS</h3> Patients who underwent indirect decompression with LLIF (XLIF or OLIF) at King Chulalongkorn Memorial Hospital between April 2014 to June 2020 were enrolled. In our institution, all patients had to meet all of the following criteria before undergoing indirect decompression with LLIF. 1) Dynamic clinical symptoms (Pain developed when standing or walking but subsided more than 50% when resting in a supine position.) 2) No profound motor weakness (Patients have motor strength gr. < III.) 3) Presence of disc height discrepancy between supine and upright position (correctable disc height in a supine position) 4) No static stenosis such as facet cysts or bony lateral recess. <h3>RESULTS</h3> Of 191 patients, 13 patients (6.8%) required subsequent direct decompression after the index procedure due to persistent pain. Thus, the clinical success rate of the criteria to select surgical candidates was 92.8%. The average patient age was 66.3 years and average follow-up period was 1.21 years. The failure group has significantly lower average BMD T-score (-2.34 vs -0.75; p < 0.05). The degree of preoperative disc height discrepancy between supine and standing position in the failure group was significantly less than the success group (5.9% vs 16.5%; p < 0.05). The low postoperative disc height (< 10.2 mm), use of supplementary fixation with anterolateral plate and high-grade cage subsidence (gr. 2-3) were also identified as the significant risk factors resulting in failure. <h3>CONCLUSIONS</h3> Our study presented the criteria of patient selection to undergo indirect decompression with LLIF which resulted in a 92.8% success rate with satisfactory outcome. Risk factors of reoperation with direct decompression included low BMD, low preoperative disc height discrepancy between standing and supine position, use of supplementary fixation with anterolateral plate, low postoperative disc height and high-grade cage subsidence. Our proposed criteria and reported risk factors may provide a guidance for spine surgeons to select appropriate patients who would achieve successful results following indirect decompression with LLIF and to optimize the risk factors resulting in failure. However, further studies with larger subjects requiring subsequent direct decompression are needed to identify additional significant risk factors. <h3>FDA DEVICE/DRUG STATUS</h3> This abstract does not discuss or include any applicable devices or drugs.

Could ante-mortem computed tomography be useful in forensic pathology of traumatic intracranial haemorrhage?

BackgroundImaging techniques have proven valuable in forensic pathology practice, with computed tomography being preferred for forensic use. In the era of virtual autopsy and a low- to middle-income, resource-constrained country, a question arises as to whether ante-mortem computed tomography (ACT) could be cost-effective by reducing the number of invasive autopsies performed.ObjectiveThe objective of this study was to assess the usefulness of ACT in forensic pathology by examining discrepancy rates between ACT scans and autopsy findings in cases of deceased individuals with traumatic intracranial haemorrhages and assess factors associated with discrepancies.MethodsEighty-five cases of ACT and autopsy reports from 01 January 2014 to 31 December 2016 from the Polokwane Forensic Pathology Laboratory, South Africa, were analysed retrospectively. Using Cohen’s kappa statistics, measures of agreement and resultant discrepancy rates were determined. Also, the discrepancy patterns for each identified factor was also analysed.ResultsThe discrepancy rate between ACT and autopsy detection of haemorrhage was 24.71% while diagnostic categorisation of haemorrhage was 55.3%. Classification discrepancy was most observed in subarachnoid haemorrhages and least observed in extradural haemorrhages. A markedly reduced level of consciousness, hospital stay beyond two weeks and three or fewer years of doctors’ experience contributed to classification discrepancies.ConclusionAnte-mortem computed tomography should be used only as an adjunct to autopsy findings. However, the low discrepancy rate seen for extradural haemorrhages implies that ACT may be useful in the forensic diagnosis of extradural haemorrhages.