Quality Metrics Research Articles

Quality of care assessment for non-small cell lung cancer patients: transforming routine care data into a continuous improvement system.

The complexity of cancer care requires planning and analysis to achieve the highest level of quality. We aim to measure the quality of care provided to patients with non-small cell lung cancer (NSCLC) using the data contained in the hospital's information systems, in order to establish a system of continuous quality improvement. Retrospective observational cohort study conducted in a university hospital in Spain, consecutively including all patients with NSCLC treated between 2016 and 2020. A total of 34 quality indicators were selected based on a literature review and clinical practice guideline recommendations, covering care processes, timeliness, and outcomes. Applying data science methods, an analysis algorithm, based on clinical guideline recommendations, was set up to integrate activity and administrative data extracted from the Electronic Patient Record along with clinical data from a lung cancer registry. Through data generated in routine practice, it has been feasible to reconstruct the therapeutic trajectory and automatically calculate quality indicators using an algorithm based on clinical practice guidelines. Process indicators revealed high adherence to guideline recommendations, and outcome indicators showed favorable survival rates compared to previous data. Our study proposes a methodology to take advantage of the data contained in hospital information sources, allowing feedback and repeated measurement over time, developing a tool to understand quality metrics in accordance with evidence-based recommendations, ultimately seeking a system of continuous improvement of the quality of health care.

Deep multimodal saliency parcellation of cerebellar pathways: Linking microstructure and individual function through explainable multitask learning.

Parcellation of human cerebellar pathways is essential for advancing our understanding of the human brain. Existing diffusion magnetic resonance imaging tractography parcellation methods have been successful in defining major cerebellar fibre tracts, while relying solely on fibre tract structure. However, each fibre tract may relay information related to multiple cognitive and motor functions of the cerebellum. Hence, it may be beneficial for parcellation to consider the potential importance of the fibre tracts for individual motor and cognitive functional performance measures. In this work, we propose a multimodal data-driven method for cerebellar pathway parcellation, which incorporates both measures of microstructure and connectivity, and measures of individual functional performance. Our method involves first training a multitask deep network to predict various cognitive and motor measures from a set of fibre tract structural features. The importance of each structural feature for predicting each functional measure is then computed, resulting in a set of structure-function saliency values that are clustered to parcellate cerebellar pathways. We refer to our method as Deep Multimodal Saliency Parcellation (DeepMSP), as it computes the saliency of structural measures for predicting cognitive and motor functional performance, with these saliencies being applied to the task of parcellation. Applying DeepMSP to a large-scale dataset from the Human Connectome Project Young Adult study (n = 1065), we found that it was feasible to identify multiple cerebellar pathway parcels with unique structure-function saliency patterns that were stable across training folds. We thoroughly experimented with all stages of the DeepMSP pipeline, including network selection, structure-function saliency representation, clustering algorithm, and cluster count. We found that a 1D convolutional neural network architecture and a transformer network architecture both performed comparably for the multitask prediction of endurance, strength, reading decoding, and vocabulary comprehension, with both architectures outperforming a fully connected network architecture. Quantitative experiments demonstrated that a proposed low-dimensional saliency representation with an explicit measure of motor versus cognitive category bias achieved the best parcellation results, while a parcel count of four was most successful according to standard cluster quality metrics. Our results suggested that motor and cognitive saliencies are distributed across the cerebellar white matter pathways. Inspection of the final k = 4 parcellation revealed that the highest-saliency parcel was most salient for the prediction of both motor and cognitive performance scores and included parts of the middle and superior cerebellar peduncles. Our proposed saliency-based parcellation framework, DeepMSP, enables multimodal, data-driven tractography parcellation. Through utilising both structural features and functional performance measures, this parcellation strategy may have the potential to enhance the study of structure-function relationships of the cerebellar pathways.

Association of county-level provider density with hepatobiliary cancer incidence and mortality.

Access to healthcare providers is a key factor in reducing cancer incidence and mortality, underscoring the significance of provider density as a crucial metric of health quality. We sought to characterize the association of provider density on hepatobiliary cancer population-level incidence and mortality. County-level hepatobiliary cancer incidence and mortality data from 2016 to 2020 and provider data from 2016 to 2018 were obtained from the CDC and Area Health Resource File. Multivariable logistic regression was utilized to evaluate the relationship between provider density and hepatobiliary cancer incidence and mortality. Among 1359 counties, 851 (62.6%) and 508 (37.4%) counties were categorized as urban and rural, respectively. The median number of providers in any given county was 104 (IQR: 44-306), while provider density was 120.1 (IQR: 86.7-172.2) per 100,000 population; median household income was $51,928 (IQR: $45,050-$61,655). Low provider-density counties were more likely to have a greater proportion of residents over 65years of age (52.7% vs. 49.6%) who were uninsured (17.4% vs. 13.2%) versus higher provider-density counties (p<0.05). Moreover, all-stage incidence, late-stage incidence, and mortality rates were higher in counties with low provider density. On multivariable analysis, moderate, and high provider density were associated with lower odds of all-stage incidence, late-stage incidence, and mortality. Higher county-level provider density was associated with lower hepatobiliary cancer-related incidence and mortality. Efforts to increase access to healthcare providers may improve healthcare equity as well as long-term cancer outcomes.

Quality, shelf-life of broiler fillets dipped in clove oil compared to peroxyacetic acid and chlorine and their consequences on inoculated Salmonella enterica

The study aimed to evaluate the decontamination efficacy of clove oil on broiler Pectoralis major fillets natural spoilage-flora and Salmonella contamination compared to chlorine and a commercial triple-mixture of peroxyacetic acid (PAA). Furthermore, the intervention’s impact on breast fillet shelf-life and physicochemical quality was investigated. Thirty-six fillets (fillets n = 3 × storage-period n = 6 × replication n = 2) were dipped in one of four treatments: sterile water (control), chlorine (50 ppm), clove (1.5%) or PAA (225 ppm). For Salmonella-inoculated experiment, 18 fillets were dipped per treatment (fillet n = 3 × storage-period n = 6). Overall, the PAA retarded most spoilage microflora and Salmonella growth, particularly aerobic plate count, below the unacceptable spoilage level of 6 log CfU/g for 9 chilling-days. However, specific meat quality metrics, particularly oxidative stability, were negatively impacted by PAA. Clove exhibited a lower and/or similar, but delayed, antibacterial effect on spoilage-flora and Salmonella levels than PAA, along with an impressive antioxidant protective effect on fillets attributes, particularly lightness and appearance. Chlorine at the current recommended dose did not extend shelf-life beyond six days or reduce Salmonella growth, although it generated lower yellowness, higher redness, and tenderness scores than other treatments. Treatments did not prolong the fillets shelf-life beyond 6 cooling-days due to oxidizing properties of PAA, diminished chlorine concentration, and shorter contact time of clove. Cloves could therefore be sprayed in another step before final packaging to extend contact-time and boost antioxidant and antibacterial barriers. Lower doses and shorter exposure durations seem desirable and could contribute to minimize the PAA negative oxidative effect on meat quality parameters.Article HighlightsNatural and chemical antimicrobial dippings can lower broiler initial bacteria load but do not boost meat shelf life.The PAA strong antimicrobial benefits are hindered by negative oxidative impacts on the quality of broiler fillets.Pre-packaging clove application potentially maximizes preservative benefits on fillets and reducing dipping obstacles.Graphical

Retraction Note: A copyright protection using watermarking scheme based on nonlinear permutation and its quality metrics

Retraction Note: A copyright protection using watermarking scheme based on nonlinear permutation and its quality metrics

Many-Objective Grasshopper Optimization Algorithm (MaOGOA): A New Many-Objective Optimization Technique for Solving Engineering Design Problems

In metaheuristic multi-objective optimization, the term effectiveness is used to describe the performance of a metaheuristic algorithm in achieving two main goals—converging its solutions towards the Pareto front and ensuring these solutions are well-spread across the front. Achieving these objectives is particularly challenging in optimization problems with more than three objectives, known as many-objective optimization problems. Multi-objective algorithms often fall short in exerting adequate selection pressure towards the Pareto front in these scenarios and difficult to keep solutions evenly distributed, especially in cases with irregular Pareto fronts. In this study, the focus is on overcoming these challenges by developing an innovative and efficient a novel Many-Objective Grasshopper Optimisation Algorithm (MaOGOA). MaOGOA incorporates reference point, niche preserve and information feedback mechanism (IFM) for superior convergence and diversity. A comprehensive array of quality metrics is utilized to characterize the preferred attributes of Pareto Front approximations, focusing on convergence, uniformity and expansiveness diversity in terms of IGD, HV and RT metrics. It acknowledged that MaOGOA algorithm is efficient for many-objective optimization challenges. These findings confirm the approach effectiveness and competitive performance. The MaOGOA efficiency is thoroughly examined on WFG1-WFG9 benchmark problem with 5, 7 and 9 objectives and five real-world (RWMaOP1- RWMaOP5) problem, contrasting it with MaOSCA, MaOPSO, MOEA/DD, NSGA-III, KnEA, RvEA and GrEA algorithms. The findings demonstrate MaOGOA superior performance against these algorithms.

An Image-Domain Deep-Learning Denoising Technique for Accelerated Parallel Brain MRI: Prospective Clinical Evaluation

Abstract Background Parallel imaging can accelerate MRI acquisitions, but excessive accelerations can introduce amplified noise and aliasing artifacts. Purpose To evaluate a vendor-agnostic AI-based approach to remove image degradation artifacts in highly-accelerated MRI scans, improving image quality and reducing scan time. Materials and Methods Training was performed by retrospectively degrading standard accelerated images. Evaluation was performed with both retrospective and prospectively-collected highly-accelerated images. Retrospective data were taken from ∼2000 MRI studies obtained between August 2016 and October 2022, and prospective data were collected in &amp;gt; 200 subjects between June and November 2022, using scanners from multiple vendors and locations. Scan time data were collected from prospective studies and used to compute time savings per sequence and per protocol for each vendor. Images were evaluated qualitatively by five board-certified radiologists and quantitatively by assessing noise, contrast and spatial resolution. Paired Wilcoxon signed-rank tests were used to compare model outputs to model inputs and low-acceleration images. Results Images from 101 adults from 5 sites and 6 scanner models from different vendors were enrolled. 89% of imaged subjects had noteworthy imaging features or pathology. Model outputs were rated superior to model inputs (p &amp;lt; 0.001) and most were either non-inferior (p inf &amp;gt; 0.05) or superior (p sup &amp;lt; 0.05) to baseline images in qualitative metrics of image quality and feature visibility. Quantitative evaluation of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) improved for model outputs compared to inputs (p &amp;lt; 0.001) or baseline images (p &amp;lt; 0.005). Apparent resolution measured using the full width at half maximum or minimum (FWHM) was either enhanced (p sup &amp;lt; 0.05) or preserved (non-superior p sup &amp;gt; 0.05 and non-inferior p inf &amp;gt; 0.05). The scan time was reduced by an average of 29% (19%-41% per sequence). Conclusion This vendor-agnostic AI-based method achieved robust scan time savings without loss of image quality, potentially allowing for reduced cost and improved patient experience.

Grouping and scheduling multiple sports leagues: an integrated approach

This paper introduces the multi-league grouping and scheduling problem, which integrates the grouping of teams into leagues and the scheduling of each league. This involves two possibly conflicting objectives: minimizing travel distance and minimizing capacity violations of venues shared by teams. We formulate this problem as a bi-objective mixed-integer programming model. Given the NP-hardness of the grouping problem, the integrated problem is particularly challenging. Hence, we design a two-layer constructive heuristic to efficiently approximate the Pareto set, using simulated annealing on the outer layer and an integer programming model on the inner layer. We further develop a speed-up version where the inner layer is solved heuristically. We develop a series of large-scale problem instances, including one based on data from the Royal Belgian Football Association. In a computational study, we compare our algorithms with an epsilon-constraint method and evaluate their results using various multi-objective solution quality metrics.

Evidence for the Positive Impact of Centralization in Esophageal Cancer Surgery.

In this study we analyzed the impact of centralization on key metrics, outcomes and patterns of care at the Irish National Center. Overall survival rates in esophageal cancer in the West have doubled in the last 25 years. An international trend towards centralization may be relevant, however this model remains controversial with Ireland, centralizing esophageal cancer surgery in 2011. All patients (n=1245) with adenocarcinoma of the esophagus or junction treated with curative intent involving surgery, including endoscopic surgery, were included (n= 461 from 2000-2011, and 784 from 2012-2022). All data entry was prospectively recorded. Overall survival was measured (i) for the entire cohort; (ii) patients with locally advanced disease (cT2-3N0-3); and (iii) patients undergoing neoadjuvant therapy. All complications were recorded as per Esophageal Complication Consensus Group (ECCG) definitions, and the Clavien Dindo (CD) severity classification. Data were analyzed using GraphPad Prism (v.6.0) for Windows and SPSS (v.23.0) software (SPSS,Chicago,IL) RStudio (Rversion4.2.2). Survival times were calculated using log-rank test and a Cox-regression analysis, and Kaplan-Meier curves generated. Endotherapy for cT1a/IMC adenocarcinoma increased from 40 (9% total) to 245 (31% total) procedures between the pre-centralization (pre-C) and post-centralization (post-C) periods. A significantly (P<0.001) higher proportion of patients with cT2-3N0-3 disease in the post-C period underwent neoadjuvant therapy (66% vs 53%). Operative mortality was lower (P=0.02) post-C, at 2% vs 4.5%, and>IIIa CD major complications decreased from 33% to 25% (P<0.01). Recurrence rates were lower post-C (38% vs 53%, P<0.01). Median overall survival was 73.83 versus 47.23 months in the 2012-22 and 2000-11 cohorts respectively (P<0.001). For those who received neoadjuvant therapy, the median survival was 28.5 months pre-C and 42.5 months post-C (P<0.001). These data highlight improvements in both operative outcomes and survival from the time of centralization, and a major expansion of endoscopic surgery. Although not providing proof, the study suggests a positive impact of formal centralization with governance on key quality metrics, and an evolution in patterns of care.

Optically stimulated luminescence detectors for LET determination and dosimetry in ion beam therapy

Optically stimulated luminescence detectors (OSLDs) have been utilized for various dosimetry applications for many years. The use of Al2O3:C OSLDs for proton dosimetry began over a decade ago, taking advantage of the correlation between the ionization density of the radiation field and the ratio of intensities of the material’s two emission bands. The correlation allows for determining both linear energy transfer (LET) and dose in proton beams, with corrections for ionization quenching derived from the LET. However, the previous methodology for proton dosimetry and simultaneous LET determination with Al2O3:C OSLDs was cumbersome and occasionally associated with large uncertainties, while carbon beam dosimetry posed further challenges due to an elevated LET.This paper reviews the recent advancements in ion beam dosimetry and LET determination using OSLDs. Employing Al2O3:C,Mg OSLDs alongside improved, automatized read-out techniques, and the use of other radiation quality metrics than averaged LET, has removed most of the previous obstacles for ion beam dosimetry with OSLDs.The feasibility of simultaneous LET determination and dosimetry in ion beams is demonstrated through two case studies involving realistic proton and carbon ion therapy scenarios.

National 30-Day Readmission Trends in IBD 2014-2020-Are We Aiming for Improvement?

Background: Inflammatory bowel disease (IBD) prevalence in Eastern Europe is increasing. The 30-day readmission rate is a crucial quality metric in healthcare, reflecting the effectiveness of initial treatment and the continuity of care post-discharge; however, such parameters are rarely analyzed. The aim of this study was to explore the trends in 30-day readmissions among patients with inflammatory bowel disease in Latvia between 2014 and 2020. Methods: This is a retrospective trends study in IBD-ulcerative colitis and Crohn's disease (UC and CD)-patients in Latvia between 2014 and 2020, involving all IBD patients identified in the National Health service database in the International Classification of Diseases-10 (ICD) classification (K50.X and K51.X) and having at least one prescription for IBD diagnoses. We assessed all IBD-related hospitalizations (discharge ICD codes K50X and K51X), as well as hospitalizations potentially related to IBD comorbidities. We analyzed hospitalization trends and obtained the 30 day all-cause readmission rate, disease specific readmission rate and readmission proportion for specific calendar years. Trends in readmissions and the mean length of stay (LOS) for CD and UC were calculated. Results: Despite a decrease in admission rates observed in 2020, the total number of readmissions for CD and UC has increased. Female patients prevailed through the study period and were significantly older than male patients in both the CD and UC groups, p < 0.05. We noted that there was no trend for 30 day all-cause readmission rate for CD (p > 0.05); however, there was a statistically significant trend for 30 day all-cause readmission for UC patients (p-trend = 0.018) in the period from 2014 to 2019. There was a statistically significant trend for CD-specific readmission rate (p < 0.05); however, no statistically significant trend was observed for UC-specific readmission (p > 0.05). An exploratory analysis did not reveal any statistically significant differences between treated and not-treated IBD patients (p > 0.05). The increasing trend is statistically significant over the period 2014-2018 (p < 0.05); however, the trend interrupts in 2020, which can be associated with the COVID-19 global pandemic and the related changes in admission flows where the gastroenterology capacity was reallocated to accommodate increasing numbers of COVID-19 patients. More studies are needed to evaluate the long-term impact of COVID-19 pandemic and 30-day readmissions. No significant dynamics were observed in the mean total hospitalization costs over the 2014-2020 period.

Changes in quality attributes of coconut water treated with UV-radiation and nisin during cold storage: Kinetics modelling and shelf-life prediction

Exploring the innovative combination of UV radiation and nisin treatment for enhancing the shelf life and quality of coconut water (CW) offers promising avenues for sustainable beverage processing. This study investigated the effects of UV radiation, nisin, and their combined treatments on the physical and microbiological attributes of coconut water during 20 days of refrigerated storage. The study also aimed to develop mathematical models for tracking changes in coconut water's quality parameters during refrigerated storage and determining its shelf life by integrating quality and microbial metrics. The results revealed that the cold storage period has significant impacts on the physical and microbiological characteristics of coconut water. The combination of UV radiation and nisin resulted in microbiologically stable coconut water, with populations remaining below 106 CFU/mL, for up to 20 days of cold storage. Moreover, a decline in pH over time, particularly in UV and nisin-treated samples, indicated increased acidity by only 15%. Colour analysis showed shifts towards darker and redder hues, accompanied by increases in a*-values. Conversely, b*-values increased in most samples, except for pasteurized coconut water. Additionally, a consistent decrease in antioxidant activity, total phenolic compound, and total flavonoid content was observed in the samples treated with the combination of UV radiation and nisin during storage period, with averages of 50%, 18%, and 20%, respectively. The zero-order model accurately represented most coconut water's properties, exhibiting R2 value of ≥0.85, and the derived reaction rate constants used to predict shelf life. Integration of UV and nisin treatment significantly extended the projected shelf life of coconut water compared to control (untreated CW sample) and individual treatments except thermal treatment, with shelf-life extensions of up to 19.5 days, highlighting the potential of this combination to enhance coconut water's shelf life while maintaining its safety.

Medical Tourism Management

Medical tourism, the practice of traveling abroad for medical treatment, has gained substantial attention in recent years due to its implications for healthcare delivery and tourism industry dynamics. This paper investigates the management strategies employed in medical tourism destinations, focusing on enhancing healthcare services and tourism competitiveness. Utilizing secondary data from scholarly articles, reports, and industry publications, the research synthesizes existing knowledge on medical tourism management frameworks, destination competitiveness factors, patient experiences, and healthcare quality metrics. The study employs a qualitative analysis approach to distill key insights and patterns from the literature, shedding light on effective management practices and their impact on patient outcomes and destination attractiveness. Findings reveal the importance of strategic planning, collaboration between healthcare providers and tourism stakeholders, quality assurance mechanisms, and marketing strategies in optimizing medical tourism experiences. The paper discusses implications for policy development, industry regulations, and sustainable growth strategies in medical tourism destinations. By leveraging secondary data sources, this research contributes to a comprehensive understanding of medical tourism management dynamics, offering practical insights for healthcare providers, policymakers, and tourism professionals navigating this evolving landscape. Keywords- Medical tourism, Management strategies, Destination competitiveness, Healthcare quality

Deep Learning based DWT- Bi-LSTM Classifier for Enhanced Cardiovascular Arrhythmia Classification

Nowadays heart diseases and their diagnosis have emerged as a prominent subject in health care systems, given that the heart performs a crucial role in the human body. Several computational techniques have been explored for the recognition and classification of cardiac diseases using Electrocardiogram (ECG) signals. Deep Learning (DL) is a present focus in healthcare solicitations, particularly in the classification of heartbeats in ECG signals. Many studies have utilized dissimilar DL models, including RNN (Recurrent Neural Networks), GRU (Gated Recurrent Unit), and CNN (Convolutional Neural Networks), to classify heartbeats using the MIT-BIH arrhythmia dataset. This article presents a methodical exploration of Bi-LSTM (Bi directional Long Short-Term Memory) based DL models for heartbeat classification using various quality metrics. Proposed variants include the Bi-LSTM model, demonstrating remarkable accuracy in classifying the heartbeats into five classes: Normal (N) beat, Supraventricular (S) beat, Ventricular contraction (V), Fusion beats (F), and Unclassifiable Beat (Q). The proposed technique outperforms present classifiers with Accuracy, Sensitivity, Specificity, and F1 score values of 98%, 96.9%, 97.4%, and 97.5% respectively. The simulations are conducted using MATLAB 2020a.

Associations of residential greenness with behavioural, physical, and mental health: a Hong Kong study during the fifth wave of COVID-19 pandemic

ABSTRACT Exposure to greenspace is protective of physical and mental health, but its role during the COVID-19 pandemic is unclear. We examined cross-sectional associations of residential greenness with behavioural, physical, and mental health outcomes during the fifth wave of COVID-19 in Hong Kong. A questionnaire of n = 160 participants assessed frequency of greenspace visits, physical activity from the International Physical Activity Questionnaire, and physical and mental health based on the 12-item Short-Form Health Survey. Residential greenspace was measured in terms of number of greenspace within a 400-m buffer, proximity to greenspace, and Landsat-derived Normalised Difference Vegetation Index as a metric of quality. Residing in higher greenness tertiles within 400 m was associated with higher odds of at least weekly visits to greenspace (OR = 3.23, 95% CI: 1.20-8.69 for the middle and OR = 5.62; 95% CI: 2.08-15.21 for highest tertiles) relative to the lowest exposure tertile. The count of residential greenspace was protectively associated with physical and mental health outcomes. The beneficial association between greenness and weekly usage remained robust at 500 m and was significant only among private housing residents. Policies towards scaling up and optimising residential greenness may constitute important public health interventions for enhancing population-level health and resilience during societal shocks

PointPCA: point cloud objective quality assessment using PCA-based descriptors

Point clouds denote a prominent solution for the representation of 3D photo-realistic content in immersive applications. Similarly to other imaging modalities, quality predictions for point cloud contents are vital for a wide range of applications, enabling trade-off optimizations between data quality and data size in every processing step from acquisition to rendering. In this work, we focus on use cases that consider human end-users consuming point cloud contents and, hence, we concentrate on visual quality metrics. In particular, we propose a set of perceptually relevant descriptors based on principal component analysis (PCA) decomposition, which is applied to both geometry and texture data for full-reference point cloud quality assessment. Statistical features are derived from these descriptors to characterize local shape and appearance properties for both a reference and a distorted point cloud. The extracted statistical features are subsequently compared to provide corresponding predictions of visual quality for the distorted point cloud. As part of our method, a learning-based approach is proposed to fuse these individual predictors to a unified perceptual score. We validate the accuracy of the individual predictors, as well as the unified quality scores obtained after regression against subjectively annotated datasets, showing that our metric outperforms state-of-the-art solutions. Insights regarding design decisions are provided through exploratory studies, evaluating the performance of our metric under different parameter configurations, attribute domains, color spaces, and regression models. A software implementation of the proposed metric is made available at the following link: https://github.com/cwi-dis/pointpca.

Quantitative metrics of bone quality determined at the distal radius using photon-counting CT.

To determine the accuracy of photon-counting-detector CT (PCD-CT) at deriving bone morphometric indices and demonstrate utility in vivo in the distal radius. Ten cadaver wrists were scanned using PCD-CT and high-resolution peripheral quantitative CT (HRpQCT). Correlation between PCD-CT and HRpQCT morphometric indices was determined. Agreement was assessed by Lin's concordance correlation coefficient (Lin's CCC). Wrist PCD-CTs of patients between 02/2022 and 08/2023 were also evaluated for clinical utility. Morphometric indices of the in vivo distal radii were extracted and compared between patients with or without osteoporosis. In cadavers, strong correlation between PCD-CT and HRpQCT was observed for cortical thickness (Spearman correlation, ρ, 0.85), trabecular spacing (ρ = 0.98), and trabecular bone volume fraction (ρ = 0.68). Moderate negative correlation (ρ = - 0.49) was observed for trabecular thickness. PCD-CT shows good agreement to HRpQCT for cortical thickness, trabecular spacing, and trabecular bone volume fraction (Lin's CCC = 0.80, 0.94, and 0.86, respectively) but poor agreement (Lin's CCC = - 0.1) for trabecular thickness. In forty participants (31 adults and 9 pediatric), bone morphometrics indices for cortical thickness, trabecular thickness, trabecular spacing, and trabecular bone volume fraction were 0.99mm (IQR, 0.89-1.06), 0.38mm (IQR, 0.25-0.40), 0.82mm (IQR, 0.72-1.05), and 0.28 (IQR, 0.25-0.33), respectively. Patients with osteoporosis had statistically significantly larger trabecular spacing (p = 0.025) and lower trabecular volumetric bone mineral density (p = 0.042). This study demonstrates the agreement of PCD-CT to HRpQCT in cadavers of most cortical and bone morphometrics examined and provide in vivo quantitative metrics of bone microarchitecture from routine clinical PCD-CT images of the distal radius.

Optimizing Camera Exposure Time for Automotive Applications.

Camera-based object detection is integral to advanced driver assistance systems (ADAS) and autonomous vehicle research, and RGB cameras remain indispensable for their spatial resolution and color information. This study investigates exposure time optimization for such cameras, considering image quality in dynamic ADAS scenarios. Exposure time, the period during which the camera sensor is exposed to light, directly influences the amount of information captured. In dynamic scenarios, such as those encountered in typical driving scenarios, optimizing exposure time becomes challenging due to the inherent trade-off between Signal-to-Noise Ratio (SNR) and motion blur, i.e., extending exposure time to maximize information capture increases SNR, but also increases the risk of motion blur and overexposure, particularly in low-light conditions where objects may not be fully illuminated. The study introduces a comprehensive methodology for exposure time optimization under various lighting conditions, examining its impact on image quality and computer vision performance. Traditional image quality metrics show a poor correlation with computer vision performance, highlighting the need for newer metrics that demonstrate improved correlation. The research presented in this paper offers guidance into the enhancement of single-exposure camera-based systems for automotive applications. By addressing the balance between exposure time, image quality, and computer vision performance, the findings provide a road map for optimizing camera settings for ADAS and autonomous driving technologies, contributing to safety and performance advancements in the automotive landscape.

Towards unified reporting of genome sequencing results in clinical microbiology.

Whole genome sequencing (WGS) has become a vital tool in clinical microbiology, playing an important role in outbreak investigations, molecular surveillance, and identification of bacterial species, resistance mechanisms and virulence factors. However, the complexity of WGS data presents challenges in interpretation and reporting, requiring tailored strategies to enhance efficiency and impact. This study explores the diverse needs of key stakeholders in healthcare, including clinical management, laboratory work, public surveillance and epidemiology, infection prevention and control, and academic research, regarding WGS-based reporting of clinically relevant bacterial species. In order to determine preferences regarding WGS reports, human-centered design approach was employed, involving an online survey and a subsequent workshop with stakeholders. The survey gathered responses from 64 participants representing the above mentioned healthcare sectors across geographical regions. Key findings include the identification of barriers related to data accessibility, integration with patient records, and the complexity of interpreting WGS results. As the participants designed their ideal report using nine pre-defined sections of a typical WGS report, differences in needs regarding report structure and content across stakeholders became evident. The workshop discussions further highlighted the need to feature critical findings and quality metrics prominently in reports, as well as the demand for flexible report designs. Commonalities were observed across stakeholder-specific reporting templates, such as the uniform ranking of certain report sections, but preferences regarding the depth of content within these sections varied. Using these findings, we suggest stakeholder-specific structures which should be considered when designing customized reporting templates. In conclusion, this study underscores the importance of tailoring WGS-based reports of clinically relevant bacteria to meet the distinct needs of diverse healthcare stakeholders. The evolving landscape of digital reporting increases the opportunities with respect to WGS reporting and its utility in managing infectious diseases and public health surveillance.

Fish Growth, Diversity, and Water Quality Metrics at Chashma Barrage, Pakistan

Fish Growth, Diversity, and Water Quality Metrics at Chashma Barrage, Pakistan