Reduction In Time Research Articles

Accelerated cardiac magnetic resonance imaging using deep learning for volumetric assessment in children.

Ventricular volumetry using a short-axis stack of two-dimensional (D) cine balanced steady-state free precession (bSSFP) sequences is crucial in any cardiac magnetic resonance imaging (MRI) examination. This task becomes particularly challenging in children due to multiple breath-holds. To assess the diagnostic performance of accelerated 3-RR cine MRI sequences using deep learning reconstruction compared with standard 2-D cine bSSFP sequences. Twenty-nine consecutive patients (mean age 11 ± 5, median 12, range 1-17years) undergoing cardiac MRI were scanned with a conventional segmented 2-D cine and a deep learning accelerated cine (three heartbeats) acquisition on a 1.5-tesla scanner. Short-axis volumetrics were performed (semi-)automatically in both datasets retrospectively by two experiencedreaderswho visually assessed image quality employing a 4-point grading scale. Scan times and image quality were compared using the Wilcoxon rank-sum test. Volumetrics were assessed with linear regression and Bland-Altman analyses, and measurement agreement with intraclass correlation coefficient (ICC). Mean acquisition time was significantly reduced with the 3-RR deep learning cine compared to the standard cine sequence (45.5 ± 13.8s vs. 218.3 ± 44.8s; P < 0.001). No significant differences in biventricular volumetrics were found. Left ventricular (LV) mass was increased in the deep learning cine compared with the standard cine sequence (71.4 ± 33.1g vs. 69.9 ± 32.5g; P < 0.05). All volumetric measurements had an excellent agreement with ICC > 0.9 except for ejection fraction (EF) (LVEF 0.81, RVEF 0.73). The image quality of deep learning cine images was decreased for end-diastolic and end-systolic contours, papillary muscles, and valve depiction (2.9 ± 0.5 vs. 3.5 ± 0.4; P < 0.05). Deep learning cine volumetrics did not differ significantly from standard cine results except for LV mass, which was slightly overestimated with deep learning cine. Deep learning cine sequences result in a significant reduction in scan time with only slightly lower image quality.

Imaging method of surface defect using leaky Rayleigh wave with ultrasonic phased array

ABSTRACT Leaky Rayleigh wave testing has the advantages of non-contact, high sensitivity, and good beam directivity. However, current leaky Rayleigh wave testing mostly uses monolithic transducers and is seriously affected by acoustic wave attenuation and diffusion, resulting in poor image quality, limited detection range and low imaging efficiency. In this paper, a novel leaky Rayleigh wave testing method using an ultrasonic phased array is developed, and the total focusing method (TFM) is used to achieve high-quality imaging of defects. To improve imaging efficiency, the root-mean-square velocity (RMSV) model is utilised to rapidly acquire an approximate analytical solution for the time of flight of the acoustic wave. To obtain more accurate defect images further, the amplitude of the total focusing imaging signal is calibrated. The experimental results demonstrate that the proposed method achieves a 48% improvement in the imaging accuracy of a 1 mm hole, compared to the traditional Synthetic Aperture Focusing Technique (SAFT) imaging method. The proposed method achieves an 92% reduction in imaging time compared to the conventional TFM. The proposed method provides a new non-contact approach for high-accuracy and high-efficiency defect imaging using leaky Rayleigh wave.

Optical characterizations of densely doped Tm3+:KYW crystals at low temperatures

We investigate the optical lifetime, decay characteristics, spectral linewidth and energy level properties of thulium ions doped in a KY(WO 4 ) 2 crystal at 4 K temperature. High doping concentration of thulium ions with inhomogeneous broadening allow us to study nonradiative behaviors, instantaneous spectral diffusion, and spectral power broadening in this solid-state material. The theoretical consideration of ion–ion interactions is shown to accurately characterize the absorption, decay and other spectral behaviors of Tm 3+ ions. We observe more than ten-fold reduction in the decay time of 3H4 state and about three-fold reduction in the spectral-hole lifetime as we approach the center of the inhomogeneous broadening, corresponding to higher optical densities.

The incremental design of a machine learning framework for medical records processing.

This work presents the development and evaluation of coordn8, a web-based application that streamlines fax processing in outpatient clinics using a "human-in-the-loop" machine learning framework. We demonstrate the effectiveness of the platform at reducing fax processing time and producing accurate machine learning inferences across the tasks of patient identification, document classification, spam classification, and duplicate document detection. We deployed coordn8 in 11 outpatient clinics and conducted a time savings analysis by observing users and measuring fax processing event logs. We used statistical methods to evaluate the machine learning components across different datasets to show generalizability. We conducted a time series analysis to show variations in model performance as new clinics were onboarded and to demonstrate our approach to mitigating model drift. Our observation analysis showed a mean reduction in individual fax processing time by 147.5 s, while our event log analysis of over 7000 faxes reinforced this finding. Document classification produced an accuracy of 81.6%, patient identification produced an accuracy of 83.7%, spam classification produced an accuracy of 98.4%, and duplicate document detection produced a precision of 81.0%. Retraining document classification increased accuracy by 10.2%. coordn8 significantly decreased fax-processing time and produced accurate machine learning inferences. Our human-in-the-loop framework facilitated the collection of high-quality data necessary for model training. Expanding to new clinics correlated with performance decline, which was mitigated through model retraining. Our framework for automating clinical tasks with machine learning offers a template for health systems looking to implement similar technologies.

Reduced noise in the emergency department: the impact on staff well-being and room acoustics.

The ED is often perceived as noisy. Excessive noise has deleterious effects on health and productivity. This study evaluated if a package of noise-reducing interventions altered workload, physical complaints, productivity and room acoustics. This was an observational pre-post implementation study. It was performed in our non-academic ED in the Netherlands from July 2021 to April 2022. Our primary objective was to determine if a combination of technical, acoustical and behavioural interventions was associated with improved staff well-being, and the secondary objective was to evaluate if these interventions resulted in better room acoustics. Moreover, the correlation of noise sensitivity with staff well-being and its effect on interventions were evaluated. All ED staff that were sufficiently exposed to the interventions received questionnaires to assess their well-being before and after the interventions. Room acoustics before and after interventions were expressed in reverberation time (seconds) and participant ratings are reflected as the mean of the sum of their Likert scale ratings. 43 participants were included. At baseline, individual noise sensitivity was significantly correlated with physical complaints (r=0.409, p=0.006) and productivity (r=0.399, p=0.008). After the interventions, a reduction in reverberation time was reached in the central ED area (0.49 s, SD 0.06 s vs 0.39 s, SD 0.05; p<0.001). Participants experienced significantly less noise disturbance during work (sum score 7.28 vs 3.19; p<0.001). Productivity and physical complaints improved significantly (sum score 4.81 vs 2.70; p<0.001 and sum score -3.74 vs -8.14; p<0.001, respectively). Participants also showed a perceived change in behaviour (sum score -2.00 vs -4.70; p<0.001). There was no confounding by noise sensitivity nor age. The package of behavioural, acoustical and technical interventions was associated with increased staff well-being, reflected by decreased perception of noise, increased productivity, decreased physical complaints and observable changes in behaviour. Furthermore, the interventions positively influenced the room acoustics.

Deep learning-based cell segmentation for rapid optical cytopathology of thyroid cancer

Fluorescence polarization (Fpol) imaging of methylene blue (MB) is a promising quantitative approach to thyroid cancer detection. Clinical translation of MB Fpol technology requires reduction of the data analysis time that can be achieved via deep learning-based automated cell segmentation with a 2D U-Net convolutional neural network. The model was trained and tested using images of pathologically diverse human thyroid cells and evaluated by comparing the number of cells selected, segmented areas, and Fpol values obtained using automated (AU) and manual (MA) data processing methods. Overall, the model segmented 15.8% more cells than the human operator. Differences in AU and MA segmented cell areas varied between − 55.2 and + 31.0%, whereas differences in Fpol values varied from − 20.7 and + 10.7%. No statistically significant differences between AU and MA derived Fpol data were observed. The largest differences in Fpol values correlated with greatest discrepancies in AU versus MA segmented cell areas. Time required for auto-processing was reduced to 10 s versus one hour required for MA data processing. Implementation of the automated cell analysis makes quantitative fluorescence polarization-based diagnosis clinically feasible.

“Under the hood”: artificial intelligence in personalized radiotherapy

Abstract This review presents and discusses the ways in which artificial intelligence (AI) tools currently intervene, or could potentially intervene in the future, to enhance the diverse tasks involved in the radiotherapy workflow. The radiotherapy framework is presented on two different levels for the personalization of the treatment, distinct in tasks and methodologies. The first level is the clinically well-established anatomy-based workflow, known as adaptive radiation therapy (ART). The second level is referred to as biology-driven workflow, explored in research literature and recently appearing in some preliminary clinical trials for personalized radiation treatments. A twofold role for AI is defined according to these two different levels. In the anatomy-based workflow, the role of AI is to streamline and improve the tasks in terms of time and variability reductions compared to conventional methodologies. The biology-driven workflow instead fully relies on AI, which introduces decision-making tools opening uncharted frontiers which were in the past deemed challenging to explore. These methodologies are referred to as radiomics and dosiomics, handling imaging and dosimetric information, or to multiomics, when complemented by clinical and biological parameters (i.e., biomarkers). The review explicitly highlights the methodologies that are currently incorporated into clinical practice or still in research, with the aim of presenting the AI’s growing role in personalized radiotherapy.

Can Porphyrin-Triphenylphosphonium Conjugates Enhance the Photosensitizer Performance Toward Bacterial Strains?

Antimicrobial photodynamic treatment (aPDT) offers an alternative option for combating microbial pathogens, and in this way, addressing the challenges of growing antimicrobial resistance. In this promising and effective approach, cationic porphyrins and related macrocycles have emerged as leading photosensitizers (PS) for aPDT. In general, their preparation occurs via N-alkylation of nitrogen-based moieties with alkyl halides, which limits the ability to fine-tune the features of porphyrin-based PS. Herein, is reported that the conjugation of porphyrin macrocycles with triphenylphosphonium units created a series of effective cationic porphyrin-based PS for aPDT. The presence of positive charges at both the porphyrin macrocycle and triphenylphosphonium moieties significantly enhances the photodynamic activity of porphyrin-based PS against both Gram-positive and Gram-negative bacterial strains. Moreover, bacterial photoinactivation is achieved with a notable reduction in irradiation time, exceeding 50%, compared to 5,10,15,20-tetrakis(1-methylpyridinium-4-yl)porphyrin (TMPyP), used as the reference and known as good PS. The improved capability of the porphyrin macrocycle to generate singlet oxygen combined with the enhanced membrane interaction promoted by the presence of triphenylphosphonium moieties represents a promising approach to developing porphyrin-based PS with enhanced photosensitizing activity.

Optimization of Memristor Crossbar’s Mapping Using Lagrange Multiplier Method and Genetic Algorithm for Reducing Crossbar’s Area and Delay Time

Memristor crossbars offer promising low-power and parallel processing capabilities, making them efficient for implementing convolutional neural networks (CNNs) in terms of delay time, area, etc. However, mapping large CNN models like ResNet-18, ResNet-34, VGG-Net, etc., onto memristor crossbars is challenging due to the line resistance problem limiting crossbar size. This necessitates partitioning full-image convolution into sub-image convolution. To do so, an optimized mapping of memristor crossbars should be considered to divide full-image convolution into multiple crossbars. With limited crossbar resources, especially in edge devices, it is crucial to optimize the crossbar allocation per layer to minimize the hardware resource in term of crossbar area, delay time, and area–delay product. This paper explores three optimization scenarios: (1) optimizing total delay time under a crossbar’s area constraint, (2) optimizing total crossbar area with a crossbar’s delay time constraint, and (3) optimizing a crossbar’s area–delay-time product without constraints. The Lagrange multiplier method is employed for the constrained cases 1 and 2. For the unconstrained case 3, a genetic algorithm (GA) is used to optimize the area–delay-time product. Simulation results demonstrate that the optimization can have significant improvements over the unoptimized results. When VGG-Net is simulated, the optimization can show about 20% reduction in delay time for case 1 and 22% area reduction for case 2. Case 3 highlights the benefits of optimizing the crossbar utilization ratio for minimizing the area–delay-time product. The proposed optimization strategies can substantially enhance the neural network’s performance of memristor crossbar-based processing-in-memory architectures, especially for resource-constrained edge computing platforms.

Comparative evaluation of reproductive organ-preserving versus standard radical cystectomy in female: a meta-analysis and systematic review of perioperative, oncological, and functional outcomes.

To evaluate the perioperative, oncological, and functional outcomes of reproductive organ-preserving radical cystectomy (ROPRC) compared to standard radical cystectomy (SRC) in the treatment of female bladder cancer. A systematic search was conducted in November 2023 across several scientific databases. We executed a systematic review and cumulative meta-analysis of the primary outcomes of interest, adhering to the PRISMA and AMSTAR guidelines. The study was registered in PROSPERO (CRD42024501522). The meta-analysis included 10 studies with a total of 2015 participants. ROPRC showed a significant reduction in operative time and postoperative fasting period compared to SRC (MD -45.69, 95% CI -78.91 ~ -12.47, p = 0.007, and MD -0.69, 95% CI -1.25 ~ -0.13, p = 0.02, respectively). Functional outcomes, both daytime continence rate (OR 4.94, 95% CI 1.53 ~ 15.91, p = 0.008) and nighttime continence rate (OR 5.91, 95% CI 1.94 ~ 18.01, p = 0.002), and sexual function measured by the Female Sexual Function Index (MD 5.72, 95% CI 0.19 ~ 11.26, p = 0.04), were significantly improved in the ROPRC group. There were no significant differences between ROPRC and SRC in terms of estimated blood loss, length of hospital stay, overall postoperative complications, minor complications or major complications. Oncologically, both procedures showed comparable outcomes with no significant differences in positive surgical margins, tumor recurrence rates, overall survival, cancer-specific survival, recurrence-free survival, or progression-free survival. ROPRC is a viable and effective alternative to SRC in female bladder cancer patients, offering enhanced functional outcomes and similar oncological safety. These findings suggest that ROPRC can improve the quality of life in female bladder cancer patients without compromising the efficacy of cancer treatment.

Using virtual reality for occupational safety and health in a UK tunnelling megaproject

Recent advancements into key technological developments (KTDs) are leading to a paradigm shift in the way that occupational safety and health (OSH) is managed in the architecture, engineering, construction and operation sector. This paper reports on a pilot case study where building information modelling and virtual reality were used for OSH training purposes related to the operation of a tunnel-boring machine. The aim of the research was to identify the benefits, barriers and challenges associated with the implementation of these KTDs in a joint venture organisation delivering part of a complex megaproject in Central London, UK, the Thames Tideway Tunnel project (thereafter, ‘Tideway’). The reported benefits included better safety in design and improved hazard perception. Tideway estimates that the project could potentially see a 20% reduction in training time and a 10% reduction in accidents. The key lessons learned include the need to keep costs down, the necessity for system portability and ease of access. The results of the study will assist organisations that are interested in the adoption of these KTDs and provide valuable insights to the wider construction industry. This will in turn enable companies to improve how OSH is managed and promote the uptake of KTDs for OSH.

Dosimetric consequences of flap dose due to rapid beam off control for a high intensity carbon ion radiation therapy synchrotron.

In carbon ion radiation therapy (CIRT) the predominant method of irradiation is raster scanning, called dose driven continuous scanning (DDCS) by Hitachi, allowing for continuous synchrotron extraction. The reduction in irradiation time is highly beneficial in minimizing the impact of patient and target movement on dose distribution. The RF knock out (RFKO) slow-extraction method is commonly used for beam on/off control. When the Hitachi synchrotron receives a beam off signal the control system stops the RFKO and after some delay time (t-delay) during which the beam intensity declines, a high-speed steering magnet (HSST) is used to sweep the remaining beam from isocenter to a beam dump for safety reasons. Mayo Clinic Florida (MCF) will use a very short delay of the HSST operation from the RFKO beam OFF signal to minimize the delay time and delayed dose. MCF clinical beam intensity, a tenfold increase over HIMAK, is still less than 100 mMU/ms (approximately 4.9×109 pps for 430 MeV/u). The rapid beam off control (RBOC) proposed for MCF is associated with the occurrence of flap dose (FD), which refers to the asymmetric shoulder of the spot dose profile formed from the beam bent by HSST deviating from its planned spot position on the isocenter plane. In this study, we quantitatively assessed FD, proposed a treatment planning system (TPS) implementation using a flap spot (FS) and evaluated its impact on dose distribution. The experiments were conducted at the Osaka Heavy Ion Therapy Center (HIMAK) varying the t-delay from 0.01 to 1ms in a research environment to simulate the MCF RBOC. We studied the dependence of FD position on beam transport and its dependence on energy and beam intensity. FD was generated by delivering 10000 continuous spots on the central axis that are occasionally triggered by an external 10Hz gate signal. Measurements were conducted using an oscilloscope, and the nozzle's spot position monitor (SPM) and dose monitor (DM). All spot profile data were corrected for the gain of the SPM's beam intensity dependence. FD was determined by fitting the (SPM) Profile data to a double Gaussian. The position of the FS was found to be transport path dependent, with FS occurring on the opposite sides of the scanning x-direction for vertical and horizontal ports, respectively, as predicted by transport calculations. It was observed that the FD increases with beam intensity and did not exhibit a significant dependence on energy. The effect of FD on treatment planning is shown to have no significant dose impact on the organs at risk (OARs) near the target for clinical beam intensities and a modest increase for very high intensities. Using HIMAK in research mode the implications are that the FD has no clinical impact on the clinical CIRT beam intensities for MCF and maybe planned for higher intensities by incorporating FS into the TPS to predict the modest increased dose to OARs. A method for commissioning and quality assurance of FD has been proposed.

Ultrasmall copper-metal organic framework (Cu-MOF) quantum dots decorated on waste derived biochar for enhanced removal of emerging contaminants: Synergistic effect and mechanistic insight

This study proposes a novel one-pot hydrothermal impregnation strategy for surface decoration of waste derived pisum sativum biochar with zero‒dimensional Cu‒MOF Quantum dots (PBC‒HK), with an average particle size of 5.67 nm, for synergistic removal of an emerging sulfur containing drug pantoprazole (PTZ) and Basic Blue 26 (VB) dye within 80 min and 50 min of visible-light exposure, respectively. The designed Integrated Photocatalytic Adsorbent (IPA) presented an enhanced PTZ removal efficiency of 95.23% with a catalyst loading of 0.24 g/L and initial PTZ conc. 30 mg/L at pH 7, within 80 min via synergistic adsorption and photodegradation under visible-light exposure. While, on the other hand, 96.31% VB removal efficiency was obtained in 50 min with a catalyst dosage of 0.20 g/L, initial VB conc. 60 mg/L at pH 7 under similar irradiation conditions. An in-depth analysis of the synergistic adsorption and photocatalysis mechanism resulting in the shortened time for the removal of contaminants in the synergistic integrated model has been performed by outlining the various advantageous attributes of this strategy. The first-order degradation rate constant for PTZ was found to be 0.04846 min−1 and 0.04370 min−1 for PTZ and VB, respectively. Adsorption of contaminant molecules on the biochar (PS‒BC) surface can facilitate photodegradation by accelerating the kinetics, and photodegradation promotes regeneration of adsorption sites, contributing to an overall reduction in operation time for removal of contaminants. Besides enhancing the adsorption of targeted pollutants, the carbon matrix of IPAs serves as a surface for adsorption of intermediates of degradation, thereby minimizing the risk of secondary pollution. The photogenerated holes present in the VB is responsible for the generation of •OH radicals. While, the photogenerated electrons present in the CB are captured by Cu2+ of the MOF metal center, reducing it to Cu+, which is subsequently oxidized to produce additional •OH species in the aqueous medium. This process leads to effective charge separation of the photogenerated charge carriers and minimizes the probability of charge recombination as evident from photoluminescence (PL) analysis. Meanwhile, PL studies, EPR and radical trapping experiments indicate the predominant role of •OH radicals in the removal mechanism of PTZ and VB. The investigation of the degradation reaction intermediates was confirmed by HR‒LCMS, on the basis of which the plausible degradation pathway was elucidated in detail. Moreover, effects of pH, inorganic salts, other organic compounds and humic acid concentration have been investigated in detail. The environmental impact of the proposed method was comprehensively evaluated by ICP-OES analysis and TOC and COD removal studies. Furthermore, the economic feasibility and the cost-effectiveness of the catalyst was assessed to address the potential for large scale commercialization. Notably, this research not only demonstrates a rational design strategy for the utilization of solid waste into treasure via the fabrication of IPAs based on MOF Quantum dots (QDs) and waste-derived biochar, but also provides a practical solution for real wastewater treatment systems for broader industrial applications.

Coupled modeling and simulation of tank self-pressurization and thermal stratification

Numerical analysis of thermo-fluid dynamics for cryogenic propellant storage primarily consists of nodal and computational fluid dynamics (CFD) modeling approaches. While nodal approaches prioritize faster computation over fidelity, CFD approaches promise accuracy and fidelity at the expense of significant computational resources. This paper presents an efficient coupling methodology developed to facilitate the simulation of a long-term self-pressurization process of a cryogenic propellant tank as well as associated thermal stratification phenomenon under both normal and reduced gravity conditions. Key highlight of the methodology is the development of a coupling scheme based on a domain decomposition approach that separates the tank into two computational regions at the liquid-vapor interface. SINDA/FLUINT, the nodal code, is utilized to simulate the liquid region, while ANSYS Fluent, the CFD code, handles the vapor region. A data exchange algorithm was developed to compute required boundary conditions for each region using the local thermodynamic properties of assumed infinitely thin interface. The coupling between the nodal and CFD codes was demonstrated by simulating a self-pressurization process in a small size tank using hydrogen as the working fluid. A sensitivity study on the grid sizing and coupling time step was conducted to determine appropriate spatial and ensure a divergent-free explicit data exchange time step size. Using the coupling approach, two numerical case studies were performed to study tank self-pressurization by considering two initial hydrogen fill levels. The effectiveness and efficiency of the coupling methodology was assessed by comparing the temperature and pressure evolution results of the coupled simulation with those obtained solely from the CFD simulation. Results showed that the coupling scheme and data exchange algorithm were successfully implemented, and the coupled simulation agreed well with the CFD simulations. In addition, a stratification number was used to characterize the transient dynamic development of the thermal stratification. Lastly, the computational costs associated with both approaches were compared. Significant reductions in simulation time were achieved for both cases.

Parallel ensemble of a randomization-based online sequential neural network for classification problems using a frequency criterion

Randomization-based neural networks have gained wide acceptance in the scientific community owing to the simplicity of their algorithm and generalization capabilities. Random vector functional link (RVFL) networks and their variants are a class of randomization-based neural networks. RVFL networks have shown promising results in classification, regression, and clustering problems. For real-world applications, learning algorithms that can train with new samples over previous results are necessary because of to the constant generation of problems related to large-scale datasets. Various online sequential algorithms, commonly involving an initial learning phase followed by a sequential learning phase, have been proposed to address this issue. This paper presents a training algorithm based on multiple online sequential random vector functional link (OS-RVFL) networks for large-scale databases using a shared memory architecture. The training dataset is distributed among p OS-RVFL networks, which are trained in parallel using p threads. Subsequently, the test dataset samples are classified using each trained OS-RVFL network. Finally, a frequency criterion is applied to the results obtained from each OS-RVFL network to determine the final classification. Additionally, an equation was derived to reasonably predict the total training time of the proposed algorithm based on the learning time in the initial phase and the time scaling factor compared to the sequential learning phase. The results demonstrate a drastic reduction in training time because of data distribution and an improvement in accuracy because of the adoption of the frequency criterion.

Survey on pattern of myopia in school children in Hangzhou after the COVID-19 pandemic: a school-based vision screening study

BackgroundMyopia is a major health issue around the world. Myopia in children has increased significantly during the COVID-19 pandemic in China, but reports are scarce on the prevalence of myopia following the pandemic. This study collected vision screening data of school children in China for five consecutive years to observe the changes in myopia after the pandemic and compare the observed prevalence of myopia before and after the pandemic.MethodsA school-based vision screening study used stratified samplings to collect the vision screening data in school children aged 6–13 from 45 primary schools in Hangzhou. Vision screening data including uncorrected visual acuity(UCVA) and spherical equivalent refraction(SER). Calculating the mean of SER and the prevalence of myopia and hyperopia from 2019 to 2023.ResultsA total of 79,068 screening results (158,136 eyes) were included in the analysis. A substantial myopic shift (approximately -0.30 diopters [D] on average) was found in 2020 and 2021 compared with 2019 in all age groups and a substantial myopic shift (approximately 0.4 D on average) was found in 2022 compared with 2021. A slight myopic shift (approximately -0.14 D on average) was found in 2023 compared with 2022. The prevalence of myopia in all age groups was the highest for five years in 2020 or 2021, which was 31.3% for 6-year-olds, 43.0% for 7-year-olds, and 53.7% for 8-year-olds. A positive change in the prevalence rate of myopia was found at 6 years old (0.59%, 0.12%, 0.36%, 0.25%, p < 0.001). The change in prevalence rate in myopia was shifted slightly in children aged 10–13 years. Children aged 8 to 13 years had a slight increase in myopia prevalence from 2022 to 2023. The prevalence of hyperopia was low and stable in all grade groups, ranging from 0.7% to 2.2% over five years.ConclusionMyopia in children has increased rapidly during the COVID-19 pandemic. After the pandemic, the prevalence of myopia in children gradually decreased temporarily and then rebounded. Myopic shift was more apparent in younger children. Myopic shift in children may be related to the reduction of outdoor time, less light, and near work habits, and further research is needed.

Automated segmentation of brain metastases with deep learning: a multi-center, randomized crossover, multi-reader evaluation study.

Artificial intelligence has been proposed for brain metastasis (BM) segmentation but it has not been fully clinically validated. The aim of this study was to develop and evaluate a system for BM segmentation. A deep-learning-based BM segmentation system (BMSS) was developed using contrast-enhanced MR images from 488 patients with 10,338 brain metastases. A randomized crossover, multi-reader study was then conducted to evaluate the performance of the BMSS for BM segmentation using data prospectively collected from 50 patients with 203 metastases at five centers. Five radiology residents and five attending radiologists were randomly assigned to contour the same prospective set in assisted and unassisted modes. Aided and unaided Dice similarity coefficients (DSCs) and contouring times per lesion were compared. The BMSS alone yielded a median DSC of 0.91 (95% confidence interval, 0.90-0.92) in the multi-center set and showed comparable performance between the internal and external sets (p = 0.67). With BMSS assistance, the readers increased the median DSC from 0.87 (0.87-0.88) to 0.92 (0.92-0.92) (p < 0.001) with a median time saving of 42% (40-45%) per lesion. Resident readers showed a greater improvement than attending readers in contouring accuracy (improved median DSC, 0.05 [0.05-0.05] vs. 0.03 [0.03-0.03]; p < 0.001), but a similar time reduction (reduced median time, 44% [40-47%] vs. 40% [37-44%]; p = 0.92) with BMSS assistance. The BMSS can be optimally applied to improve the efficiency of brain metastasis delineation in clinical practice.

Effectiveness of behavioural interventions with motivational interviewing on physical activity outcomes in adults: systematic review and meta-analysis

ObjectiveTo evaluate the effectiveness of behavioural interventions that include motivational interviewing on physical activity outcomes in adults.DesignSystematic review and meta-analysis.Study selectionA search of seven databases for randomised controlled trials published...

Effectiveness of behavioural interventions with motivational interviewing on physical activity outcomes in adults: systematic review and meta-analysis

ObjectiveTo evaluate the effectiveness of behavioural interventions that include motivational interviewing on physical activity outcomes in adults.DesignSystematic review and meta-analysis.Study selectionA search of seven databases for randomised controlled trials published...

Lean manufacturing for reducing lead time in foundry processes: a design science approach

Purpose The lean manufacturing (LM) approach is a highly effective method that can be implemented in any industry to streamline production processes, meet customer demand and eliminate any unnecessary waste. This paper aims to propose and evaluate a generic project-based framework grounded on the LM approach for reducing lead time in foundry processes. Design/methodology/approach Using design science research (DSR), we developed a generic LM project-based framework for reducing lead time in foundry processes. Findings The developed framework provides an alternative method to implement LM projects to reduce lead time and nonvalue activities in foundry factories. Practical implications The findings of this research can guide better lean practitioners for lead time reduction in foundry processes. Originality/value This paper contributes to the operational excellence literature when discussing the impact of the LM approach on foundry processes. In addition, the paper provides a roadmap for reducing lead time in a foundry company.