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Performance of Commercial Deep Learning-Based Auto-Segmentation Software for Prostate Cancer Radiation Therapy Planning: A Systematic Review
Get PDFAs yet, there is no systematic review focusing on benefits and issues of commercial deep learning-based auto-segmentation (DLAS) software for prostate cancer (PCa) radiation therapy (RT) planning despite that NRG Oncology has underscored such necessity. This article’s purpose is to systematically review commercial DLAS software product performances for PCa RT planning and their associated evaluation methodology. A literature search was performed with the use of electronic databases on 7 November 2024. Thirty-two articles were included as per the selection criteria. They evaluated 12 products (Carina Medical LLC INTContour (Lexington, KY, USA), Elekta AB ADMIRE (Stockholm, Sweden), Limbus AI Inc. Contour (Regina, SK, Canada), Manteia Medical Technologies Co. AccuContour (Jian Sheng, China), MIM Software Inc. Contour ProtégéAI (Cleveland, OH, USA), Mirada Medical Ltd. DLCExpert (Oxford, UK), MVision.ai Contour+ (Helsinki, Finland), Radformation Inc. AutoContour (New York, NY, USA), RaySearch Laboratories AB RayStation (Stockholm, Sweden), Siemens Healthineers AG AI-Rad Companion Organs RT, syngo.via RT Image Suite and DirectORGANS (Erlangen, Germany), Therapanacea Annotate (Paris, France), and Varian Medical Systems, Inc. Ethos (Palo Alto, CA, USA)). Their results illustrate that the DLAS products can delineate 12 organs at risk (abdominopelvic cavity, anal canal, bladder, body, cauda equina, left (L) and right (R) femurs, L and R pelvis, L and R proximal femurs, and sacrum) and four clinical target volumes (prostate, lymph nodes, prostate bed, and seminal vesicle bed) with clinically acceptable outcomes, resulting in delineation time reduction, 5.7–81.1%. Although NRG Oncology has recommended each clinical centre to perform its own DLAS product evaluation prior to clinical implementation, such evaluation seems more important for AccuContour and Ethos due to the methodological issues of the respective single studies, e.g., small dataset used, etc
Using Inertial Measurement Units and Machine Learning to Classify Body Positions of Adults in a Hospital Bed
Get PDFIn hospitals, timely interventions can prevent avoidable clinical deterioration. Early recognition of deterioration is vital to stopping further decline. Measuring the way patients position themselves in bed and change their positions may signal when further assessment is necessary. While inertial measurement units (IMUs) have been used in health research, their use inside hospitals has been limited. This study explores the use of IMUs with machine learning to continuously capture, classify and visualise patient positions in hospital beds. The participants attended a data collection session in a simulated hospital bedspace and were asked to adopt nine positions. Movement data were captured using five IMU Xsens DOTs attached to the forehead, wrists and ankles. Support Vector Machine (SVM) and K-Nearest Neighbours classifiers were trained using five different combinations of sensors (e.g., right wrist only, right and left wrist) to determine body positions. Data from 30 participants were analysed. The highest accuracy (87.7%) was achieved by SVM using forehead and wrist sensors. Adding data from ankle sensors reduced the accuracy. To preserve patient privacy in a hospital setting, a 3D visualisation was developed in Unity, offering a non-identifiable representation of patient positions. This system could help clinicians monitor changes in position which may signal clinical deterioration
Spillovers and small spatial scale analyses: contributions from spatial economics
No full textThis editorial introduces the seven papers included in this issue of Spatial Economic Analysis (SEA). The papers analyse two important topics in spatial economics. The first addresses the spillovers between units in space, specifically the phenomena through which different locations interact and the multiple channels through which these interactions take place. The second topic is related to the obtainment and processing of information at small spatial scales. The topics that are covered in the first theme are hence how distance influences venture capital (VC) investment decisions; the role of various proximities in innovation and regional knowledge production functions; the effects on local labour markets caused by what happens in other markets nearby; the use of different types of proximities and different distances at the same time in estimating spatial autoregressive model with autoregressive disturbances (SARAR) models. On the second topic the issue covers a new two-step technique to estimate small spatial scale synthetic data from microdata and aggregate statistics as an alternative to spatial microsimulation; the use of satellite data to estimate consumer confidence and expectations; and the use of disaggregated general equilibrium modelling based on the partial hypothetical extraction approach in input–output systems to estimate the effects of emergency aid
Interventions That Impact Aged Care Job Demands: A Systematic Review of Strategies and Their Evidence
Get PDFIntensifying job demands and their negative consequences are observed to a higher degree in aged care compared to health care and other sectors. However, the ability to identify and apply effective strategies to reduce job demands in this sector is impeded by a lack of synthesis and integration of existing research on interventions that directly or indirectly modify job demands. This article provides a systematic review of 68 interventions that provided data regarding implications for employee job demands in aged care. The most common strategies observed were professional education and client care protocol interventions. Cumulatively, these strategies provide moderately consistent evidence of reducing employee perceived demands and demands resulting from clients’ behavioral and psychological symptoms and client care, as well as improving employee work-related well-being and ill-being. Fewer studies investigated complex mixed-level and multicomponent interventions, potentially contributing to inconsistent evidence for their efficacy in reducing demands. This review highlights the potential for interventions to simultaneously address job demands and quality of client care, a relevant objective for health care industries. However, careful consideration of intervention effects on both these outcomes during and postimplementation is needed to maximize their benefit. More broadly, this review highlights the challenges in integrating interdisciplinary literature with relevant insights into the reduction of job demands, for job demand intervention researchers and practitioners, and provides guidance for further consolidation of existing and emerging research
Optimized design of a permanent magnet brushless DC motor for solar water-pumping applications
Get PDFThis paper presents a volume-optimized architecture for a brushless DC (BLDC) motor in the permanent magnet (PM) category. The proposed design minimizes the volume of the PM, achieving a 20 % reduction in material usage and lowering the overall cost of the motor without compromising performance. Efficiency improvements of up to 4.7 % over conventional BLDC motors with equivalent ratings are realized through parametric design optimization. Additionally, a sensorless control system is developed to drive the motor, reducing electronic controller complexity and cost by eliminating the need for position sensors. The control strategy, based on two voltage sensing points, accurately manages electronic commutation and speed control across a wide range, independent of motor parameters, using adaptive error optimization. The motor and solar maximum power are controlled through a single-stage three-phase inverter, making the electronic controller compact and enhancing its suitability for integrated applications. Magnetic and performance characteristics of the optimized motor are analyzed through finite element analysis (FEA). The motor prototype is manufactured in an industrial setting, and experimental validation using the developed sensorless controller demonstrates superior performance and an enhanced efficiency-to-cost ratio, making the proposed design an attractive solution for solar-powered irrigation systems
The future of dsRNA-based biopesticides will require global regulatory cohesion
No full textInternational regulatory harmonization through consistent regulations and risk assessment guidelines across countries could help to overcome challenges and potential delays in the development and commercialization of double-stranded RNA (dsRNA)-based biopesticides
Framework for Design Recommendations to Optimise Operational Dashboards in Supply Chain Management by Integrating Cognitive Neuroscience Principles
No full textDesigning intuitive dashboards for supply chains is vital for boosting transparency, efficiency, and productivity, ultimately leading to significant cost savings and substantial profits for businesses. In our research, we developed 30 key recommendations for designers to follow, when designing dashboards aimed at enhancing decision-making and streamlining operations. These guidelines make complex data accessible and actionable, empowering staff to respond swiftly to operational challenges. Our research insights will empower designers to build impactful dashboards that deliver measurable results in the supply chain sector
The Shadow of Leadership: Examining the Prevalence of Toxic Leadership in Malaysia
Get PDFToxic leadership has emerged as a pervasive and destructive force across organizational landscapes globally, yet empirical,
data-driven studies remain scarce—particularly within Southeast Asia. This study investigates the prevalence, perception,
and emotional impact of toxic leadership in Malaysia through a quantitative survey of seventy-nine (79) respondents
representing diverse industries. The findings reveal a troubling yet illuminating reality: 80% of participants reported firsthand
experience with toxic leadership, and over half perceived such behaviors as intentional. Respondents overwhelmingly
identified integrity, respect, and accountability as core attributes of effective leadership—traits perceived to be widely lacking
among Malaysian leaders. Crucially, the study uncovers three culturally grounded insights: the erosion of trust between
leaders and followers, the enabling role of conformity in high power distance environments, and the presence of entrenched
cognitive biases that normalize and perpetuate toxicity. Drawing from leadership theory, behavioral science, and cultural
psychology, the study establishes powerful links between leadership behavior, follower motivation, public perception, and
organizational climate. In doing so, it offers evidence-informed strategies to address toxic leadership, with particular
emphasis on fostering psychologically safe workplaces. While rooted in the Malaysian context, the findings bear significant
implications for leadership reform in similarly structured societies across the globe