Median Deviation Research Articles

Deep learning based next word prediction aided assistive gaming technology for people with limited vocabulary

AimVocabulary is the base for communication and vocabulary deficits reduce effective social communication. This study aims to develop and analyze English language-based assistive gaming technology to support individuals with vocabulary deficits. Materials and methodsThe developed intervention is based on deep learning technology for next word prediction, and it is equipped with web and mobile application interaction support. Various deep learning models, including long short-term memory, bidirectional long short-term memory, and bidirectional encoder representations from transformers, were learned and deployed using an open-source dataset corpus. The proposed intervention was analyzed using a specified task of a 5-minute lecture on a simple technical topic. The completed activities were also evaluated by professionals using vocabulary-related expert characteristics. ResultsThe results of the conducted 5-minutetask on social domain without intervention and later with intervention improved. The results were assessed by counting vocabulary words as well as by vocabulary specialists. The levels of centraltendencies and deviations changed as the task improved with the proposed intervention. Mean, median, and standard deviation scores of the experimental group improved significantly (189.1, 194.0, and 67.61) in comparison to the control group (125.4, 127.0, and 43.55). Furthermore, language specialists observed significant differences in the mean and median without intervention (12.84 and 13.0) compared to the intervention group (24.23 and 24.0, respectively). ConclusionsThe outcome of the experiments appears to be in favor of the suggested intervention. As a result, the suggested deep learning based next word prediction aided assistive gaming technology could support persons in overcoming their limited vocabulary disadvantages. In the future, the integration of the proposed technology into existing communication enhancement technology could achieve better results.

Enhanced echocardiographic assessment of intracardiac flow in congenital heart disease.

4D flow magnetic resonance imaging (4D flow MRI) can assess and measure the complex flow patterns of the right ventricle (RV) in congenital heart diseases, but its limited availability makes the broad application of intracardiac flow assessment challenging. Color Doppler imaging velocity reconstruction from conventional echocardiography is an emerging alternative, but its validity against 4D flow MRI needs to be established. To compare intracardiac flow parameters measured by color Doppler velocity reconstruction (DoVeR) against parameters measured from 4D flow MRI. We analyzed 20 subjects, including 7 normal RVs and 13 abnormal RVs (10 with repaired Tetralogy of Fallot, and 3 with atrial-level shunts). Intracardiac flow parameters such as relative pressure difference, vortex strength, total kinetic energy, and viscous energy loss were quantified using DoVeR and 4D flow MRI. The agreement between the two methods was determined by comparing the spatial fields and quantifying the cross-correlation and normalized difference between time-series measurements. The hemodynamic parameters obtained from DoVeR and 4D flow MRI showed similar flow characteristics and spatial distributions. The time evolutions of the parameters were also in good agreement between the two methods. The median correlation coefficient between the time-series of any parameter was between 0.87 and 0.92, and the median L2-norm deviation was between 10% to 14%. Our study shows that DoVeR is a reliable alternative to 4D flow MRI for quantifying intracardiac hemodynamic parameters in the RV.

Evaluating soil moisture simulations from a national-scale gridded hydrological model over Great Britain

Study RegionThe study covers sites across Great Britain. Study FocusSoil moisture information is important for a range of applications including flood and drought monitoring, seasonal hydrological forecasting, and agricultural management. However, spatially distributed soil moisture estimates for sub-surface soils are scarce despite their importance. The Grid-to-Grid hydrological model (G2G) was primarily developed to simulate river flows at a national scale, but can also output simulated depth-integrated soil moisture on a 1 km grid. Here, we evaluate G2G soil moisture simulations against in situ neutron probe (NP) observations at 85 sites across Great Britain, to evaluate whether modelled soil moisture outputs have value and to identify areas for improvement. New Hydrological Insights for the Region:Despite large uncertainties in observed soil moisture within a site, there was a good temporal correlation between observed and modelled soil moisture, with Pearson correlation values exceeding 0.7 for 77% of sites. However, the model tended to under-predict soil moisture values (median bias of −12 cm/m) and over-predict variation (median standard deviation error of 2 cm/m). Model agreement with observations was generally better for areas with deep or mid-depth mineral soils and worst in areas of peat. Based on this evaluation against NP observations, we demonstrate that G2G soil moisture is a useful resource for estimating relative wetness of the soil, but not necessarily the soil moisture content values themselves.

Effects of the prone position on gas exchange and ventilatory mechanics and their correlations with mechanical power in burn patients with ARDS

Background Prone position has many documented benefits on severe ARDS patients especially on mortality. The benefits in ARDS secondary to severe burns have not been fully documented. Aim To quantify the effects of prone positioning on gas exchange, ventilatory mechanics and their correlations with mechanical power in burn subjects with ARDS. Methods Cross-sectional observational analytical study that took place between January 2023 and October 2023 in Burns ICU in Brazil on subjects with moderate to severe ARDS ventilated with the volume controlled mode. Data were collected in the first prone positioning lasting 24 hours in the first 30 minutes after changing position and 30 minutes before returning to the supine position. The parameters of the components of mechanical ventilation and mechanical power calculated by the Gatinoni’s formula (respiratory rate, tidal volume, driving pressure, PEEP, peak and plateau pressures) were collected to evaluate ventilatory mechanics, and the values of the FiO2, PaO2, PaO2/FiO2 ratio, SpO2, EtCO2, PaCO2, PaCO2 - EtCO2 gradient to assess gas exchange. Mean, minimum and maximum values, 1st and 3rd quartiles, median and standard deviation are calculated. To compare the results obtained at the two evaluation moments, the student’s t-test for dependent samples and non-parametric Wilcoxon tests were considered. To evaluate the association between the variation between the two moments of each variable, and the variation in mechanical power, the Pearson correlation coefficient was calculated. The normality of the variables was assessed using the Jarque-Béra test. P values <0.05 indicated statistical significance. Results Except for EtCO2 (P 0.939) and PaCO2 (P 0.391) all other variables presented statistical significance in relation to their variations with reduction in FiO2 (P <0.001), reduction in PaCO2 - EtCO2 gradient (P 0.011), and increases in PaO2 (P 0.008), PaO2/FiO2 (P <0.001), SpO2 (P 0.004). In the analysis of variables, reduction in respiratory rate (P 0.142), VT (P 0.385), peak pressure (P 0.085), plateau pressure (P 0.009), PEEP (P 0.032), driving pressure (P 0.083), elastance (P 0.180), mechanical power (P < 0.001) with increase static compliance (P 0.414) and resistance pressure (P 0.443). Among the ventilatory mechanics variables, only the reductions in plateau pressure, PEEP, and mechanical power showed statistical significance. Conclusion The prone position in burns induced ARDS improved oxygenation and reduced arterial partial pressure to end tidal CO2 gradient, furthermore, reducing plateau pressures and PEEP, which in turn reduced mechanical power. Keywords: mechanical power, burns, ARDS, prone position

Implementation of Team Quiz to Improve Mathematics Learning Outcomes of Elementary School Students

This research aims to enhance students' learning outcomes. This type of research is classroom action research conducted in two cycles. The subjects of this research were 20 fourth-grade elementary school students. The instruments used were learning outcome tests, teacher and student observation sheets, and student response questionnaires. The data analysis techniques used firstly involved continuous data reduction throughout the research. Secondly, data presentation was done in the form of a presentation of the improvement of students' abilities to determine learning completeness using individual, classical, mean, median, percentage analysis, and standard deviation formulas. Thirdly, data verification, where the categorization of learning outcomes is adjusted to the school. The results showed that the team quiz type of learning successfully improved students' learning outcomes in the class. In two cycles of learning, the percentage of mathematics completeness increased from 10% to 75%. This model is effective and recommended for further learning processes.

Assessment of Knowledge and Attitudes Toward Children With Autism Spectrum Disorder Among Undergraduate Nursing Students.

Background There has been a dramatic increase in the incidence and prevalence rate of autism globally. This increase could be correlated to the increase in the awareness level and understanding of the disorder among healthcare providers and autistic children's families. Aim This study aimed to assess undergraduate nursing students' knowledge about and attitudes toward children with autism spectrum disorder (ASD). Methods A cross-sectionalquantitative study design was used. A convenience sample of 235 undergraduate nursing students from all levels was obtained. Data were collected using a questionnaire. Descriptive analysis (including mean, median, and standard deviation) was conducted. Inferential analysis was conducted to identify the relation between participants' demographic data and the subscales. Results The majority of the studied sample was aged 20 years or over (n=194, 82.55%) and was from the second year of undergraduate nursing school (n=120, 51.1%). The total mean score of the overall knowledge levels among nursing students at King Abdulaziz University (KAU) was mild (M=0.91, SD=±1.04), whereas the overall mean percentage score of undergraduate nursing students' attitudes toward care, education, and advocacy for autistic children was 74.38%, indicating a positive high level of attitude. Conclusions This study highlighted the level of nursing students' knowledge about childhood autism. Additionally, it disclosed their attitudes toward autistic children. The study filled a gap in the literature by revealing the perspectives of nursing students toward ASD.

Endoscopic self-expandable metal stent versus endoscopy vacuum therapy for traumatic esophageal perforations: a retrospective cohort study

BackgroundTraumatic esophageal perforations (TEP) are a grave medical condition and require immediate intervention. Techniques such as Esophageal Self-Expandable Metal Stent (E-SEMS) and Endoscopic Vacuum Therapy (EVT) show promise in reducing tissue damage and controlling esophageal leakage. The present study aims to compare the application of EVT to E-SEMS placement in TEP.MethodsRetrospective cohort study valuated 30 patients with TEP. The E-SEMS and EVT groups were assessed for time of hospitalization, treatment duration, costs, and clinical outcome.ResultsPatients treated with EVT (24.4 ± 13.2) demonstrated significantly shorter treatment duration (p < 0.005) compared to the group treated with E-SEMS (45.8 ± 12.9) and patients submitted to E-SEMS demonstrated a significant reduction (p = 0.02) in the time of hospitalization compared to the EVT (34 ± 2 vs 82 ± 5 days). Both groups demonstrated a satisfactory discharge rate (E-SEMS 93.7% vs EVT 71.4%) but did not show statistically significant difference (p = 0.3155). E-SEMS treatment had a lower mean cost than EVT (p < 0.05). Descriptive statistics were utilized, arranged in table form, where frequencies, percentages, mean, median, and standard deviation of the study variables were calculated and counted. The Fisher's Exact Test was used to evaluate the relationship between two categorical variables. To evaluate differences between means and central points, the parametric t-test was utilized. Comparisons with p value up to 0.05 were considered significant.ConclusionE-SEMS showed a shorter time of hospitalization, but a longer duration of treatment compared to EVT. The placement of E-SEMS and EVT had the same clinical outcome. Treatment with E-SEMS had a lower cost compared with EVT.

Evaluating the Impact of Training Teachers to Identify Learning Disabilities: A Pre-experimental Study on Knowledge Enhancement.

Introduction Learning disability (LD) affects many school-going children and is seldom recognized or treated. As teachers spend time with students, they can easily recognize LD by observing academic activities and behaviors. In this context, the present study was conducted to assess the knowledge and practices of teachers regarding LD and evaluate the impact of an educational intervention on teachers' knowledge regarding LD and its screening and referral. Methods A pre-experimental study, including pre-post interventional assessments of teachers, was conducted from June 2018 to December 2019. A universal sample of 150 teachers from 10 schools teaching primary (first to fifth grade) and upper primary (sixth to eight grade) grades was included. Their knowledge about LD was assessed using the Dyslexia Assessment for the Languages of India (DALI), and an educational intervention for assessing, screening, and identifying LD was implemented. Data was analyzed using SPSS version 24.0 (IBM Inc., Armonk, New York). Using descriptive statistics (mean, median, and standard deviation). The pre-post test results were compared using the McNemar test. Results Overall knowledge about LD was 24.7% at baseline, and improved to 76% post-intervention (p<0.001). The knowledge for most of the components showed improvement. Teachers with a good level of knowledge increased from 21% to 84%. Post-intervention screening of students increased from 0.53% to 13.37%. The suspicion rate for LD increased from 0.04% to 1.94% post-intervention. Conclusion Knowledge about LD was poor among the school teachers. However, the overall knowledge about LD, its specific domains, screening as well as actual LD screening significantly improved after the intervention (p<0.001). This emphasizes the need of training primary and post-primary school teachers about LD and the services available for children with LD.

Quantifying nasal deformities using a novel mathematical method to complement preoperative assessment in rhinoplasty patients

BackgroundRhinoplasty enhances facial symmetry and functionality. However, the accurate and reliable quantification of nasal defects pre-surgery remains an ongoing challenge. AimThis study introduces a novel approach for defect quantification using 2D images and artificial intelligence, providing a tool for better preoperative planning and improved surgical outcomes. Materials and MethodsA pre-trained AI model for facial landmark detection was utilised on a dataset of 250 images of male patients aged 18 to 24 who underwent rhinoplasty for cosmetic nasal deformity correction. The analysis concentrated on 36 different distances between the facial landmarks. These distances were normalised using min-max scaling to counter image size and quality variations. Post-normalisation, statistical parameters, including mean, median, and standard deviation, were calculated to identify and quantify nasal defects. ResultsThe methodology was tested and validated using images from different ethnicities and regions, showing promising potential as a beneficial surgical aid. The normalised data produced reliable quantifications of nasal defects (average 76.2%), aiding in preoperative planning and improving surgical outcomes and patient satisfaction. ApplicationsThe developed method can be extended to other facial plastic surgeries. Furthermore, it can be used to create app-based software, assist medical education, and improve patient-doctor communication. ConclusionThis novel method for defect quantification in rhinoplasty using AI and image processing holds significant potential in improving surgical planning, outcomes, and patient satisfaction, marking an essential step in the fusion of AI and plastic surgery.

Explanation of the influence of geomorphometric variables on the landform classification based on selected areas in Poland

In recent years, automatic image classification methods have significantly progressed, notably black box algorithms such as machine learning and deep learning. Unfortunately, such efforts only focused on improving performance, rather than attempting to explain and interpret how classification models actually operate. This article compares three state-of-the-art algorithms incorporating random forests, gradient boosting and convolutional neural networks for geomorphological mapping. It also attempts to explain how the most effective classifier makes decisions by evaluating which of the geomorphometric variables are most important for automatic mapping and how they affect the classification results using one of the explainable artificial intelligence techniques, namely accumulated local effects (ALE). This method allows us to understand the relationship between predictors and the model’s outcome. For these purposes, eight sheets of the digital geomorphological map of Poland on the scale of 1:100,000 were used as the reference material. The classification results were validated using the holdout method and cross-validation for individual sheets representing different morphogenetic zones. The terrain elevation entropy, absolute elevation, aggregated median elevation and standard deviation of elevation had the greatest impact on the classification results among the 15 geomorphometric variables considered. The ALE analysis was conducted for the XGBoost classifier, which achieved the highest accuracy of 92.8%, ahead of Random Forests at 84% and LightGBM at 73.7% and U-Net at 59.8%. We conclude that automatic classification can support geomorphological mapping only if the geomorphological characteristics in the predicted area are similar to those in the training dataset. The ALE plots allow us to analyze the relationship between geomorphometric variables and landform membership, which helps clarify their role in the classification process.

A framework for the analysis of skin sores disease using evolutionary intelligent computing approach

The most common and contagious bacterial skin disease i.e. skin sores (impetigo) mostly affects newborns and young children. On the face, particularly around the mouth and nose area, as well as on the hands and feet, it typically manifests as reddish sores. In this study, a neuro-evolutionary global algorithm is introduced to solve the dynamics of nonlinear skin sores disease model (SSDM) with the help of an artificial neural network. The global genetic algorithm is integrated with local sequential quadratic programming (GA-LSQP) to obtain the optimal solution for the proposed model. The designed differential model of skin sores disease is comprised of susceptible (S), infected (I), and recovered (R) categories. An activation function based neural network modeling is exploited for skin sores system through mean square error to achieve best trained weights. The integrated approach is validated and verified through the comparison of results of reference Adam strategy with absolute error analysis. The absolute error results give accuracy of around 10 − 11 to 10 − 5 , demonstrating the worthiness and efficacy of proposed algorithm. Additionally, statistical investigations in form of mean absolute deviation, root mean square error, and Theil’s inequality coefficient are exhibited to prove the consistency, stability, and convergence criteria of the integrated technique. The accuracy of the proposed solver has been examined from the smaller values of minimum, median, maximum, mean, semi-interquartile range, and standard deviation, which lie around 10 − 12 to 10 − 2 .

DAVSS: Development of Mobile Application for Documenting and Assessing Domestic Violence

This paper presents the development and evaluation of the Domestic Abuse and Violence Support System (DAVSS), a mobile application designed to address domestic violence issues in Malaysia. DAVSS integrates the essential 27-DASH (Danger Assessment for Separation and Harassment) questionnaire to assess the severity of domestic violence situations. The project follows the phased approach of the Waterfall Model, encompassing requirements, analysis, design, development, and testing stages.Usability testing was conducted to evaluate the DAVSS application, revealing overall user satisfaction. However, this assessment relied primarily on frequency analysis, prompting consideration for enhanced reliability through alternative measurement methods such as median and standard deviation approaches.The DAVSS application's Danger Assessment Test (DAT) algorithm, built upon the 27-DASH questions, represents a significant aspect of the system. Future work includes the validation of this rule-based algorithm using expert evaluation, potentially through Delphi techniques.Given the absence of dedicated domestic abuse mobile applications in Malaysia, DAVSS promises several advantages. It empowers victims to report incidents, locate shelters for protection, and facilitates shelter access through QR code integration. In conclusion, as domestic violence remains a pressing concern requiring increased support, the DAVSS mobile application emerges as a vital system that offers assistance to victims while breaking down barriers that may silence their voices.

Influence of soil - foundation - tank interaction on buckling strength of liquid storage tanks

Large earthquakes in the last 25 years have caused significant damage to buildings and infrastructure, including the partial or total collapse of storage tanks in various industries. Elephant foot buckling, or local buckling at the base, is one of the main failure modes observed in these structures, and this failure mode can lead to their collapse and/or complete loss of contents. Although hydrostatic and hydrodynamic loads typically affect the seismic response of tanks, the effect of soil type on tank buckling behavior has not been widely studied or recognized. This research aims to evaluate the effect of soil type on seismic fragility of tanks by analyzing typical storage tanks used in the wine industry. The work focuses on elephant foot buckling for tanks with both unanchored and anchored bases and compares the influence of three different types of soil and two different tank geometries. The approach uses the capacity spectrum method, as opposed to the more commonly used incremental dynamic analysis, to determine a critical peak ground acceleration to cause buckling at the tank. The tanks were subjected to 21 Chilean seismic records with three different soil types and a no-soil condition. From the results a lognormal fragility curve, and its median and standard deviation, are calculated. The results indicate that unanchored tanks built softer soils exhibit poorer performance, while tanks in competent soils and rock exhibit good performance. Anchored tanks show less sensitivity to soil types than unanchored tanks. The study demonstrates the importance of considering soil-foundation-structure interaction for wine storage tanks, but the results indicate that many comparable storage structures will be similarly affected.

A Development of Athletes Towards Excellence Model for Chinese Table Tennis Association

Background and Aim: Founded in 1955, the Chinese Table Tennis Association (CTA) is a national mass sports organization with an independent legal personality, headquartered in Beijing. It is a national social organization representing China's table tennis activities and the only legal organization representing China in international table tennis activities and the International Table Tennis Federation (FITT), and is the permanent office of the Table Tennis and Badminton Management Centre of the General Administration of Sport of China. However, in the process of the development of the Chinese Table Tennis Association (CTA), several problems have arisen, four of which are more prominent: the first is the problem of talent shortage, the second is the lack of sponsorship, the third is the institutional problem, and the fourth is the problem of competitive pressure. The objective of this research is qualitative research and aims to present a model for developing excellent athletes of the Chinese Table Tennis Association Materials and Methods: How to conduct research to Structured interviews used Bridge and Roqnemore planning, organizing, implementing, and controlling. The sample of 19 persons was selected as follows: 1 former association executive; 5 people, 2 trainers, 7 people, 3 athletes, and 7 champions. The 1st round of interviews takes the data to estimate the scale 5 Level and takes it to the 2nd round there is no need to interview in the 2nd round. Third, it is considered that the data to focus on, summarizing the suitability and feasibility of bringing the data into a format by considering the data Statistical analysis of experts in the second or third round of inquiries; Median (Mdn), Interquartile Rang (IR), Mean (), and standard deviations (SD). Results: The results that the 4 strategies and 28 factors as follows: 1 Strategy 1: Process planning strategy 1.1 Planning for organizing sports excellence 1.2 Find other organizations to join 1.3 Adding new athletes 1.4 Planning for recruiting talented athletes 1.5 Finding quantitative results for the number of athletes 1.6 Finding quantitative products 1.7 Budget 2 Strategy 2 Strategy for bringing factors that 2.1 Organizing the organization into a system 2.2 Bringing policy to use 2.3 Organizing the system of excellent athletes 2.4 Organizing the sports training 2.5 Organizing quality trainers 2.6 Preparing training equipment 2.7 Developing excellent athletes into careers 3 Strategy 3: Strategy of implementation 3.1 Stages the excellence practice 3.2 Testing fitness before training 3.3 Testing sports skills Excellent 3.4 Basic, middle and advanced athlete training 3.5 Organizing training for competitions with a program 3.6 Examples of striking exercises 7 Examples of striking exercises 4 Strategy 4: Proactive operational strategy, 7 factors 4.1 Assessing fitness of athletes 4.2 Creating a brand for athletes 4.3 A evaluation of training 4.4 A systematic evaluation of competition schedules 4.5 Applying sports science 4.6 Applying medicine and nutrition 4.7 Systematic measurement of excellent athletes. Conclusion: The strategies presented offer a well-rounded blueprint for achieving sports excellence by encompassing strategic planning, systematic organization, and proactive operational measures. Together, these components form a comprehensive framework that addresses the essential aspects of talent management, training, and performance evaluation, fostering a holistic approach to success in the realm of sports.

On the use of fractional calculus to improve the pulse arrival time (PAT) detection when using photoplethysmography (PPG) and electrocardiography (ECG) signals.

The pulse arrival time (PAT) has been considered a surrogate measure for pulse wave velocity (PWV), although some studies have noted that this parameter is not accurate enough. Moreover, the inter-beat interval (IBI) time series obtained from successive pulse wave arrivals can be employed as a surrogate measure of the RR time series avoiding the use of electrocardiogram (ECG) signals. Pulse arrival detection is a procedure needed for both PAT and IBI measurements and depends on the proper fiducial points chosen. In this paper, a new set of fiducial points that can be tailored using several optimization criteria is proposed to improve the detection of successive pulse arrivals. This set is based on the location of local maxima and minima in the systolic rise of the pulse wave after fractional differintegration of the signal. Several optimization criteria have been proposed and applied to high-quality recordings of a database with subjects who were breathing at different rates while sitting or standing. When a proper fractional differintegration order is selected by using the RR time series as a reference, the agreement between the obtained IBI and RR is better than that for other state-of-the-art fiducial points. This work tested seven different traditional fiducial points. For the agreement analysis, the median standard deviation of the difference between the IBI and RR time series is 5.72 ms for the proposed fiducial point versus 6.20 ms for the best-performing traditional fiducial point, although it can reach as high as 9.93 ms for another traditional fiducial point. Other optimization criteria aim to reduce the standard deviation of the PAT (7.21 ms using the proposed fiducial point versus 8.22 ms to 15.4 ms for the best- and worst-performing traditional fiducial points) or to minimize the standard deviation of the PAT attributable to breathing (3.44 ms using the proposed fiducial point versus 4.40 ms to 5.12 ms for best- and worst-performing traditional fiducial points). The use of these fiducial points may help to better quantify the beat-to-beat PAT variability and IBI time series.

Motion-compensated image reconstruction for improved kidney function assessment using dynamic contrast-enhanced MRI.

Accurately measuring renal function is crucial for pediatric patients with kidney conditions. Traditional methods have limitations, but dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides a safe and efficient approach for detailed anatomical evaluation and renal function assessment. However, motion artifacts during DCE-MRI can degrade image quality and introduce misalignments, leading to unreliable results. This study introduces a motion-compensated reconstruction technique for DCE-MRI data acquired using golden-angle radial sampling. Our proposed method achieves three key objectives: (1) identifying and removing corrupted data (outliers) using a Gaussian process model fitting with a -space center navigator, (2) efficiently clustering the data into motion phases and performing interphase registration, and (3) utilizing a novel formulation of motion-compensated radial reconstruction. We applied the proposed motion correction (MoCo) method to DCE-MRI data affected by varying degrees of motion, including both respiratory and bulk motion. We compared the outcomes with those obtained from the conventional radial reconstruction. Our evaluation encompassed assessing the quality of images, concentration curves, and tracer kinetic model fitting, and estimating renal function. The proposed MoCo reconstruction improved the temporal signal-to-noise ratio for all subjects, with a 21.8% increase on average, while total variation values of the aorta, right, and left kidney concentration were improved for each subject, with 32.5%, 41.3%, and 42.9% increases on average, respectively. Furthermore, evaluation of tracer kinetic model fitting indicated that the median standard deviation of the estimated filtration rate ( ), mean normalized root-mean-squared error (nRMSE), and chi-square goodness-of-fit of tracer kinetic model fit were decreased from 0.10 to 0.04, 0.27 to 0.24, and, 0.43 to 0.27, respectively. The proposed MoCo technique enabled more reliable renal function assessment and improved image quality for detailed anatomical evaluation in the case of bulk and respiratory motion during the acquisition of DCE-MRI.

Reexamining the Incidence of Nonarteritic Anterior Ischemic Optic Neuropathy: A Rochester Epidemiology Project Study.

This study aims to determine the population-based incidence and characterize the features of nonarteritic anterior ischemic optic neuropathy (NAION) using the Rochester Epidemiology Project (REP). All patients diagnosed with an optic neuropathy from January 1, 1990, to December 31, 2016, were retrospectively reviewed to identify incident cases of NAION using the REP database, which is a record-linkage system of medical records for all patient-physician encounters among Olmsted County, Minnesota residents. The overall incidence of NAION was estimated using the age-specific and sex-specific population figures for Olmsted County census data for 1990 through 2016. Visual outcomes and risk factors were evaluated. The systemic risk factors were compared with age-matched controls. One hundred four patients were diagnosed with NAION during the 26-year study period. The overall age and sex adjusted incidence was 3.89 per 100,000 individuals (95% confidence interval [CI]: 3.14-4.65). The incidence was 7.73 (CI: 6.24-9.22) in patients aged 40 years or older and 10.19 (CI: 8.15-12.23) in patients aged 50 years or older. Median age at diagnosis was 65 years (range, 40-90 years), and 59 (56.7%) were male. The median logMAR visual acuity at presentation was 0.35 (Snellen equivalent of 20/40) with 14 patients (13.5%) having vision of counting fingers or worse. Among the 91 patients with final visual acuity outcome data available, the median visual acuity was 0.40 (Snellen equivalent of 20/50) with 12 patients (13.2%) having vision of counting fingers or worse. Twenty-four patients (26.4%) were noted to have final acuity at least 3 Snellen lines worse than at presentation, whereas 17 patients (18.7%) were noted to improve by at least 3 lines. The median mean deviation on automated visual field testing was -10.2 dB at presentation and -11.1 dB at follow-up. Twenty-two patients (21.2%) suffered NAION in the fellow eye; the median interval between involvement of the first and second eyes was 1.39 years. Systemic diseases present in the NAION cohort included hypertension (79.8%), diabetes mellitus (39.4%), obstructive sleep apnea (23.1%), and hyperlipidemia (74.0%), which were all statistically higher than age-matched controls. NAION is a relatively common optic neuropathy in elderly patients with vascular risk factors. Our data indicate that the incidence of NAION has remained relatively stable in the population of Olmsted County over the past 4 decades.

Accuracy of instrument tip position using fiber optic shape sensing for navigated bronchoscopy

The purpose of this study was to evaluate the accuracy of a method for estimating the tip position of a fiber optic shape-sensing (FOSS) integrated instrument being inserted through a bronchoscope. A modified guidewire with a multicore optical fiber was inserted into the working channel of a custom-made catheter with three electromagnetic (EM) sensors. The displacement between the instruments was manually set, and a point-based method was applied to match the position of the EM sensors to corresponding points on the shape. The accuracy was evaluated in a realistic bronchial model. An additional EM sensor was used to sample the tip of the guidewire, and the absolute deviation between this position and the estimated tip position was calculated. For small displacements between the tip of the FOSS integrated tool and the catheter, the median deviation in estimated tip position was ≤5 mm. For larger displacements, deviations exceeding 10 mm were observed. The deviations increased when the shape sensor had sharp curvatures relative to more straight shapes. The method works well for clinically relevant displacements of a biopsy tool from the bronchoscope tip, and when the path to the lesion has limited curvatures. However, improvements must be made to our configuration before pursuing further clinical testing.

Insights into single-timepoint ASL hemodynamics: what visual assessment and spatial coefficient of variation can tell

PurposeArterial spin labeling (ASL) represents a noninvasive perfusion biomarker, and, in the study of nonvascular disease, the use of the single-timepoint ASL technique is recommended. However, the obtained cerebral blood flow (CBF) maps may be highly influenced by delayed arterial transit time (ATT). Our aim was to assess the complexity of hemodynamic information of single-timepoint CBF maps using a new visual scale and comparing it with an ATT proxy, the “coefficient of spatial variation” (sCoV).Material and methodsIndividual CBF maps were estimated in a memory clinic population (mild cognitive impairment, dementia and cognitively unimpaired controls) and classified into four levels of delayed perfusion based on a visual rating scale. Calculated measures included global/regional sCoVs and common CBF statistics, as mean, median and standard deviation. One-way ANOVA was performed to compare these measures across the four groups of delayed perfusion. Spearman correlation was used to study the association of global sCoV with clinical data and CBF statistics.ResultsOne hundred and forty-four participants (72 ± 7 years, 53% women) were included in the study. The proportion of maps with none, mild, moderate, and severe delayed perfusion was 15, 20, 37, and 28%, respectively. SCoV demonstrated a significant increase (p < 0.05) across the four groups, except when comparing none vs mild delayed perfusion groups (pBonf > 0.05). Global sCoV values, as an ATT proxy, ranged from 67 ± 4% (none) to 121 ± 24% (severe delayed) and were significantly associated with age and CBF statistics (p < 0.05).ConclusionThe impact of ATT delay in single-time CBF maps requires the use of a visual scale or sCoV in clinical or research settings.

Robot-Assisted Lumbar Pedicle Screw Placement Based on 3D Magnetic Resonance Imaging.

Human Cadaveric Study. This study aims to explore the feasibility of using preoperative magnetic resonance imaging (MRI), zero-time-echo (ZTE) and spoiled gradient echo (SPGR), as source data for robotic-assisted spine surgery and assess the accuracy of pedicle screws. Zero-time-echo and SPGR MRI scans were conducted on a human cadaver. These images were manually post-processed, producing a computed tomography (CT)-like contrast. The Mazor X robot was used for lumbar pedicle screw-place navigating of MRI. The cadaver underwent a postoperative CT scan to determine the actual position of the navigated screws. Ten lumbar pedicle screws were robotically navigated of MRI (4 ZTE; 6 SPGR). All MR-navigated screws were graded A on the Gertzbein-Robbins scale. Comparing preoperative robotic planning to postoperative CT scan trajectories: The screws showed a median deviation of overall 0.25mm (0.0; 1.3), in the axial plane 0.27mm (0.0; 1.3), and in the sagittal plane 0.24mm (0.0; 0.7). This study demonstrates the first successful registration of MRI sequences, ZTE and SPGR, in robotic spine surgery here used for intraoperative navigation of lumbar pedicle screws achieving sufficient accuracy, showcasing potential progress toward radiation-free spine surgery.