We developed a software to automatically measure the linearity between the CT numbers and densities of objects using an ACR 464 CT phantom, and investigated the CT number linearity of 16 different CT scanners. The software included a segmentation-rotation method. After segmenting five objects within the phantom image, the software computed the mean CT number of each object and plotted a graph between the CT numbers and densities of the objects. Linear regression and coefficients of regression, R, were automatically calculated. The software was used to investigate the CT number linearity of 16 CT scanners from Toshiba, Siemens, Hitachi, and GE installed at 16 hospitals in Indonesia. The linearity of the CT number obtained on most of the scanners showed a strong linear correlation (> 0.99) between the CT numbers and densities of the five phantom materials. Two scanners (Siemens Emotion 16) had the strongest linear correlation with= 0.999, and two Hitachi Eclos scanners had the weakest linear correlation with< 0.99.
Background: Receptive language, the ability to comprehend and respond to spoken language, poses significant challenges for individuals with Autism Spectrum Disorder (ASD). To support communication in autistic children, interventions like Lovaas' simple-conditional method and Green's conditional-only method are commonly employed. Personalized approaches are essential due to the spectrum nature of autism.
Department of statistics, University of South Africa, c/o Christiaan de Wet Road & Pioneer Avenue, Johannesburg, 1709, Gauteng, South Africa. Electronic address:
Background And Objective: In epidemiological research, studying the long-term dependencies between multiple diseases is important. This study extends the multilevel Bayesian network (MBN) for repeated measures data that can estimate the rate of change in outcomes over time while quantifying the variabilities of these rates across higher-level units through various variance-covariance structures.
Method: The performance and reliability of a model are examined through a simulation study, and its practical application is demonstrated using child morbidity data.
Objectives: The prevalence of dental caries is high in Japan, leading to a huge burden of overall expenditure on dental caries treatment for the population. School-based fluoride mouth-rinse (S-FMR) has been implemented as a public health intervention. However, its cost-effectiveness remains unclear, where universal health insurance covers a broad range of dental treatments at relatively low cost.
Introduction: Low back pain (LBP) is a significant global health burden, with variable treatment outcomes and an unclear underlying molecular mechanism. Effective prediction of treatment responses remains a challenge. In this study, we aimed to develop gene signature-based machine learning models using transcriptomic data from peripheral immune cells to predict treatment outcomes in patients with LBP.
Objective: Spinal muscular atrophy (SMA) is an autosomal recessive neuromuscular disease characterized by proximal muscle weakness and atrophy. The increasing availability of disease-modifying therapies has prompted the development of biomarkers to facilitate clinical assessments. We explored the association between disease severity and serum creatinine (Crn) levels in SMA patients undergoing up to two years of treatment with nusinersen.
