Translational Ultrasound Research Laboratory, Division of Cardiovascular Diseases, Mayo Clinic College of Medicine, Scottsdale, Arizona 85259, USA.
Published: September 2009
AI Article Synopsis
Article Abstract
Background: Automated function imaging is a software tool available to facilitate the efficiency of workflow when analyzing left ventricular strain. In this study, automated function imaging was compared with a conventional approach for the analysis of right ventricular strain in normal and pressure-overloaded right ventricles.
Methods: Twelve pigs were subjected to graded acute right ventricular systolic pressure overload. Intraclass and interclass correlation coefficients (ICCs) with 95% confidence intervals were used for statistical evaluation, with grading based on the kappa statistic as follows: ICC >0.75 = excellent, 0.4 to 0.75 = good, and <0.40 = poor.
Results: Intraobserver and interobserver variability for both regional and global strains consistently ranged from good to excellent (ICC, 0.50-0.99), with good agreement between the conventional and automated methods.
Conclusion: Automated function imaging correlates well with conventional strain analysis of the right ventricle. Automated function imaging is a practical tool for measuring regional and global longitudinal strain in both normal and pressure-overloaded right ventricles.
Background: Traditional cognitive assessments, often reliant on paper-and-pencil tests and professional evaluators, suffer from subjectivity and limited result discrimination. This study introduces the Baguan Online Cognitive Assessment System (BOCAS), a tablet-based system that evaluates both general cognitive ability (GCA) and domain-specific functions across six domains: sensory-motor skills, processing speed, sustained attention, working memory, cognitive flexibility, and spatial ability.
Methods: BOCAS was validated with 151 healthy Chinese adults aged 18-40.
Background: The composite physiologic index (CPI) was developed to estimate the extent of interstitial lung disease (ILD) in idiopathic pulmonary fibrosis (IPF) patients based on pulmonary function tests (PFTs). The CALIPER-revised version of the CPI (CALIPER-CPI) was also developed to estimate the volume fraction of ILD measured by CALIPER, an automated quantitative CT postprocessing software. Recently, artificial intelligence-based quantitative CT image analysis software (AIQCT), which can be used to quantify the bronchial volume separately from the ILD volume, was developed and validated in IPF.
National Clinical Research Center for Aging and Medicine at Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China.
Mental health disorders emerge from complex interactions among neurobiological processes across multiple scales, which poses challenges in uncovering pathological pathways from molecular dysfunction to neuroimaging changes. Here, we proposed a multiscale fusion (mFusion) method to evaluate the relevance of each gene to the neuroimaging traits of mental health disorders. We combined gene-neuroimaging associations with gene-positron emission tomography (PET) and PET-neuroimaging associations using protein-protein interaction networks, where various genes traced by PET maps are involved in neurotransmission.
Objective: Telemonitoring (TM) remotely monitors individuals' health. Awareness of personal clinical data has resulted in improved glycaemic control in adults with type-2 diabetes mellitus (T2DM). However, its effects on their health-seeking behaviour remain unclear.
Co-activation of distinct brain areas provides a valuable measure of functional interaction, or connectivity, between them. One well-validated way to investigate the co-activation patterns of a precise area is meta-analytic connectivity modeling (MACM), which performs a seed-based meta-analysis on task-based functional magnetic resonance imaging (task-fMRI) data. While MACM stands as a powerful automated tool for constructing robust models of whole-brain human functional connectivity, its inherent limitation lies in its inability to capture the distinct interrelationships among multiple brain regions.
