Objective: The aim of this study was to determine the repeatability and reproducibility of optical coherence tomography angiography (OCTA) based on optical microangiography (OMAG) measurements of macular vessels in normal eyes.
Methods: In this prospective cohort study, 40 eyes of 40 healthy volunteers underwent repeated OCTA (Cirrus HD-OCT 5000 angiography system, Carl Zeiss Meditec, Inc.) scans on two separate visit days. On each visit day, the eyes were scanned three times. The following parameters were used to quantitatively describe the OCTA images of the superficial vascular network: vessel area density (VAD), vessel skeleton density (VSD), vessel diameter index (VDI), vessel perimeter index (VPI), vessel complexity index (VCI), flux, and foveal avascular zone (FAZ). Coefficient of variation (CV) and intraclass correlation coefficient (ICC) were calculated for evaluating intravisit and intervisit repeatability, as well as interobserver reproducibility.
Results: The measurements showed high repeatability [CVs ⪕ 4.2% (intravisit) and ⪕ 4.6% (intervisit)] and interobserver reproducibility (ICCs ⪖ 0.923) for all parameters.
Conclusion: This study demonstrated good repeatability and reproducibility of OCTA based on OMAG for the measurement of superficial vessel parameters in normal eyes.
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http://dx.doi.org/10.3967/bes2018.054 | DOI Listing |
Sensors (Basel)
December 2024
Antal Bejczy Center for Intelligent Robotics, Obuda University, 1034 Budapest, Hungary.
This paper presents a robust and efficient method for validating the accuracy of orientation sensors commonly used in practical applications, leveraging measurements from a commercial robotic manipulator as a high-precision reference. The key concept lies in determining the rotational transformations between the robot's base frame and the sensor's reference, as well as between the TCP (Tool Center Point) frame and the sensor frame, without requiring precise alignment. Key advantages of the proposed method include its independence from the exact measurement of rotations between the reference instrumentation and the sensor, systematic testing capabilities, and the ability to produce repeatable excitation patterns under controlled conditions.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA.
Mobility tasks like the Timed Up and Go test (TUG), cognitive TUG (cogTUG), and walking with turns provide insights into the impact of Parkinson's disease (PD) on motor control, balance, and cognitive function. We assess the test-retest reliability of these tasks in 262 PD participants and 50 controls by evaluating machine learning models based on wearable-sensor-derived measures and statistical metrics. This evaluation examines total duration, subtask duration, and other quantitative measures across two trials.
View Article and Find Full Text PDFCanine coronavirus (CCoV), canine respiratory coronavirus (CRCoV), canine adenovirus type 2 (CAV-2), and canine norovirus (CNV) are important pathogens for canine viral gastrointestinal and respiratory diseases. Especially, co-infections with these viruses exacerbate the damages of diseases. In this study, four pairs of primers and probes were designed to specifically amplify the conserved regions of the CCoV M gene, CRCoV N gene, CAV-2 hexon gene, and CNV RdRp gene.
View Article and Find Full Text PDFJ Clin Med
December 2024
Department of Pathology and Laboratory Medicine, Children's Hospital Los Angeles, 4650 Sunset Blvd, M/S #32, Los Angeles, CA 90027, USA.
N-terminal-proBNP (NT-proBNP) is a biomarker released into the blood in response to heart failure, reflecting the extent of cardiac stress and damage. QuidelOrtho Diagnostics released its latest reformulation of its NT-proBNP assay, the Vitros NT-proBNP II assay. This study aims to evaluate the analytical performance of the Vitros NT-proBNP II assay.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
Department of Radiology and Interventional Radiology, Lausanne University Hospital, Lausanne University, 1015 Lausanne, Switzerland.
Background/objectives: Recent advancements in artificial intelligence (AI) have spurred interest in developing computer-assisted analysis for imaging examinations. However, the lack of high-quality datasets remains a significant bottleneck. Labeling instructions are critical for improving dataset quality but are often lacking.
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