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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7720050 | PMC |
| http://dx.doi.org/10.21037/hbsn.2020.03.23 | DOI Listing |
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Revolutionizing Cardiology: The Role of Artificial Intelligence in Echocardiography.
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Guthrie Cortland Medical Center, Cortland, NY 13045, USA.
Artificial intelligence (AI) in echocardiography represents a transformative advancement in cardiology, addressing longstanding challenges in cardiac diagnostics. Echocardiography has traditionally been limited by operator-dependent variability and subjective interpretation, which impact diagnostic reliability. This study evaluates the role of AI, particularly machine learning (ML), in enhancing the accuracy and consistency of echocardiographic image analysis and its potential to complement clinical expertise.
View Article and Find Full Text PDFMicrofluidic Integration of Magnetically Functionalized Microwires for Flow Cytometry Protein Quantification.
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January 2025
Life Sciences Division, National Research Council of Canada, 75 de Mortagne Boulevard, Boucherville, QC J4B 6Y4, Canada.
A novel approach to protein quantification utilizing a microfluidic platform activated by a magnetic assembly of functionalized magnetic beads around soft magnetic capture centers is presented. Functionalized magnetic beads, known for their high surface area and facile manipulation under external magnetic fields, are injected inside microfluidic channels and immobilized magnetically on the surface of glass-coated soft magnetic microwires placed along the symmetry axis of these channels. A fluorescent (Cy5) immunomagnetic sandwich ELISA is then performed by sequentially flowing the sample and all necessary reagents in the microfluidic channels.
View Article and Find Full Text PDFDeep Learning in Oral Hygiene: Automated Dental Plaque Detection via YOLO Frameworks and Quantification Using the O'Leary Index.
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Facultad de Informática, Universidad Autónoma de Querétaro, Querétaro 76230, Mexico.
: Oral diseases such as caries, gingivitis, and periodontitis are highly prevalent worldwide and often arise from plaque. This study focuses on detecting three plaque stages-new, mature, and over-mature-using state-of-the-art YOLO architectures to enhance early intervention and reduce reliance on manual visual assessments. : We compiled a dataset of 531 RGB images from 177 individuals, captured via multiple mobile devices.
View Article and Find Full Text PDFAutomated AI-based image analysis for quantification and prediction of interstitial lung disease in systemic sclerosis patients.
Respir Res
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National Heart and Lung Institute, Imperial College London, London, UK.
Background: Systemic sclerosis (SSc) is a rare connective tissue disease associated with rapidly evolving interstitial lung disease (ILD), driving its mortality. Specific imaging-based biomarkers associated with the evolution of lung disease are needed to help predict and quantify ILD.
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Fully automated coronary artery calcium score and risk categorization from chest CT using deep learning and multiorgan segmentation: A validation study from National Lung Screening Trial (NLST).
Int J Cardiol Heart Vasc
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Department of Radiology, Frimley Park Hospital NHS Foundation Trust, Camberley, Surrey, UK.
Background: The National Lung Screening Trial (NLST) has shown that screening with low dose CT in high-risk population was associated with reduction in lung cancer mortality. These patients are also at high risk of coronary artery disease, and we used deep learning model to automatically detect, quantify and perform risk categorisation of coronary artery calcification score (CACS) from non-ECG gated Chest CT scans.
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