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http://dx.doi.org/10.1038/s41415-024-8191-0 | DOI Listing |
Ultrasound Med Biol
March 2025
Biomedical Engineering, Cardiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands; Medical Imaging, Department of Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft, the Netherlands. Electronic address:
Objective: Assessing myocardial perfusion in acute myocardial infarction is important for guiding clinicians in choosing appropriate treatment strategies. Echocardiography can be used due to its direct feedback and bedside nature, but it currently faces image quality issues and an inability to differentiate coronary macro- from micro-circulation. We previously developed an imaging scheme using high frame-rate contrast-enhanced ultrasound (HFR CEUS) with higher order singular value decomposition (HOSVD) that provides dynamic perfusion and vascular flow visualization.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
December 2024
School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Background: [F] Fluorodeoxyglucose (FDG) PET-CT is a clinical imaging modality widely used in diagnosing and staging lung cancer. The clinical findings of PET-CT studies are contained within free text reports, which can currently only be categorised by experts manually reading them. Pre-trained transformer-based language models (PLMs) have shown success in extracting complex linguistic features from text.
View Article and Find Full Text PDFJ Conserv Dent Endod
October 2024
Private Practice.
Photography is crucial in modern dentistry, facilitated by digital technology advancements. The two main types of dental photography are macrophotography and photomicrography. Photomicrography, particularly valuable for its high magnification capabilities, offers clear documentation of minute dental details critical for the diagnosis and treatment planning.
View Article and Find Full Text PDFStud Health Technol Inform
November 2024
University Politehnica Timişoara, Romania.
The present study explored alternative methods for photographing skin lesions in the absence of specialized instruments like dermatoscopes, aiming to enhance remote diagnostic capabilities, particularly in light of the increasing incidence of melanoma cases annually. Using two lenses attached to a smartphone camera, one macroscopic and the other microscopic, study images of nevus formations from one individual were captured, and, in the absence of a collaboration with a dermatologist, subsequently labeled as melanoma or non-melanoma using a Convolutional Neural Network (CNN) which was trained, with dermoscopic images of melanoma and non-melanoma formations, to see on which image set better performances would be attained. The CNN demonstrated better performance on microscopic images, with 75% of the dataset being labeled correctly, compared to the macroscopic one, with 63% of the dataset being labeled correctly.
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