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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
February 2025
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.
Materials And Methods: Automated calcium quantification was performed using a neural network based on Mask regions with convolutional neural networks (R-CNN) for multiorgan segmentation.
Donor hepatitis C status is not associated with an increased risk of acute rejection in kidney transplantation.
Surg Pract Sci
March 2024
Department of Surgery, Division of Multiorgan Transplant and Hepatobiliary Surgery, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555-0655, USA.
Introduction: In renal transplantation, donor hepatitis C virus (HCV) status is crucial to consider when selecting a recipient given the high likelihood of transmission. We analyzed the effect of donor HCV status on post-renal transplant rejection and virologic infectious outcomes using electronic health record data from multiple US health care organizations.
Methods: Using real world data from electronic health records of renal transplant recipients, a propensity score-matched case-control study of one-year renal transplant outcomes was conducted on cohorts of HCV-negative recipients who received an organ from an HCV-positive donor (HCV D+/R-) versus from an HCV-negative donor (HCV D-/R-).
Medical image segmentation based on frequency domain decomposition SVD linear attention.
Sci Rep
January 2025
Affiliated Hospital 6 of Nantong University, Yancheng Third People's Hospital, Yancheng, 224001, Jiangsu, China.
Convolutional Neural Networks (CNNs) have achieved remarkable segmentation accuracy in medical image segmentation tasks. However, the Vision Transformer (ViT) model, with its capability of extracting global information, offers a significant advantage in contextual information compared to the limited receptive field of convolutional kernels in CNNs. Despite this, ViT models struggle to fully detect and extract high-frequency signals, such as textures and boundaries, in medical images.
View Article and Find Full Text PDFCardiac amyloidosis: when to suspect and how to confirm.
J Cardiovasc Med (Hagerstown)
February 2025
Center for Diagnosis and Treatment of Cardiomyopathies, Cardiovascular Department, Azienda Sanitaria Universitaria Giuliano-Isontina (ASUGI), University of Trieste.
Diagnosing cardiac amyloidosis (CA) is challenging because of its phenotypic heterogeneity, multiorgan involvement requiring interaction among experts in different specialties and subspecialties, lack of a single noninvasive diagnostic tool, and still limited awareness in the medical community. Missing or delaying the diagnosis of CA may profoundly impact on patients' outcomes, as potentially life-saving treatments may be omitted or delayed. The suspicion of CA should arise when "red flags" for this condition are present, together with increased left ventricular wall thickness.
View Article and Find Full Text PDFMicrophysiological system to address the opioid crisis: A novel multi-organ model of acute opioid overdose and recovery.
Curr Res Toxicol
December 2024
University of Central Florida, NanoScience Technology Center, 12424 Research Parkway, Suite 400, Orlando, FL 23826, United States.
Opioids have been the primary method used to manage pain for hundreds of years, however the increasing prescription rate of these drugs in the modern world has led to a public health crisis of overdose related deaths. Naloxone is the current standard treatment for opioid overdose rescue, but it has not been fully investigated for potential off-target toxicity effects. The current methods for pharmaceutical development do not correlate well with pre-clinical animal studies compared to clinical results, creating a need for improved methods for therapeutic evaluation.
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