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AI-Driven Innovations for Early Sepsis Detection by Combining Predictive Accuracy With Blood Count Analysis in an Emergency Setting: Retrospective Study.
J Med Internet Res
January 2025
Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
Background: Sepsis, a critical global health challenge, accounted for approximately 20% of worldwide deaths in 2017. Although the Sequential Organ Failure Assessment (SOFA) score standardizes the diagnosis of organ dysfunction, early sepsis detection remains challenging due to its insidious symptoms. Current diagnostic methods, including clinical assessments and laboratory tests, frequently lack the speed and specificity needed for timely intervention, particularly in vulnerable populations such as older adults, intensive care unit (ICU) patients, and those with compromised immune systems.
View Article and Find Full Text PDFEvaluation of MRX PT DOAC as a new screening method for detecting interferences in thrombophilia analyses.
Scand J Clin Lab Invest
January 2025
Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden.
Background: Direct oral anticoagulants (DOACs) can interfere with coagulation analyses, causing erroneous results such as false-positive lupus anticoagulant and false-normal antithrombin, threatening patient safety when overlooked. A test using a prothrombin time quotient method to detect DOAC presence in plasma samples is now commercially available, the MRX PT DOAC, with the result expressed as Clot Time Ratio (CTR).
Objectives: Evaluate the ability of MRX PT DOAC to identify interfering apixaban or rivaroxaban concentrations, identify non-interfering or interfering patient samples, and detect whether a patient is on DOAC treatment.
Sleep apnea screening through a news portal using the STOP-bang questionnaire.
Sleep Breath
January 2025
Pulmonary Medicine, Universidad Austral, Hospital Universitario Austral, Pilar, Argentina.
Purpose: Obstructive sleep apnea (OSA) affects up to 936 million adults globally and is linked to significant health risks, including neurocognitive impairment, cardiovascular diseases, and metabolic conditions. Despite its prevalence, OSA remains largely underdiagnosed. This study aimed to enhance OSA awareness and risk assessment using the STOP-Bang questionnaire in a telemedicine format.
View Article and Find Full Text PDFExploring Multi-Pathology Brain Segmentation: From Volume-Based to Component-Based Deep Learning Analysis.
J Imaging
December 2024
Technology Department, CERN, 1211 Geneva, Switzerland.
Detection and segmentation of brain abnormalities using Magnetic Resonance Imaging (MRI) is an important task that, nowadays, the role of AI algorithms as supporting tools is well established both at the research and clinical-production level. While the performance of the state-of-the-art models is increasing, reaching radiologists and other experts' accuracy levels in many cases, there is still a lot of research needed on the direction of in-depth and transparent evaluation of the correct results and failures, especially in relation to important aspects of the radiological practice: abnormality position, intensity level, and volume. In this work, we focus on the analysis of the segmentation results of a pre-trained U-net model trained and validated on brain MRI examinations containing four different pathologies: Tumors, Strokes, Multiple Sclerosis (MS), and White Matter Hyperintensities (WMH).
View Article and Find Full Text PDFAutomatic Aortic Valve Extraction Using Deep Learning with Contrast-Enhanced Cardiac CT Images.
J Cardiovasc Dev Dis
December 2024
Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo 060-8638, Japan.
Purpose: This study evaluates the use of deep learning techniques to automatically extract and delineate the aortic valve annulus region from contrast-enhanced cardiac CT images. Two approaches, namely, segmentation and object detection, were compared to determine their accuracy.
Materials And Methods: A dataset of 32 contrast-enhanced cardiac CT scans was analyzed.
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