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http://dx.doi.org/10.1002/jhbp.12195 | DOI Listing |
Pol J Radiol
July 2025
Department of Neurosurgery, Functional and Stereotactic Neurosurgery, CM UMK Bydgoszcz, Poland.
Diffusion tensor imaging (DTI) and tractography are powerful non-invasive techniques for studying the human brain's white matter pathways. The uncinate fasciculus (UF) is a key frontotemporal tract involved in emotion regulation, memory, and language. Despite advancements, challenges persist in accurately mapping its structure and function due to methodological limitations in data acquisition and analysis.
View Article and Find Full Text PDFQuant Imaging Med Surg
September 2025
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China.
Background: Thyroid nodules are a common clinical concern, with accurate diagnosis being critical for effective treatment and improved patient outcomes. Traditional ultrasound examinations rely heavily on the physician's experience, which can lead to diagnostic variability. The integration of artificial intelligence (AI) into medical imaging offers a promising solution for enhancing diagnostic accuracy and efficiency.
View Article and Find Full Text PDFCurr Atheroscler Rep
September 2025
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
Purpose Of Review: To define the emerging role of artificial intelligence-enhanced electrocardiography (AI-ECG) in advancing population-level screening for atherosclerotic cardiovascular disease (ASCVD), we provide a comprehensive overview of its role in predicting major adverse cardiovascular events and detecting subclinical coronary artery disease. We also outline the clinical, methodological, and implementation challenges that must be addressed for widespread adoption.
Recent Findings: State-of-the-art AI-ECG models exhibit high accuracy, correctly re-classifying patients deemed 'low risk' by traditional risk models.
Circulation
September 2025
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT; Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT; Secti
Background: Artificial intelligence (AI)-enhanced electrocardiogram (ECG) models are often designed to detect specific anatomical and functional cardiac abnormalities. Understanding the selectivity of their phenotypic associations is essential to inform their clinical use. Here, we sought to assess whether AI-ECG models function as condition-specific classifiers or broader cardiovascular risk markers.
View Article and Find Full Text PDFWater Res
August 2025
NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Shandong, Jinan 250012, China; National Glycoengineering Research Center, Shandong University, Shandong, Jinan 250012, China; Key Laborato
Pharmaceutical wastewater commonly contains volatile organic compounds (VOCs) such as methanol, isopropanol, and acetone, which pose serious threats to wastewater treatment efficiency, ecological systems, and human health. Therefore, the rapid and robust quantitative monitoring of VOCs in wastewater has become an urgent necessity to ensure treatment effectiveness and environmental safety. However, traditional detection methods suffer from issues such as complex operations and delayed responses, failing to meet the monitoring requirements of industrial sites.
View Article and Find Full Text PDF