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Fully automated epicardial adipose tissue volume quantification with deep learning and relationship with CAC score and micro/macrovascular complications in people living with type 2 diabetes: the multicenter EPIDIAB study.
Cardiovascular diabetology
Sep 3, 2024
BACKGROUND: The aim of this study (EPIDIAB) was to assess the relationship between epicardial adipose tissue (EAT) and the micro and macrovascular complications (MVC) of type 2 diabetes (T2D).
Automation
Diabetic Angiopathies
Vascular Calcification
Computed Tomography Angiography
Pericardium
Reproducibility of Results
Predictive Value of Tests
Epicardial Adipose Tissue
Risk Factors
Coronary Angiography
Aged
Radiographic Image Interpretation, Computer-Assisted
Male
Coronary Artery Disease
Middle Aged
Adiposity
Risk Assessment
Deep Learning
Humans
Diabetes Mellitus, Type 2
Prognosis
Adipose Tissue
Female
Machine learning to identify precachexia and cachexia: a multicenter, retrospective cohort study.
Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Sep 3, 2024
BACKGROUND: Detection of precachexia is important for the prevention and treatment of cachexia. However, how to identify precachexia is still a challenge.
Advancing Tau PET Quantification in Alzheimer Disease with Machine Learning: Introducing THETA, a Novel Tau Summary Measure.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Sep 3, 2024
Alzheimer disease (AD) exhibits spatially heterogeneous 3- or 4-repeat tau deposition across participants. Our overall goal was to develop an automated method to quantify the heterogeneous burden of tau deposition into a single number that would be c...
Cost-Sensitive Weighted Contrastive Learning Based on Graph Convolutional Networks for Imbalanced Alzheimer's Disease Staging.
IEEE transactions on medical imaging
Sep 3, 2024
Identifying the progression stages of Alzheimer's disease (AD) can be considered as an imbalanced multi-class classification problem in machine learning. It is challenging due to the class imbalance issue and the heterogeneity of the disease. Recentl...
A retrospective evaluation of the potential of ChatGPT in the accurate diagnosis of acute stroke.
Diagnostic and interventional radiology (Ankara, Turkey)
Sep 2, 2024
PURPOSE: Stroke is a neurological emergency requiring rapid, accurate diagnosis to prevent severe consequences. Early diagnosis is crucial for reducing morbidity and mortality. Artificial intelligence (AI) diagnosis support tools, such as Chat Genera...
Machine learning-assisted diagnosis of parotid tumor by using contrast-enhanced CT imaging features.
Journal of stomatology, oral and maxillofacial surgery
Sep 2, 2024
PURPOSE: This study aims to develop a machine learning diagnostic model for parotid gland tumors based on preoperative contrast-enhanced CT imaging features to assist in clinical decision-making.
Super-Resolving and Denoising 4D flow MRI of Neurofluids Using Physics-Guided Neural Networks.
Annals of biomedical engineering
Sep 2, 2024
PURPOSE: To obtain high-resolution velocity fields of cerebrospinal fluid (CSF) and cerebral blood flow by applying a physics-guided neural network (div-mDCSRN-Flow) to 4D flow MRI.
Serum targeted metabolomics uncovering specific amino acid signature for diagnosis of intrahepatic cholangiocarcinoma.
Journal of pharmaceutical and biomedical analysis
Sep 2, 2024
Intrahepatic cholangiocarcinoma (iCCA) is a hepatobiliary malignancy which accounts for approximately 5-10 % of primary liver cancers and has a high mortality rate. The diagnosis of iCCA remains significant challenges owing to the lack of specific an...
Artificial Intelligence-Enhanced Risk Stratification of Cancer Therapeutics-Related Cardiac Dysfunction Using Electrocardiographic Images.
Circulation. Cardiovascular quality and outcomes
Sep 2, 2024
BACKGROUND: Risk stratification strategies for cancer therapeutics-related cardiac dysfunction (CTRCD) rely on serial monitoring by specialized imaging, limiting their scalability. We aimed to examine an application of artificial intelligence (AI) to...
Ventricular Dysfunction, Left
Trastuzumab
Cardiotoxicity
Anthracyclines
Ventricular Function, Left
Stroke Volume
Middle Aged
Risk Assessment
Retrospective Studies
Antineoplastic Agents
Female
Time Factors
Risk Factors
United States
Aged
Electrocardiography
Humans
Male
Predictive Value of Tests
Artificial Intelligence
Automated MRI-based segmentation of intracranial arterial calcification by restricting feature complexity.
Magnetic resonance in medicine
Sep 2, 2024
PURPOSE: To develop an automated deep learning model for MRI-based segmentation and detection of intracranial arterial calcification.
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