Acute Coronary Syndrome

Explainable ML for ACS culprit plaques: a multidimensional CCTA model highlighting hemodynamic increment.

To develop an interpretable, multi-parameter machine learning (ML) model that integrates plaque morphology, composition, perivascular inflammation, and comprehensive hemodynamic descriptors to identify future acute coronary syndrome (ACS) culprit plaques and evaluate the incremental predictive value of hemodynamic parameters. A total of 217 lesions from 88 patients with pre-ACS coronary computed t...

Toward practical screening of mortality risk: Insights from interpretable machine learning in NHANES.

BACKGROUND: Efficient community-based screening for individuals at high risk of mortality is a major public health challenge. While many predictors have been proposed, there is limited consensus on which factors are both robust and practical for population screening. This study applied interpretable machine learning to identify efficient predictors of all-cause and cardiovascular mortality in a na...

Deep Learning and Machine Learning for Differentiation Between Contrast Extravasation and Hemorrhagic Transformation in Post-Thrombectomy Stroke CT.

BACKGROUND: Mechanical thrombectomy (MT) improves outcomes in acute ischemic stroke (AIS) but often results in hyperdensities on non-contrast CT (NCCT...

Development and validation of a machine learning model for prediction of 1-year mortality following ST-elevation myocardial infarction: a retrospective cohort study.

OBJECTIVES: To develop a machine learning (ML)-based risk prediction model for 1-year mortality in ST-elevation myocardial infarction (STEMI) patients...

Explainable machine learning model based on clinical and radiological features for predicting hematoma expansion or rebleeding after decompressive craniectomy in traumatic brain injury: a bicentric cohort study.

BACKGROUND: Hematoma expansion or rebleeding after decompressive craniectomy (DC) is a critical determinant of poor prognosis in traumatic brain injur...

[Cardiology : what's new in 2025].

In 2025, significant progress has been made in the management of heart failure and cardiovascular diseases, driven by the emergence of new treatments ...

Computerized Self-Reported Medical History Taking to Support Early Rule Out of Major Adverse Cardiac Events in Patients With Acute Chest Pain: Post Hoc Analysis of the CLEOS-CPDS Prospective Cohort Study.

BACKGROUND: Self-reported, computerized history taking (CHT) may enable efficient collection of medical histories for acute chest pain management. OBJ...

Practical machine learning model for early and accurate prediction of disseminated intravascular coagulation before its progression to an overt stage.

BACKGROUND AND AIMS: In patients with sepsis, anticoagulant therapy is expected to have maximal efficacy when administered before the development of s...

Coronary revascularization: a long-term perspective.

This paper will forecast advancements in coronary revascularization by 2040, drawing on historical trends and recent breakthroughs. Having forecasted ...

Prehospital Blood Collection for High Sensitivity Cardiac Troponin Measurement in Patients with Acute Chest Pain.

OBJECTIVES: High sensitivity cardiac troponin (hs-cTn) measures are used in the emergency department (ED) to evaluate patients with acute chest pain. ...

High-Resolution MRI Using Artificial Intelligence-Assisted Acceleration and Radial Dynamic Contrast Enhancement for Improved Detection of Pituitary Microadenomas in Cushing's Disease.

BACKGROUND AND PURPOSE: Accurate detection of pituitary microadenomas is critical for the diagnosis and treatment of Cushing's disease (CD). However, ...

[Application of molecularly imprinted polymers-based sensors for determination of acute coronary syndrome biomarkers].

Cardiovascular diseases （CVDs） are among the leading cause of global morbidity and mortality. Due to their high prevalence and often asymptomatic prog...

Letter: Deep Learning Model Significantly Improves MAGGIC Score for Predicting Contrast-Induced Nephropathy in STEMI Patients.

Interpretable machine-learning risk prediction of unplanned extubation among cancer patients with peripherally inserted central catheters.

Unplanned extubation of peripherally inserted central catheters (PICC-UE) in patients with cancer has been linked to factors including women, diabetes...

Artificial intelligence risk stratification from dynamic digital subtraction angiography radiomics predicts pulmonary embolism and associates with clinical outcomes in deep vein thrombosis: A retrospective cohort study.

OBJECTIVE: Current risk stratification for lower extremity deep vein thrombosis remains limited, often failing to identify high-risk patients for impe...

Multisite derivation of a machine learning algorithm using high sensitivity troponin to predict major adverse cardiac events in the emergency department.

OBJECTIVE: To develop a machine learning (ML) algorithm to stratify risk for major adverse cardiac events (MACE) within 30 days in emergency departmen...

Prediction of mortality, bleeding, and ischaemic events in patients with cancer and acute coronary syndrome: a model development and validation study.

BACKGROUND: Accurate assessment of mortality, bleeding, and atherothrombotic risk in patients with cancer and acute coronary syndrome could inform nov...

Investigation of Bias Using ChatGPT-4 in Pain Management and Patient Profiling.

BACKGROUND: Large language models, such as ChatGPT (cGPT), are being integrated increasingly into clinical workflows and medical education. However, c...

Toward Smart and Adaptive Endovascular Devices: The Role of Digital Twins in Precision Vascular Medicine.

Endovascular implants, such as stents, flow diverters, and endografts, have reshaped cardiovascular therapy but still face trade-offs in restenosis, t...

Local-Hybrid Functional With a Composite Local Mixing Function Built From a Neural Network and a Strong-Correlation Model.

Due to their position-dependent admixture of the exact-exchange (EXX) energy density, local hybrid functionals (LHs) enable a flexible balance between...