AIMC Topic: Coronary Angiography

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Can large language models be new supportive tools in coronary computed tomography angiography reporting?

Clinical imaging
The advent of large language models (LLMs) marks a transformative leap in natural language processing, offering unprecedented potential in radiology, particularly in enhancing the accuracy and efficiency of coronary artery disease (CAD) diagnosis. Wh...

Integrated deep learning model for automatic detection and classification of stenosis in coronary angiography.

Computational biology and chemistry
Coronary artery disease poses a significant threat to human health. In clinical settings, coronary angiography remains the gold standard for diagnosing coronary heart disease. A crucial aspect of this diagnosis involves detecting arterial narrowings....

Effective descriptor extraction strategies for correspondence matching in coronary angiography images.

Scientific reports
The importance of 3D reconstruction of coronary arteries using multiple coronary angiography (CAG) images has been increasingly recognized in the field of cardiovascular disease management. This process relies on the camera matrix's optimization, nee...

Rationale and design of the artificial intelligence scalable solution for acute myocardial infarction (ASSIST) study.

Journal of electrocardiology
BACKGROUND: Acute coronary syndrome (ACS), specifically ST-segment elevation myocardial infarction is a major cause of morbidity and mortality throughout Europe. Diagnosis in the acute setting is mainly based on clinical symptoms and physician's inte...

Development of machine learning models for fractional flow reserve prediction in angiographically intermediate coronary lesions.

Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions
BACKGROUND: Fractional flow reserve (FFR) represents the gold standard in guiding the decision to proceed or not with coronary revascularization of angiographically intermediate coronary lesion (AICL). Optical coherence tomography (OCT) allows to car...

Predicting angiographic coronary artery disease using machine learning and high-frequency QRS.

BMC medical informatics and decision making
AIM: Exercise stress ECG is a common diagnostic test for stable coronary artery disease, but its sensitivity and specificity need to be further improved. In this paper, we construct a machine learning model for the prediction of angiographic coronary...

Development of Clinically Validated Artificial Intelligence Model for Detecting ST-segment Elevation Myocardial Infarction.

Annals of emergency medicine
STUDY OBJECTIVE: Although the importance of primary percutaneous coronary intervention has been emphasized for ST-segment elevation myocardial infarction (STEMI), the appropriateness of the cardiac catheterization laboratory activation remains subopt...

Noninvasive and fast method of calculation for instantaneous wave-free ratio based on haemodynamics and deep learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Instantaneous wave-free ratio (iFR) is a new invasive indicator of myocardial ischaemia, and its diagnostic performance is as good as the "gold standard" of myocardial ischaemia diagnosis: fractional flow reserve (FFR). iFR...

Deep learning reconstruction for coronary CT angiography in patients with origin anomaly, stent or bypass graft.

La Radiologia medica
PURPOSE: To develop and validate a deep learning (DL)-model for automatic reconstruction for coronary CT angiography (CCTA) in patients with origin anomaly, stent or bypass graft.