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Related Concept Videos

AC Sources01:20

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Direct current is a flow of electric charge in only one direction and has a steady state of constant voltage in the circuit. Rectifiers, batteries, commutator-equipped generators, and fuel cells are some examples of devices that generate direct current. Nowadays, most applications use a time-varying voltage source. Alternating current is a flow of electric charge that periodically reverses direction. An alternating current is produced by an alternating emf that is generated in a power plant. If...
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In a DC circuit, the power consumed is simply the product of the DC voltage times the DC current, given in watts. However, the power consumed for AC circuits with reactive components is calculated differently. Since electrical power is the "rate" at which energy is used in a circuit, all electrical and electronic components and devices have a safe operating range for electrical power.
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The principle of power preservation is applicable to both ac and dc circuits. This principle, when applied to AC power, asserts that the complex, real, and reactive powers produced by the source are equal to the total complex, real, and reactive powers absorbed by the loads. When two load impedances are connected in parallel to an ac source V, the complex power provided by the source can be calculated using the relation
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Capacitor in an AC Circuit01:23

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A capacitor is charged by passing an electric current through it, which causes the plates to start accumulating an electrostatic charge. Since the strength of the charging current is maximum when the capacitor plates are uncharged and gradually decreases exponentially until the capacitor is fully charged, the charging process is neither instantaneous nor linear. The property of a capacitor to store a charge on its plates is called its capacitance.
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Explainable ML for ACS culprit plaques: a multidimensional CCTA model highlighting hemodynamic increment.

Meijing Wu1, Aoxue Chen1, Yanan Gui1

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Machine learning models integrating plaque characteristics and hemodynamics can predict acute coronary syndrome (ACS) culprit plaques. Comprehensive hemodynamic assessment significantly improves prediction accuracy over plaque features alone.

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Area of Science:

  • Cardiovascular Imaging
  • Machine Learning in Medicine
  • Computational Fluid Dynamics

Background:

  • Identifying vulnerable plaques is crucial for preventing acute coronary syndrome (ACS).
  • Current methods often lack comprehensive integration of plaque morphology, composition, and hemodynamic forces.
  • Machine learning (ML) offers potential for integrating complex datasets to improve risk prediction.

Purpose of the Study:

  • To develop an interpretable ML model integrating plaque features and hemodynamics for ACS culprit plaque identification.
  • To evaluate the incremental predictive value of hemodynamic parameters in ACS risk stratification.
  • To assess the performance of different ML classifiers and the interpretability of the developed model.

Main Methods:

  • Analysis of 217 lesions from 88 patients using coronary computed tomography angiography (CCTA).
  • Extraction of anatomical features, plaque composition (including fat attenuation index - FAI), and computational fluid dynamics (CFD) derived hemodynamic metrics (e.g., wall shear stress - WSS).
  • Development and comparison of three progressive ML models using Random Forest, with feature selection and SHAP for interpretability.

Main Results:

  • The Random Forest model integrating plaque features and hemodynamics demonstrated superior performance.
  • Shapley Additive Explanations (SHAP) identified WSS and FAI as key predictors.
  • The hemodynamic-integrated model significantly improved discrimination and reclassification compared to the FAI-integrated model (Delta AUC = 0.162, P = 0.035).

Conclusions:

  • An explainable, multi-parameter ML framework shows promise for identifying ACS culprit plaques.
  • Comprehensive hemodynamic assessment provides critical incremental value in predicting ACS events.
  • This approach highlights the potential of integrating imaging, plaque characteristics, and CFD-derived hemodynamics for improved cardiovascular risk stratification.