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関連する概念動画

AC Sources01:20

AC Sources

4.2K
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...
4.2K
Resistor in an AC Circuit01:31

Resistor in an AC Circuit

3.2K
An alternating emf or voltage source is needed to supply an alternating current (AC) to a circuit. A coil of wire rotating in a magnetic field at a constant angular speed represents such a source. It also generates a sinusoidal alternating emf and serves as an industrial alternator.
One-way current through the meter is measured using diodes. A diode is a device with better conductivity in one direction compared to the other; in its ideal state, it has zero resistance in one direction and allows...
3.2K
Power in an AC Circuit01:26

Power in an AC Circuit

2.3K
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.
In a DC circuit, there is no sinusoidal waveform associated with the supply; the voltages and currents are...
2.3K
RMS Value in AC Circuit01:13

RMS Value in AC Circuit

3.5K
The root mean square (RMS) value is a measure of the effective or average value of an alternating current (AC) waveform. In AC circuits, the voltage or current waveform constantly changes direction and magnitude, making it difficult to describe with a single value. The RMS value provides a convenient way to calculate the equivalent DC voltage or current that would produce the same heating effect in a resistor as the AC waveform.
Mathematically, the RMS value of an AC waveform is the square root...
3.5K
Conservation of AC Power01:15

Conservation of AC Power

687
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
687
Capacitor in an AC Circuit01:23

Capacitor in an AC Circuit

3.9K
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.
Consider a purely capacitive circuit consisting...
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A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis
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A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis

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ACSの罪悪者プラークの説明可能なML:多次元CCTAモデルで,血液動力学的インクリメントを強調しています.

Meijing Wu1, Aoxue Chen1, Yanan Gui1

  • 1Xuzhou Medical University, 209 Tongshan Road, Xuzhou, Jiangsu, China.

The international journal of cardiovascular imaging
|February 13, 2026
PubMed
まとめ

プラークの特徴と血液動力学を統合した機械学習モデルは,急性冠動脈症候群 (ACS) の加害者のプラークを予測することができます. 総合的な血液動力学的評価は,プラークの特徴だけで予測の精度を大幅に改善します.

キーワード:
急性冠動脈症候群とは冠動脈コンピュータトモグラフィー アンジオグラフィーヘモダイナミクス (血動力学)機械学習 (Machine Learning) とは,機械学習 (Machine Learning) について学ぶことです.冠周周脂肪組織である.

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科学分野:

  • 心血管画像検査についてです.
  • 医学における機械学習
  • 計算式流体力学について

背景:

  • 脆弱なプラークを特定することは,急性冠動脈症候群 (ACS) の予防に不可欠です.
  • 現在の方法では,プラークの形態学,組成,および血液動力学的力の包括的な統合が欠けていることが多い.
  • 機械学習 (ML) は,リスク予測を改善するために複雑なデータセットを統合する可能性を秘めています.

研究 の 目的:

  • ACSの犯罪者プラークの識別のためにプラークの特徴と血液動力学を統合した解釈可能なMLモデルを開発する.
  • ACSリスク層分化のヘモダイナミックパラメータのインクリメンタル予測値を評価する.
  • 異なるML分類者のパフォーマンスを評価し,開発されたモデルの解釈性を評価する.

主な方法:

  • 冠動脈コンピュータトモグラフィ・アンジオグラフィ (CCTA) を用いた88人の患者の217の病変の分析.
  • 解剖学的特徴の抽出,プラーク組成 (脂肪減弱指数 (FAI) を含む),および計算式流体動力学 (CFD) による血動力学的指標 (例えば,壁切断ストレス (WSS)) を導出しました.
  • ランダムフォレストを用いた3つのプログレッシブMLモデルの開発と比較,特性の選択と解釈可能性のためのSHAP.

主要な成果:

  • プラークの特徴と血液動力学を統合したランダムフォレストモデルは,優れたパフォーマンスを示しました.
  • Shapley Additive Explanations (SHAP) は,WSSとFAIを主要な予測要因として特定しました.
  • 血液動力学統合モデルでは,FAI統合モデル (デルタAUC=0.162,P=0.035) と比較して,差別と再分類が著しく改善されました.

結論:

  • 説明可能なマルチパラメータMLフレームワークは,ACSの犯罪者プラークを特定する有望なことを示しています.
  • 総合的な血液動力学的評価は,ACSイベントを予測する上で重要なインクリメンタル価値を提供します.
  • このアプローチは,イメージング,プラーク特性,およびCFDによる血液動力学を統合することで,心血管リスクの階層化を改善する可能性を強調しています.