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Updated: Jan 9, 2026

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition
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Murray Law-Based Quantitative Flow Ratio Proficiency in Clinical Practice: Validating Routine Diagnostic Performance

Dan Deng1,2, Yue Feng1,2, Min Zeng1,2

  • 1Department of Cardiovascular Medicine, Center for Circadian Metabolism and Cardiovascular Disease, Southwest Hospital Army Medical University Chongqing China.

Journal of the American Heart Association
|December 11, 2025
PubMed
Summary
This summary is machine-generated.

Operator experience impacts AI-driven quantitative flow ratio (QFR) accuracy. At least 170 vessels are needed for reliable QFR, with peak performance around 441 cases. Structured training is essential for clinical integration.

Keywords:
Murray law–based quantitative flow ratiocoronary physiologydiagnostic accuracyoperator learning curve

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Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Second-generation Murray law-based quantitative flow ratio (QFR) utilizes AI for improved angiography-derived physiological assessment.
  • The diagnostic reliability of AI-driven QFR in routine practice is influenced by operator learning curves.

Purpose of the Study:

  • To evaluate the impact of operator learning curves on the diagnostic reliability of AI-driven Murray law-based QFR.
  • To determine the number of cases required to achieve high diagnostic performance and stability.

Main Methods:

  • Consecutive patients with suspected myocardial ischemia underwent AI-driven QFR and fractional flow reserve measurement.
  • Diagnostic accuracy was assessed using receiver operating characteristic analysis and learning curves were analyzed by sequential data expansion.
  • Stabilization of diagnostic performance was evaluated by calculating first-order AUC differences.

Main Results:

  • AI-driven QFR demonstrated excellent diagnostic accuracy with vessel-level AUC of 0.92 and patient-level AUC of 0.91.
  • High diagnostic performance (AUC ≥0.90) was achieved after analyzing 170 vessels.
  • Peak performance and stability were reached around 441 vessels, with no significant deviation in incremental AUC values.

Conclusions:

  • Operator experience significantly influences the accuracy of AI-driven Murray law-based QFR.
  • A minimum of 170 analyzed vessels ensures high QFR reliability, with optimal performance around 441 cases.
  • Structured training is crucial for the effective clinical integration of AI-enhanced coronary physiology tools.