Deep Learning-Based Automated Quantification of TIMI Myocardial Perfusion Frame Count (DL-TMPFC) from Coronary Angiography: A Novel Framework for Rapid Assessment of Microvascular Dysfunction
Quick Answer
This paper shows that The DL-TMPFC framework automates the quantification of TIMI Myocardial Perfusion Frame Count, significantly improving CMVD diagnosis accuracy with a bias of -0.93 frames and a correlation of r=0.98 against manual measurements.
Quick Take
The DL-TMPFC framework automates the quantification of TIMI Myocardial Perfusion Frame Count, significantly improving CMVD diagnosis accuracy with a bias of -0.93 frames and a correlation of r=0.98 against manual measurements. Validated on 655 patients, it enhances clinical workflow by eliminating observer dependence and enabling rapid, objective assessments of microvascular dysfunction.
Key Points
- DL-TMPFC automates TMPFC calculation, enhancing clinical feasibility and accuracy.
- Validated on 655 patients, including 100 with confirmed CMVD.
- Achieved a correlation of r=0.98 with expert manual measurements.
- Eliminates observer dependence, providing immediate diagnostic information.
- Facilitates quantitative risk stratification across various coronary pathologies.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2605. 24012v1 Announce Type: new Abstract: Aims: Coronary microvascular dysfunction (CMVD) affects approximately 40%-60% of patients with ischemia and non-obstructive coronary arteries, yet diagnosis remains challenging due to reliance on invasive functional testing or subjective Thrombolysis In Myocardial Infarction (TIMI) flow grade.
The TIMI Myocardial Perfusion Frame Count (TMPFC) offers an objective, angiography-based quantitative measure of CMVD, but its clinical translation is hindered by cumbersome manual calculation and insufficient validation. This study aims to develop and validate a deep learning-powered TMPFC calculation (DL-TMPFC), enabling integration into clinical workflows. Methods and results: DL-TMPFC framework comprised two components. A stenosis detection network first excluded obstructive coronary artery disease (CAD).
A territory-aware segmentation network then identified perfusion territories and TMPFC calculation module automatically determined the first and last frames from angiographic sequences. The framework was validated in a cohort of 655 patients (445 of obstructive CAD, 100 of confirmed CMVD, 110 of control group) from three independent institutions. DL-TMPFC showed excellent agreement with expert manual measurements (bias: -0. 93 frames; 95% LoA: -5. 33 to +3. 47; r =0. 98).
DL-TMPFC markedly enhanced clinical feasibility by fully automating TMPFC and removing observer dependence. Clinically, DL-TMPFC accurately identified CMVD across a full spectrum of coronary pathologies and captured the continuous severity of CMVD beyond binary classification, enabling quantitative risk stratification. Conclusion: DL-TMPFC enabled automatic, standardized, and accurate quantification of CMVD directly from routine angiography.
By providing an automatic and objective measure, this tool provided immediate diagnostic information for timely recognition and management of CMVD in clinical practice.
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