A two-stage deep learning framework automates TMPFC calculation from angiography and matches expert manual measurements with r=0.98 in a 655-patient multi-center cohort.
For stenosis detection network, the training set contained opacified frames with manually annotated bounding boxes
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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
A two-stage deep learning framework automates TMPFC calculation from angiography and matches expert manual measurements with r=0.98 in a 655-patient multi-center cohort.