ArticleCardiovascular diabetology2026
CORAL: a machine learning algorithm to identify coronary microvascular disease from doppler coronary blood flow patterns.
Article in Cardiovascular diabetology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe direct diagnosis of coronary microvascular disease (CMD) in patients has remained elusive, relying on combinatorial criteria for diagnosis. Recent data in mice suggested that coronary blood flow (CBF) patterns harbor "fingerprints" that may be novel non-invasive indicators of CMD.
objectiveThe goal of this study was to test the hypothesis that machine learning (ML) of CBF patterns can directly and accurately identify CMD.
methodsTraining and test data for ML were obtained from control and diabetic db/db mice at early (12 wks), established (16 wks), and senescent (36 wks) ages with CMD (n = 460). Independent test data were obtained in a western-diet-fed (WD) mouse model of CMD (n = 40). All mice were evaluated for CBF and cardiac function by echocardiography and pressure-volume (PV) loop catheterization. CBF images were processed and input into CORAL (Coronary Risk Assessment with Learning), a ML classifier with optional input of cardiac function features via a multilayer perceptron.
resultsCBF pattern analysis using CORAL yielded remarkable accuracy of 100% at 12 weeks of age - early in CMD. CORAL correctly identified established CMD with 81% and 83% accuracy in the test and independent test data sets, respectively. The inclusion of cardiac function increased accuracy up to 89% and 90% in the test and independent test data, respectively.
conclusionsThis study demonstrates a novel ability of CORAL to unlock previously unutilized features of the CBF pattern that can accurately identify CMD early in disease progression. We envision further refinements to CORAL could lead to direct accurate CMD diagnoses in patients.
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