Key result
AI myocardial attenuation mapping raises emergency CTA ischemia detection sensitivity by ~12 percentage points.
Why the study?
To determine whether combining deep learning myocardial segmentation with attenuation-normalized myocardial mapping improves detection of myocardial ischemia and infarction on emergency ECG-gated CT angiography.
Does AI-based myocardial segmentation and attenuation mapping improve the detection of myocardial ischemia and infarction in patients with acute chest pain undergoing emergency CT angiography?
Observational (n=119)
Readers blinded to clinical outcomes and invasive coronary angiography results
Cases and readers randomly assigned in a multi-reader multi-case (MRMC) crossover design
No
Does AI-based myocardial segmentation and attenuation mapping improve the detection of myocardial ischemia and infarction in patients with acute chest pain undergoing emergency CT angiography?
Applying an AI-generated attenuation colormap to routine emergency ECG-gated CT angiography significantly improves diagnostic sensitivity and inter-reader agreement for detecting myocardial infarction and ischemia.
Answers are grounded in this paper and cite their sources.
May enhance ischemia detection on emergency CTA; hypothesis-generating and requires prospective validation before adoption.
Purpose: To investigate whether an AI-based approach combining deep learning myocardial segmentation with attenuation-normalized myocardial mapping (colormaps) improves detection of myocardial ischemia and infarction on emergency ECG-gated CT angiography. Materials and Methods: In this retrospective study, 119 patients with acute chest pain who underwent ECG-gated CT angiography to exclude pulmonary embolism or acute aortic syndrome and invasive coronary angiography within 48 h were included. A deep learning model (nnU-Net) was used for automatic left-ventricular myocardial segmentation, serving as the basis for voxel-wise attenuation normalization to generate AI-based myocardial attenuation maps. Six readers with varying experience levels evaluated all cases for myocardial hypoattenuation in a multi-reader, multi-case design, with and without AI-generated attenuation maps. Results: AI-based myocardial attenuation mapping increased mean sensitivity for detection of myocardial ischemia or infarction by 12% [IQR 2–20%] compared with standard CT interpretation alone. Sensitivity improved by 15% [IQR 10–22%] in STEMI (ST-Elevation Myocardial Infarction) and 11% [IQR −1–18%] in NSTEMI (Non-STEMI) cases. The AI-assisted approach resulted in the correct reclassification of 11% of patients and improved inter-reader agreement, particularly among less experienced readers, demonstrating reduced reader dependency. Conclusions: AI-based myocardial segmentation and attenuation mapping enhance the detection of myocardial ischemia and infarction on emergency CT angiography and improve inter-reader agreement. This AI-assisted image processing approach provides clinically meaningful decision support in acute chest pain imaging workflows.
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Segeroth et al. (2026) conducted an observational in Acute chest pain with possible acute coronary syndrome (n=119). AI-based myocardial segmentation and attenuation mapping (colormaps) vs. Standard CT interpretation without colormaps was evaluated on Mean sensitivity for detection of myocardial ischemia or infarction (12% increase (IQR 2-20%)). AI-based myocardial attenuation mapping increased the mean sensitivity for detecting myocardial ischemia or infarction on emergency CT angiography by 12% compared to standard interpretation alone.
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