Artificial intelligence-based opportunistic detection of coronary artery stenosis on aortic computed tomography angiography in emergency department patients with acute chest pain

Article Article scientifique Article Artificial intelligence-based opportunistic detection of coronary artery stenosis on aortic computed tomography angiography in emergency department patients with acute chest pain eng 2023-09-07 2023-09 author Glessgen Carl 0000-0002-9836-7139 Department of Radiology, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil 4, Geneva 1205, Switzerland. 984067 author Boulougouri Marianthi Department of Radiology, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil 4, Geneva 1205, Switzerland. 429652 author Vallée Jean-Paul 0000-0001-7546-2013 Department of Radiology, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil 4, Geneva 1205, Switzerland. 29185 Département de radiologie et informatique médicale (RADIO) author Noble Stéphane Laurent 0000-0002-8018-2857 Department of Cardiology, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil 4, Geneva 1205, Switzerland. 87673 Département de médecine (DEMED) author Platon Alexandra Department of Radiology, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil 4, Geneva 1205, Switzerland. 554893 Département de radiologie et informatique médicale (RADIO) author Poletti Pierre-Alexandre Alois Department of Radiology, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil 4, Geneva 1205, Switzerland. 34563 Département de radiologie et informatique médicale (RADIO) author Paul Jean-François 0000-0001-8251-0511 Department of Radiology, Cardiac Imaging, Institut Mutualiste Montsouris, Paris 75014, France. author Deux Jean-François Department of Radiology, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil 4, Geneva 1205, Switzerland. 975465 oead088 10.1093/ehjopen/oead088 37744954 2752-4191 PMC10516619 https://academic.oup.com/ehjopen/article/3/5/oead088/7262737 Aims: To evaluate a deep-learning model (DLM) for detecting coronary stenoses in emergency room patients with acute chest pain (ACP) explored with electrocardiogram-gated aortic computed tomography angiography (CTA) to rule out aortic dissection. Methods and results: This retrospective study included 217 emergency room patients (41% female, mean age 67.2 years) presenting with ACP and evaluated by aortic CTA at our institution. Computed tomography angiography was assessed by two readers, who rated the coronary arteries as 1 (no stenosis), 2 (<50% stenosis), or 3 (≥50% stenosis). Computed tomography angiography was categorized as high quality (HQ), if all three main coronary arteries were analysable and low quality (LQ) otherwise. Curvilinear coronary images were rated by a DLM using the same system. Per-patient and per-vessel analyses were conducted. One hundred and twenty-one patients had HQ and 96 LQ CTA. Sensitivity, specificity, positive predictive value, negative predictive value (NPV), and accuracy of the DLM in patients with high-quality image for detecting ≥50% stenoses were 100, 62, 59, 100, and 75% at the patient level and 98, 79, 57, 99, and 84% at the vessel level, respectively. Sensitivity was lower (79%) for detecting ≥50% stenoses at the vessel level in patients with low-quality image. Diagnostic accuracy was 84% in both groups. All 12 patients with acute coronary syndrome (ACS) and stenoses by invasive coronary angiography (ICA) were rated 3 by the DLM. Conclusion: A DLM demonstrated high NPV for significant coronary artery stenosis in patients with ACP. All patients with ACS and stenoses by ICA were identified by the DLM. Cardiac-gated imaging techniques Chest pain Computed tomography angiography Coronary artery disease Deep learning Dewey 616.0757 Dewey 616 56 Département de médecine 221 62 Département de radiologie et informatique médicale 227 false 286 Imagerie cardiaque fonctionnelle 541 469 Radiologie des urgences 800 European heart journal open 3 5 a9021433-2144-4930-8c88-31574b91d493 Article (Published version) Public Article_scientifique_from_europePmc.pdf 2881863 application/pdf CC BY-NC-4.0 1cba84c5f52f10094db5a4226e60becd3294636c1e0da8a729a3c81905a8a3cb openAccess Faculté de médecine / Section de médecine clinique / Département de médecine Faculté de médecine / Section de médecine clinique / Département de radiologie et informatique médicale

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Artificial intelligence-based opportunistic detection of coronary artery stenosis on aortic computed tomography angiography in emergency department patients with acute chest pain	European heart journal open			2023	86	62

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Boulougouri, Marianthi