Novel AI model enhances diagnosis of plaque erosion in patients with acute coronary syndromes
South Korea: A newly developed "transformer"-based DL (deep learning) model may help cardiologists accurately diagnose plaque erosion in patients with acute coronary syndromes (ACS), according to a recent study published in JACC: Cardiovascular Interventions.
In recent years, a concept that the underlying pathophysiology in some patients with acute coronary syndromes could be plaque erosion rather than plaque rupture has emerged. ACS caused by plaque erosion could be managed conservatively (reduction of thrombus burden followed by aggressive medical management with anti-platelet and lipid-lowering therapy) without stenting. However, this concept still needs to be fully supported by trial evidence.
Plaque erosion diagnosis requires expertise in OCT (optical coherence tomographic) image interpretation. In addition, the current DL approaches for OCT image interpretation are based on a single frame without information integration from adjacent frames. The key to furthering this concept is to recognise plaque erosion more easily in the acute clinical setting.
In the study, Sangjoon Park from the Korea Advanced Institute of Science and Technology in Daejeon, South Korea, and colleagues deployed a deep learning approach (called a "Transformer") that enables recapitulating to some degree the human method but incorporating information from multiple frames, and facilitate an accurate diagnosis of plaque erosion.
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