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Background and Aims Patients with plaque rupture (PR) present with different cardiovascular risks, clinical strategies, and outcomes from those with plaque erosion (PE). However, there are lack of noninvasive biomarkers to distinguish PE from PR. Methods A prospective analysis of 382 patients with ST-segment elevation myocardial infarction (STEMI) was conducted. Of these patients, 262 and 120 presented with PR and PE, respectively. An additional 83 patients diagnosed with stable angina pectoris were enrolled as control group. Peripheral blood monocytes were collected pre-percutaneous coronary intervention and used to evaluate the mRNA expression of IL-4 , IL-10 , IL-1β , and TNF-α in all patients. Results STEMI patients had higher IL-4 , IL-10 , IL-1β , and TNF-α expression than the control patients. The mRNA levels of IL-4 , IL-1β , and TNF-α were significantly higher in PR patients than PE; however, no significant difference was observed in IL-10 between PE and PR. The areas under the receiver-operating characteristic curves for IL-4 , IL-1β , and TNF-α for PR versus PE were 0.685, 0.747, and 0.895, respectively. At the cut-off value of 2.52, TNF-α demonstrated a sensitivity of 70.61% and specificity of 93.33% for discriminating PR from PE patients. When added to the model of established clinical risk factors, TNF-α significantly improved the predictive accuracy of PR. Multivariable logistic regression analysis indicated that TNF-α mRNA level was independently associated with PR (odds ratio, 3.09; 95% confidence interval, 2.29–4.16; p < 0.001). Conclusion The inflammatory response of peripheral blood mononuclear cells in patients with PR was higher than that in patients with PE. TNF-α may be a potential biomarker for predicting PR that could facilitate risk stratification and management in STEMI patients.

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Acute coronary syndrome is the leading cause of cardiac death and has a significant impact on patient prognosis. Early identification and proper management are key to ensuring better outcomes and have improved significantly with the development of various cardiovascular imaging modalities. Recently, the use of artificial intelligence as a method of enhancing the capability of cardiovascular imaging has grown. AI can inform the decision-making process, as it enables existing modalities to perform more efficiently and make more accurate diagnoses. This review demonstrates recent applications of AI in cardiovascular imaging to facilitate better patient care.