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AI-based Histologic Heterogeneity of Microvascular Obstruction at Cardiac MRI for Predicting MACEs: A Multicenter

Bing-Hua Chen1, Shu-Lin Li2, Jin-Yi Xiang1

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An artificial intelligence (AI) model accurately predicts major adverse cardiovascular events (MACEs) after ST-elevation myocardial infarction (STEMI) by analyzing microvascular obstruction (MVO) heterogeneity. This AI-driven radiomic score offers superior prognostic value compared to traditional methods.

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Area of Science:

  • Cardiology
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Microvascular obstruction (MVO) after ST-elevation myocardial infarction (STEMI) is linked to poor outcomes.
  • Manual MVO quantification is laborious and misses microvascular injury details.

Purpose of the Study:

  • To assess an AI model for automated MVO segmentation and radiomic feature extraction.
  • To evaluate the AI model's capability in predicting major adverse cardiovascular events (MACEs) by decoding microvascular damage.

Main Methods:

  • Utilized a multicenter retrospective cohort of 843 STEMI patients with MVO undergoing cardiac MRI.
  • Applied an AI model for automated MVO segmentation and extracted 1595 radiomic features.
  • Developed a radiomic score (radscore) using LASSO regression for MVO heterogeneity analysis.

Main Results:

  • The AI-derived radscore effectively predicted MACEs, outperforming conventional MVO volume quantification.
  • Higher radscores were observed in patients who experienced MACEs (P < .001).
  • The radscore independently predicted MACEs (HR, 4.20) and improved risk stratification (C-index increased from 0.77 to 0.80).

Conclusions:

  • AI-automated MVO radiomic analysis provides a robust and efficient method for predicting MACE risk in STEMI patients.
  • This approach surpasses traditional quantitative assessments in prognostic accuracy.
  • The AI model aids in better understanding and managing microvascular injury heterogeneity.