Multicenter evaluation of interpretable AI for coronary artery disease diagnosis from PET biomarkers - Takeaways - MDSpire

Multicenter evaluation of interpretable AI for coronary artery disease diagnosis from PET biomarkers

  • By

  • Wenhao Zhang

  • Jacek Kwiecinski

  • Aakash Shanbhag

  • Robert J. H. Miller

  • Shiva Mostafavi

  • Giselle Ramirez

  • Jirong Yi

  • Donghee Han

  • Damini Dey

  • Dominika Grodecka

  • Kajetan Grodecki

  • Mark Lemley

  • Paul Kavanagh

  • Joanna X. Liang

  • Jianhang Zhou

  • Valerie Builoff

  • Jon Hainer

  • Sylvain Carre

  • Leanne Barrett

  • Andrew J. Einstein

  • Stacey Knight

  • Steve Mason

  • Viet T. Le

  • Wanda Acampa

  • Samuel Wopperer

  • Panithaya Chareonthaitawee

  • Daniel S. Berman

  • Marcelo F. Di Carli

  • Piotr J. Slomka

  • January 14, 2026

  • 0 min

Share

  • 1

    The study developed an AI model that integrates 10 PET MPI parameters to enhance the diagnosis of coronary artery disease (CAD).

  • 2

    The AI model demonstrated superior performance in diagnosing CAD compared to traditional clinical measurements and quantitative thresholds.

  • 3

    The model was validated on a large external cohort, confirming its robustness and generalizability across multiple centers.

  • 4

    Patients with CAD exhibited significantly higher ischemic total perfusion deficit and lower myocardial blood flow compared to those without CAD.

  • 5

    Coronary artery calcium scores were assessed, revealing a substantial proportion of patients with high calcium burden in both training and testing cohorts.

Original Source(s)

Related Content