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Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Ejection fraction quantification from ungated chest CT by AI.

Jianhang Zhou1, Jacek Kwieciński1,2, Aakash Shanbhag1,3

  • 1Artificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, United States.

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Summary
This summary is machine-generated.

A new AI method estimates left ventricular ejection fraction (LVEF) from chest CT scans. This AI-derived LVEF accurately predicts heart failure and mortality risk, showing broad clinical potential.

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Cardiovascular Imaging and Diagnostics
  • Radiology and Computational Pathology

Background:

  • Left ventricular ejection fraction (LVEF) is a critical indicator of cardiac function.
  • Traditional LVEF assessment requires specialized, gated cardiac imaging.
  • There is a need for accessible methods to estimate LVEF from routine imaging.

Purpose of the Study:

  • To develop and validate a novel artificial intelligence (AI) approach for estimating LVEF from ungated chest CT scans.
  • To assess the correlation of AI-derived LVEF (AI LVEF) with established cardiac imaging modalities.
  • To evaluate the prognostic value of AI LVEF in predicting adverse cardiovascular outcomes.

Main Methods:

  • A multicenter registry of 25,852 patients was used to train and validate the AI model.
  • AI LVEF was compared against 3D gated positron emission tomography (PET) and echocardiography.
  • The prognostic capability of AI LVEF was assessed in a separate cohort of 24,054 patients with lung CT scans.

Main Results:

  • AI LVEF demonstrated strong correlation with 3D gated PET (r=0.84).
  • The AI model achieved an AUC of 0.96 for detecting reduced LVEF (<40%) with 95% NPV.
  • Reduced AI LVEF was significantly associated with increased risk of heart failure, cardiovascular death (HR 13.3), and all-cause mortality.

Conclusions:

  • AI can accurately estimate LVEF from non-contrast, ungated, low-dose chest CT scans.
  • AI LVEF effectively stratifies patients for heart failure and mortality risk.
  • This AI approach holds significant potential for widespread clinical application in cardiovascular assessment.