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Left Atrial Volume as a Biomarker of Atrial Fibrillation at Routine Chest CT: Deep Learning Approach.

Alex Bratt1, Zachary Guenther1, Lewis D Hahn1

  • 1Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305 (A.B., Z.G., L.D.H., P.L.A., A.N.C.L., H.H.G.); and Department of Radiology, University of California at Davis, Sacramento, Calif (M.K.).

Radiology. Cardiothoracic Imaging
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Summary
This summary is machine-generated.

Deep learning accurately measures left atrial volume from routine chest CT scans. This measurement predicts atrial fibrillation (AF), an irregular heart rhythm, showing DL

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Cardiology

Background:

  • Atrial fibrillation (AF) is a common arrhythmia.
  • Predicting AF risk is crucial for patient management.
  • Routine chest CT scans offer potential for incidental AF risk assessment.

Purpose of the Study:

  • To evaluate a deep learning (DL) model's performance in predicting AF using routine, nongated chest CT.
  • To assess the utility of automated left atrial volume measurement for AF prediction.

Main Methods:

  • A DL model was trained on 500 chest CT scans to measure left atrial volume.
  • The model's performance was validated on a separate cohort of 500 patients.
  • Receiver operating characteristic (ROC) analysis assessed automated atrial size as an AF predictor.

Main Results:

  • The DL model demonstrated high agreement with manual segmentation (mean Dice = 0.87).
  • Automated left atrial volume was a significant predictor of AF in the validation cohort (AUC = 0.768).
  • Left atrial volume independently predicted AF, with an age-adjusted relative risk of 2.9.

Conclusions:

  • Left atrial volume measured via routine chest CT is an independent predictor of AF.
  • Deep learning provides a suitable method for automated atrial volume measurement.
  • This approach may enhance AF risk stratification using existing imaging data.