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Deep learning algorithm for detection of aortic dissection on non-contrast-enhanced CT.

Akinori Hata1, Masahiro Yanagawa2, Kazuki Yamagata3

  • 1Department of Future Diagnostic Radiology, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan. a-hata@radiol.med.osaka-u.ac.jp.

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Summary

A new deep learning algorithm effectively detects aortic dissection (AD) using CT scans. Its diagnostic performance is comparable to that of experienced radiologists, offering potential to improve patient care.

Keywords:
Aortic dissectionArtificial intelligenceX-ray computed tomography

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Cardiovascular Imaging

Background:

  • Aortic dissection (AD) is a life-threatening condition requiring timely diagnosis.
  • Current diagnostic methods rely on radiologist interpretation of imaging studies, which can be subject to variability.

Purpose of the Study:

  • To develop and evaluate a deep learning algorithm for detecting aortic dissection (AD).
  • To compare the diagnostic performance of the algorithm against that of radiologists.

Main Methods:

  • A convolutional neural network (CNN) with Xception architecture was employed to develop the AD detection algorithm.
  • The study included 170 patients (85 with AD, 85 without AD), with 80% of data used for training/validation and 20% for testing.
  • Fivefold cross-validation and receiver operating characteristic (ROC) curve analysis were performed to assess performance.

Main Results:

  • The deep learning algorithm achieved an area under the curve (AUC) of 0.940 for AD detection.
  • At a cutoff of 0.400, the algorithm demonstrated 90.0% accuracy, 91.8% sensitivity, and 88.2% specificity.
  • The algorithm's performance metrics were comparable to the median performance of five independent radiologists.

Conclusions:

  • The developed deep learning algorithm exhibits diagnostic performance comparable to radiologists in detecting aortic dissection.
  • This AI-driven approach shows promise in supporting clinical practice and potentially reducing missed AD diagnoses.