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http://dx.doi.org/10.1073/pnas.2500334122DOI Listing

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Article Synopsis
  • - The study focuses on improving the accuracy of deep learning algorithms for measuring thoracic aortic dilatation (TAD) in chest CT scans, particularly for non-ECG gated exams, due to previous unreliable classifications, especially at the aortic root.
  • - A total of 995 patients were included, and the re-trained deep learning tool showed a significant increase in correct diameter measurements, achieving 95.5% accuracy overall, compared to the initial version.
  • - The re-trained algorithm not only improved measurements at previously problematic locations (like the aortic root) but also identified additional measurements not captured before, though it still had a small percentage of inaccuracies.
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