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Everything you need to know about Computed Tomography (CT) & CT Scanning

Cardiac: Ai and Cardiac Imaging Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Cardiac ❯ AI and Cardiac Imaging

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  • "KD is associated with mucocutaneous lymph node syndrome and predominantly affects medium and small arteries in infants and children less than 5 years of age. It is more prevalent in Asian populations and has a male dominance.”
    Radiologic Imaging in Large and Medium Vessel Vasculitis
    Weinrich JM et al.
    Radiol Clin N Am 58 (2020) 765–779
  • “The coronary arteries are often involved in KD and coronary artery aneurysms develop as a result of coronary vasculitis in about 15% to 25% of untreated patients. Coronary artery aneurysms can be classified according to their size (small, <5 mm; medium, 5–8 mm; and large, >8 mm) and shape (saccular or fusiform). Large coronary artery aneurysms are associated with a higher risk of complications such as rupture, thrombosis, and stenosis, which possibly lead to myocardial infarction and death."
    Radiologic Imaging in Large and Medium Vessel Vasculitis
    Weinrich JM et al.
    Radiol Clin N Am 58 (2020) 765–779

  • Conclusion: Compared with Coronary Artery Disease Reporting and Data System and other scores, machine learning methods better discriminated patients who subsequently experienced an adverse event from those who did not.
  • Background: Coronary CT angiography contains prognostic information but the best method to extract these data remains unknown.
    Purpose: To use machine learning to develop a model of vessel features to discriminate between patients with and without subsequent death or cardiovascular events. Performance was compared with that of conventional scores.
    Conclusion: Compared with Coronary Artery Disease Reporting and Data System and other scores, machine learning methods better discriminated patients who subsequently experienced an adverse event from those who did not.
    Scoring of Coronary Artery Disease Characteristics on Coronary CT Angiograms by Using Machine Learning
    Johnson KM et al.
    Radiology 2019; 00:1–9
  • Key Points
    * For prediction of all-cause mortality on the basis of coronary CT angiography, the area under the receiver operating characteristic curve (AUC) for a machine learning score was higher than for Coronary Artery Disease Reporting and Data System (CAD- RADS; 0.77 vs 0.72, respectively; P , .001).
    * For prediction of coronary artery deaths on the basis of coronary CT angiography, the AUC was higher for a machine learning score than for CAD-RADS (0.85 vs 0.79, respectively; P , .001).
    * When deciding whether to start statins, a machine learning score ensures 93% of patients with events will be administered the drug; if CAD-RADS is used instead, only 69% will be treated.
    Scoring of Coronary Artery Disease Characteristics on Coronary CT Angiograms by Using Machine Learning
    Johnson KM et al.
    Radiology 2019; 00:1–9
  • “In conclusion, machine learning can improve the use of vessel features to discriminate between patients who will have an event and those who will not.”
    Scoring of Coronary Artery Disease Characteristics on Coronary CT Angiograms by Using Machine Learning
    Johnson KM et al.
    Radiology 2019; 00:1–9
© 1999-2020 Elliot K. Fishman, MD, FACR. All rights reserved.