Deep learning applications in coronary anatomy imaging: a systematic review and meta-analysis

Alskaf, Ebraham; Dutta, Utkarsh; Scannell, Cian M.; Chiribiri, Amedeo

doi:10.21037/jmai-22-36

Cited by 2 publications

(1 citation statement)

References 75 publications

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“…The use of advanced AI algorithms has shown significant improvements in assessing calcified plaques, with the results of some studies showing reductions in the number of false-positive rates [88,89,[192][193][194]. In our recent study, we applied a fine-tuned DL model, the real-enhanced super-resolution generative adversarial network (Real-ESRGAN), to process data pertaining to 50 coronary CTA cases from patients with a total of 184 calcified plaques [88,89].…”

Section: Ai/ml/dl In Coronary Artery Diseasementioning

confidence: 99%

Cardiovascular Computed Tomography in the Diagnosis of Cardiovascular Disease: Beyond Lumen Assessment

Sun,

Silberstein,

Vaccarezza

2024

JCDD

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Cardiovascular CT is being widely used in the diagnosis of cardiovascular disease due to the rapid technological advancements in CT scanning techniques. These advancements include the development of multi-slice CT, from early generation to the latest models, which has the capability of acquiring images with high spatial and temporal resolution. The recent emergence of photon-counting CT has further enhanced CT performance in clinical applications, providing improved spatial and contrast resolution. CT-derived fractional flow reserve is superior to standard CT-based anatomical assessment for the detection of lesion-specific myocardial ischemia. CT-derived 3D-printed patient-specific models are also superior to standard CT, offering advantages in terms of educational value, surgical planning, and the simulation of cardiovascular disease treatment, as well as enhancing doctor–patient communication. Three-dimensional visualization tools including virtual reality, augmented reality, and mixed reality are further advancing the clinical value of cardiovascular CT in cardiovascular disease. With the widespread use of artificial intelligence, machine learning, and deep learning in cardiovascular disease, the diagnostic performance of cardiovascular CT has significantly improved, with promising results being presented in terms of both disease diagnosis and prediction. This review article provides an overview of the applications of cardiovascular CT, covering its performance from the perspective of its diagnostic value based on traditional lumen assessment to the identification of vulnerable lesions for the prediction of disease outcomes with the use of these advanced technologies. The limitations and future prospects of these technologies are also discussed.

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