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In this study, the authors aimed to investigate the effects of plaque-related factors, if any, on the diagnostic performance of an artificial intelligence coronary-assisted diagnosis system (AI-CADS). This was undertaken by analyzing 1,224 vessels in 306 patients. The authors were able to determine that AI-CADS has the ability to distinguish ≥50% coronary stenosis, but found that an additional manual interpretation based on AI-CADS is necessary.

Key points

  • AI-CADS can help radiologists quickly assess CCTA and improve diagnostic confidence.
  • Additional manual interpretation on the basis of AI-CADS is necessary.
  • The plaque length and CACs will affect the diagnostic performance of AI-CADS.

Article: Do plaque-related factors affect the diagnostic performance of an artificial intelligence coronary-assisted diagnosis system? Comparison with invasive coronary angiography

Authors: Jie Xu, Linli Chen, Xiaojia Wu, Chuanming Li, Guangyong Ai, Yuexi Liu, Bitong Tian, Dajing Guo & Zheng Fang

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This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.