AI-Assisted Precise and Rapid Diagnosis of “Heart Conditions”
Release date:2025-03-01
AI-powered medical imaging software applied to CCTA can enhance image quality and enable automated measurements. Traditional approaches, such as increasing radiation dose or employing iterative reconstruction algorithms, can reduce noise to some extent; however, when AI is used for CCTA image reconstruction, deep-learning models can effectively suppress noise and improve image quality, thereby mitigating the potential risks associated with high radiation exposure. Prior to performing a CCTA examination, a non-contrast CT scan is typically conducted to obtain the coronary artery calcium score (CACS). AI models can directly perform automated CACS calculation from CCTA data, streamlining the CCTA workflow and reducing radiation exposure. Moreover, when detecting and assessing calcified plaques in CCTA, AI assistance can significantly shorten the examination time. In terms of quantifying coronary stenosis, clinical assessment primarily relies on visual diameter measurement: the degree of stenosis is determined by the percentage reduction in the diameter at the lesion site compared with the normal diameters proximal and distal to the lesion, and the severity is graded according to the Coronary Artery Disease Reporting and Data System (CAD-RADS) classification. Notably, fractional flow reserve (FFR), an index for evaluating coronary blood flow, traditionally requires invasive angiography for measurement; in contrast, AI technology can utilize AI algorithms to perform three-dimensional reconstruction and, combined with fluid-dynamic simulations, derive the corresponding FFR value.
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