# Fat Attenuation Index Score

Coronary inflammation could previously be measured only on contrast-enhanced coronary CT angiography, while routine calcium scoring uses low-dose noncontrast CT. A deep-learning pipeline trained and externally validated in a multicenter cohort computes a pericoronary fat attenuation index (FAI) score from noncontrast CT; the score performed comparably to the contrast-based FAI and further separated cardiovascular death and major adverse cardiovascular events among people with a calcium score of zero.

Coronary inflammation could previously be measured only on contrast-enhanced coronary CT angiography, while routine calcium scoring uses low-dose noncontrast CT. A deep-learning pipeline trained and externally validated in a multicenter cohort computes a pericoronary fat attenuation index (FAI) score from noncontrast CT; the score performed comparably to the contrast-based FAI and further separated cardiovascular death and major adverse cardiovascular events among people with a calcium score of zero.

This review by a University of Oxford group synthesises the anatomical and physiological basis of pericoronary adipose tissue (PCAT) as a biosensor of coronary inflammation, explains how the standardised Fat Attenuation Index (FAI) Score corrects for technical, anatomical and biological variability and predicts MACE, reviews the role of artificial intelligence in automated segmentation and multi-parametric risk modelling, and compiles evidence that FAI falls after statins, anti-oxLDL antibodies, anti-TNF biologics, radiotherapy and cardiometabolic agents, concluding that PCAT imaging may complement traditional risk factors and plaque metrics while the evidence remains evolving.

This review by a University of Oxford group synthesises the anatomical and physiological basis of pericoronary adipose tissue (PCAT) as a biosensor of coronary inflammation, explains how the standardised Fat Attenuation Index (FAI) Score corrects for technical, anatomical and biological variability and predicts MACE, reviews the role of artificial intelligence in automated segmentation and multi-parametric risk modelling, and compiles evidence that FAI falls after statins, anti-oxLDL antibodies, anti-TNF biologics, radiotherapy and cardiometabolic agents, concluding that PCAT imaging may complement traditional risk factors and plaque metrics while the evidence remains evolving.