University of Freiburg

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.

The work analyzes temporal abstraction as a mechanism to mitigate the spectral mismatch in forward-backward (FB) representations, showing by characterizing the spectral properties of the transition operator that temporal abstraction acts analogously to a low-pass filter suppressing high-frequency spectral components, thereby reducing the effective rank of the induced successor representation while preserving a formal bound on the value function error, and empirically showing that this alignment is a key factor for stable FB learning at high discount factors.

The work analyzes temporal abstraction as a mechanism to mitigate the spectral mismatch in forward-backward (FB) representations, showing by characterizing the spectral properties of the transition operator that temporal abstraction acts analogously to a low-pass filter suppressing high-frequency spectral components, thereby reducing the effective rank of the induced successor representation while preserving a formal bound on the value function error, and empirically showing that this alignment is a key factor for stable FB learning at high discount factors.

The study developed and multireader-evaluated AI-RADS, a structured framework for case-level assessment of radiology AI output reliability, clinical utility, and recommended actions, in which 5 board-certified radiologists independently evaluated 350 cases processed by 7 representative AI applications, assigning each case one of 5 AI-RADS categories, applicable modifiers, and an independent correctness rating as a reference; substantial interreader agreement was observed for core categories in image-based tasks (Krippendorff's α=0.87; 95% CI: 0.83-0.91) and generative AI tasks (α=0.93; 95% CI: 0.91-0.

The study developed and multireader-evaluated AI-RADS, a structured framework for case-level assessment of radiology AI output reliability, clinical utility, and recommended actions, in which 5 board-certified radiologists independently evaluated 350 cases processed by 7 representative AI applications, assigning each case one of 5 AI-RADS categories, applicable modifiers, and an independent correctness rating as a reference; substantial interreader agreement was observed for core categories in image-based tasks (Krippendorff's α=0.87; 95% CI: 0.83-0.91) and generative AI tasks (α=0.93; 95% CI: 0.91-0.