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AI framework enhances cardiovascular risk assessment from CT scans

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel Explainable Cross-Disease Reasoning Framework designed to assess cardiovascular risk using low-dose computed tomography (LDCT) scans. This framework integrates pulmonary and cardiac health assessments by extracting pulmonary findings, incorporating medical knowledge for cross-organ mechanisms, and generating cardiovascular predictions with natural-language rationales. Tested on the National Lung Screening Trial cohort, the system achieved an AUC of 0.919 for CVD screening and 0.838 for CVD mortality prediction, surpassing existing baselines and demonstrating an auditable approach to risk assessment. AI

IMPACT This framework offers a more auditable and integrated approach to cardiovascular risk assessment from medical imaging, potentially improving diagnostic accuracy and patient outcomes.

RANK_REASON This is a research paper detailing a new AI framework for medical risk assessment. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI framework enhances cardiovascular risk assessment from CT scans

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This is a research paper detailing a new AI framework for medical risk assessment. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yifei Zhang, Jiashuo Zhang, Mojtaba Safari, Xiaofeng Yang, Liang Zhao ·

    Explainable Cross-Disease Reasoning for Cardiovascular Risk Assessment from Low-Dose Computed Tomography

    arXiv:2511.06625v5 Announce Type: replace-cross Abstract: Low-dose chest computed tomography (LDCT) captures pulmonary and cardiac structures in a single scan, enabling joint assessment of lung and cardiovascular health. Existing approaches typically model these domains independe…

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