arXiv Machine Learning
CARDINAL Predicts Cardiovascular Risk From Non-contrast Cardiac CT
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Aug 24
Fine-tuning an ECG Foundation Model to Predict Coronary CT Angiography Outcomes
A multicenter study developed an AI-enabled electrocardiography (AI-ECG) model that predicts vessel-specific hemodynamically significant stenosis using coronary computed tomographic angiography (CCTA) as the reference. The model demonstrated strong discrimination in internal and external cohorts, including normal ECGs, and produced low-, intermediate-, and high-risk strata that correlated with stenosis severity and major adverse cardiovascular events. Calibration, decision curve analyses, and integration with guideline-based pre-test probability showed clinical utility, while waveform and attribution analyses revealed physiologically meaningful ECG features linked to high-risk predictions.
arXiv AI
Jun 29
CPAgents: Agentic Composite Phenotype Generation for Cardiac Disease Association
arXiv:2606. 28179v1 Announce Type: cross Abstract: Identifying robust associations between cardiac imaging phenotypes and clinical diseases is fundamental to population-scale cardiovascular research and reliable risk stratification.
arXiv Computer Vision
Aug 27
Auditable CT Phenotyping Through Report-derived Radiological Observations
The study introduces Auditable CT Phenotyping (ACT), a method that uses report-derived radiological observations to predict 221 electronic-health-record phenotypes from CT scans. Trained on 38,317 patients and evaluated on 25,183 held‑out patients, ACT outperformed five vision‑language baselines and CT‑CLIP in both zero‑shot and linear probing settings. Analysis of the model’s probes revealed that a small set of observations, such as aortic and coronary calcification, dominated top predictions, but restricting the observation bank to clinician‑specified evidence improved interpretability without sacrificing accuracy.
arXiv AI
Jul 2
EchoRisk: A Multicentre Echocardiography Dataset and Benchmark for Cardio-Oncology
arXiv:2607. 01039v1 Announce Type: cross Abstract: Therapy-induced cardiotoxicity is the leading non-oncological cause of treatment interruption in breast cancer patients, yet early, automated risk stratification from routine cardiac imaging remains an unsolved problem.
arXiv AI
Jul 7
PulmoSight-XAI: An Explainable Multi-View Attention Ensemble with Gradient Boosting Meta-Learning for Multi-Label Chest X-Ray Classification
arXiv:2607. 04478v1 Announce Type: cross Abstract: Automated chest X-ray classification remains challenging due to severe class imbalance, co-occurring pathologies, and the loss of localized features in conventional architectures.
arXiv Machine Learning
Jul 2
Foundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, and Segmentation Choices
arXiv:2607. 01001v1 Announce Type: cross Abstract: Radiomics is the established approach for CT-based lung cancer phenotyping, yet comparisons with foundation models rarely isolate contributions of feature extractor, classification head, and segmentation choice, or test cross-cohort robustness.