AI-based body composition provides rapid assessment of muscle, bone, and fat in cardiac PET/CT patients https://t.co/FUDL1biM5h #EHJ #ACC25

AI-driven body tissue segmentation to analyze CT scans, revealing key correlations and outcome associations. A step toward personalized risk stratification. Read more in #EHJ. https://t.co/vUdiSjdRac #ACC2025 #cardiotwitter @ESC_Journals @escardio

Automatic motion correction for myocardial blood flow estimation via 18F-flurpiridaz PET-MPI can be performed rapidly with results comparable to manual motion correction. @Piotr_JSlomka @CedarsSinai Read now🔗 https://t.co/lcfJ4za9Yx

🎉Exciting times for nuclear cardiology w! FDA approval of #Flurpiridaz! Great potential to expand clinical use of PET MPI to redefine how we diagnose and care for our patients! A great moment to be a part of the exciting specialty of nuclear cardiology! #CVnuc #ThinkPET @MyASNC

AI indicates extrapulmonary structures at risk on chest CT scans and predicts mortality with explanations. @radiology_rsna https://t.co/EuPseODqQy

AI predicts who will benefit from revascularization! Congratulations to Aakash Shanbhag @CedarsSinai for winning the Young Investigators Award at the Annual American Society of Nuclear Cardiology in Austin, Texas. #ASNC2024 #CVNuc

CT Attenuation Correction (CTAC) – Beyond just #CAC? #AI-based CTAC derivation can accurately estimate cardiac volumes and mass to improve risk assessment from attenuation maps in hybrid SPECT-CT imaging. https://t.co/HQsZzi8eTb #JACCIMG #MMI #CVnuc #YesCCT

Secondary analysis of the PRE18FFIR study showed individual coronary atherosclerotic plaque activity is associated with subsequent myocardial infarction in the same coronary artery territory, which cascades into patient-level risk https://t.co/PGUO9NDw4o #JACC #cvMI #cvPET

Great talk on how to improve fairness in AI with generative models by Ira Ktena from @GoogleDeepMind at #ISBI2024 in Athens!

AI-enabled CT-guided quantification of PYP SPECT for cardiac amyloidosis. https://t.co/s0nNwIskLC

Mortality can be predicted from cardiac chamber and myocardial volumes derived from non-contrast ungated lung CT, coronary calcium CT, and CT attenuation scans. Adds significant information over calcium. All automatically processed by AI (18 sec per case). Nature Communications

Congratulations Jianhang! Recognition well deserved! Fantastic talk on AI detection of proximal disease from non contrast CT.

Exciting breakthrough in HF care! 🏥💓 A new AI model outperforms traditional methods in predicting hospitalizations for HF exacerbation, integrating clinical data with SPECT/CT imaging. Early intervention just got smarter! #HeartHealth #AI #Innovation https://t.co/gRFwZhspjB

Validation of a fully automated deep learning-enabled solution for CCTA atherosclerotic plaque and stenosis quantification in a diverse real-world cohort @journalCCT @Heart_SCCT https://t.co/zolCM9hgrN @danilorenzatti @analyzeF @pamelapina_s @AndreaScotti21 @aschenonegiugni

Happy to announce our Health AI PhD program at @CedarsSinaiMed! Thanks to @moorejh, @proftatonetti and the faculty in our Department of Computational Biomedicine embracing Active Learning, we offer a unique project-based educational experience.

Amazing work by Dr. Guadalupe Flores @guadalupeflores and the team!

More than attenuation correction! AI-segmented cardiac anatomy from SPECT/CT attenuation scans enhances risk prediction for hybrid MPI. https://t.co/ycOEt0Dk2g @CedarsSinai @CedarsSinaiMed @JACCJournals

🌟 Attention Emerging Leaders in Nuclear Cardiology! The Journal of Nuclear Cardiology is excited to announce the call for applications for its 2024 Early Career Editorial Board! We're looking for dynamic fellows-in-training and early career professionals eager to bring their