Deep learning-derived splenic radiomics, genomics, and coronary artery disease.
Meghana KamineniVineet RaghuBuu TruongAhmed AlaaArt SchuermansSam FriedmanChristopher ReederRomit BhattacharyaPeter LibbyPatrick T EllinorMahnaz MaddahAnthony PhilippakisWhitney HornsbyZhi YuPradeep NatarajanPublished in: medRxiv : the preprint server for health sciences (2024)
Our study, combining deep learning with genomics, presents a new framework to uncover the splenic axis of CAD. Notably, our study provides evidence for the underlying genetic connection between the spleen as a candidate causal tissue-type and CAD with insight into the mechanisms of 9p21, whose mechanism is still elusive despite its initial discovery in 2007. More broadly, our study provides a unique application of deep learning radiomics to non-invasively find associations between imaging, genetics, and clinical outcomes.
Keyphrases
- deep learning
- coronary artery disease
- heart failure
- magnetic resonance imaging
- high resolution
- artificial intelligence
- cardiovascular disease
- percutaneous coronary intervention
- computed tomography
- dna methylation
- mass spectrometry
- cardiovascular events
- coronary artery bypass grafting
- acute coronary syndrome
- genome wide
- ejection fraction