Non-invasive detection of cardiac allograft rejection among heart transplant recipients using an electrocardiogram based deep learning model.
Demilade A AdedinsewoHeather D HardwayAndrea Carolina Morales-LaraMikolaj A WieczorekPatrick W JohnsonErika J DouglassBryan J DangottRaouf E NakhlehTathagat NarulaParag C PatelRohan M GoswamiMelissa A LyleAlexander J HeckmanJuan C Leoni-MorenoD Eric SteidleyReza ArsanjaniBrian HardawayMohsin AbbasAtta BehfarItzhak Zachi AttiaFrancisco Lopez-JimenezPeter A NoseworthyPaul FriedmanRickey E CarterMohamad YamaniPublished in: European heart journal. Digital health (2023)
An AI-ECG model is effective for detection of moderate-to-severe ACR in heart transplant recipients. Our findings could improve transplant care by providing a rapid, non-invasive, and potentially remote screening option for cardiac allograft function.
Keyphrases
- loop mediated isothermal amplification
- deep learning
- left ventricular
- heart failure
- artificial intelligence
- healthcare
- palliative care
- real time pcr
- early onset
- kidney transplantation
- atrial fibrillation
- high intensity
- heart rate
- pain management
- heart rate variability
- sensitive detection
- chronic pain
- health insurance