Human Heart
Research on the human heart is intensely focused on improving the accuracy and efficiency of cardiac disease diagnosis and monitoring. Current efforts leverage advanced machine learning techniques, including deep convolutional neural networks, recurrent neural networks (like LSTMs), and generative models, to analyze diverse data sources such as heart sounds, cardiac MRI, and echocardiography. These models are being refined to improve the detection of abnormalities, predict cardiac function, and even generate realistic simulations of the heart for research and training purposes. Ultimately, this research aims to provide faster, more accurate, and less invasive methods for diagnosing and managing cardiovascular diseases.
Papers
Classification of Heart Sounds Using Multi-Branch Deep Convolutional Network and LSTM-CNN
Seyed Amir Latifi, Hassan Ghassemian, Maryam Imani
Spatio-temporal neural distance fields for conditional generative modeling of the heart
Kristine Sørensen, Paula Diez, Jan Margeta, Yasmin El Youssef, Michael Pham, Jonas Jalili Pedersen, Tobias Kühl, Ole de Backer, Klaus Kofoed, Oscar Camara, Rasmus Paulsen