New Trends of Deep Learning in Clinical Cardiology

Deep Learning (DL) is a novel type of Machine Learning (ML) model. It is showing an increasing promise in medicine, study and treatment of diseases and injuries, to assist in data classification, novel disease symptoms and complicated decision making. Deep learning is one of form of machine learning typically implemented via multi-level neural networks. This work discusses the pros and cons of using DL in clinical cardiology that is also applied in medicine in general while proposing certain directions as more viable for clinical use. DL models called deep neural networks (DNNs), recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have been applied to arrhythmias, electrocardiogram, ultrasonic analysis, genomes and endomyocardial biopsy. Convincingly, the results of the trained model are satisfactory, demonstrating the power of more expressive deep learning algorithms for clinical predictive modeling. In the future, more novel deep learning methods are expected to make a difference in the field of clinical medicines.

Automated summary

Deep Learning (DL) is a novel type of Machine Learning (ML) model that shows promise in clinical cardiology and medicine in general. Various DL models have been applied in analyzing arrhythmias, electrocardiograms, ultrasonic data, genomes, and biopsies, demonstrating the power of deep learning algorithms in clinical predictive modeling. Future advancements in deep learning are expected to further impact the field of clinical medicine.