Title:New Trends of Deep Learning in Clinical Cardiology
Volume: 16
Issue: 7
Author(s): Zichao Chen, Qi Zhou, Aziz Khan , Jordan Jill, Rixin Xiong and Xu Liu*
Affiliation:
- Medical College, Guangxi University, Nanning 530004,China
Keywords:
Clinical, cardiology, artificial intelligent, machine learning, deep learning, neural networks.
Abstract: 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.
