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Expert Rev Precis Med Drug Dev. 2019;4(2):59-72. doi: 10.1080/23808993.2019.1585805. Epub 2019 Apr 19.

Deep learning and radiomics in precision medicine.

Author information

1
The Russell H. Morgan Department of Radiology and Radiological Sciences, John Hopkins University, School of Medicine, Baltimore, MD, USA.
2
Department of Computer Science, The Johns Hopkins University, Baltimore, MD, USA.
3
Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Abstract

Introduction:

The radiological reading room is undergoing a paradigm shift to a symbiosis of computer science and radiology using artificial intelligence integrated with machine and deep learning with radiomics to better define tissue characteristics. The goal is to use integrated deep learning and radiomics with radiological parameters to produce a personalized diagnosis for a patient.

Areas covered:

This review provides an overview of historical and current deep learning and radiomics methods in the context of precision medicine in radiology. A literature search for 'Deep Learning', 'Radiomics', 'Machine learning', 'Artificial Intelligence', 'Convolutional Neural Network', 'Generative Adversarial Network', 'Autoencoders', Deep Belief Networks", Reinforcement Learning", and 'Multiparametric MRI' was performed in PubMed, ArXiv, Scopus, CVPR, SPIE, IEEE Xplore, and NIPS to identify articles of interest.

Expert opinion:

In conclusion, both deep learning and radiomics are two rapidly advancing technologies that will unite in the future to produce a single unified framework for clinical decision support with a potential to completely revolutionize the field of precision medicine.

KEYWORDS:

Deep learning networks; machine learning; multiparametric radiomics

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