J Korean Soc Radiol.  2019 Mar;80(2):176-201. 10.3348/jksr.2019.80.2.176.

Imaging Informatics: A New Horizon for Radiology in the Era of Artificial Intelligence, Big Data, and Data Science

• Kim JH

• KMID: 2442512
• DOI: http://doi.org/10.3348/jksr.2019.80.2.176

Abstract

We are witnessing the big wave of Industrial Revolution 4.0, enabled by artificial intelligence (AI) and big data, which has shaken the entire industry and day-to-day life as well as rapidly changed the landscapes of related academic disciplines. After the introduction of genome sequencing and analysis technology, biology and medical sciences have been rapidly transforming into data science. Radiology is facing a challenging period of transformation into a data science. This review article draws attention to imaging informatics as a vehicle to open a new horizon and to drive to the future path for radiology in the AI and big data era. We introduce the basic concepts of imaging informatics and consider the informatics features of picture archiving and communication system and digital imaging and communications in medicine. We discuss the concepts and differences of radiogenomics and radiomics, which are important specialties of imaging informatics. We introduce the basics of AI and its recent applications in radiology as well as requirements for the successful construction of big data for imaging informatics. We conclude by discussing unresolved issues, potential solutions, and directions for future developments.

MeSH Terms

Artificial Intelligence*
Biology
Computational Biology
Genome
Genomics
Informatics*
Medical Informatics
Radiology Information Systems

Figure

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  Fig. 1. The figure illustrates a typical analytics procedure in radiogenomics research.

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  Fig. 2. The figure shows an example of analysis procedure in radiomics research. Adapted from Lao et al. Sci Rep 2017;7:10353 (26).

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  Fig. 3. The figure shows examples of segmentation methods. (A) manual, (B) point click, (C) box draw, and (D) sketch draw. Adapted from Lee et al. J Kor Soc Imag Infor Med 2014;20:19–26 (8).

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  Fig. 4. The diagram shows an example of normalized dynamic range for features extracted from segmented tumor volumes. Adapted from Lee et al. Korean J Radiol 2017;18:498–509 (19).

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  Fig. 5. The figure shows an example of feature clustergram derived from feature cross correlation matrix. Adapt-ed from Lee et al. Korean J Radiol 2017; 18:498–509 (19).

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  Fig. 6. The figure illustrates basic building blocks and architectures in deep learning (A–F). Adapted from Alom et al. arXiv preprint 2018 arXiv:1803.01164 (41).

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  Fig. 7. The figure shows examples of deep learning applications. A. Segmentation of multiple abdominal organs in abdominal CT. Adapted from Wang et al. arXiv preprint 2018;arXiv:1804.02595 (54). B. Prognosis map of pulmonary nodules in chest CT. Adapted from Hosny et al. PLoS Med 2018;15:e1002711 (55).

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  Fig. 8. The figure shows an example list of curation document for the National Lung Screening Trial dataset. Patient, exam, lesion levels, curation dictionary, and data are provided.

Reference

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