Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2018 Dec 5;2(1):42.
doi: 10.1186/s41747-018-0071-4.

Big data, artificial intelligence, and structured reporting

Daniel Pinto Dos Santos  1 , Bettina Baeßler  2
Affiliations
Review

Big data, artificial intelligence, and structured reporting

Daniel Pinto Dos Santos et al. Eur Radiol Exp. .

Abstract

The past few years have seen a considerable rise in interest towards artificial intelligence and machine learning applications in radiology. However, in order for such systems to perform adequately, large amounts of training data are required. These data should ideally be standardised and of adequate quality to allow for further usage in training of artificial intelligence algorithms. Unfortunately, in many current clinical and radiological information technology ecosystems, access to relevant pieces of information is difficult. This is mostly because a significant portion of information is handled as a collection of narrative texts and interoperability is still lacking. This review aims at giving a brief overview on how structured reporting can help to facilitate research in artificial intelligence and the context of big data.

Keywords: Artificial intelligence; Information technology; Machine learning; Radiology.

PubMed Disclaimer

Conflict of interest statement

Ethics approval and consent to participate

Not applicable.

Consent for publication

Dr. Luke Oakden-Rayner agreed to publication of his material (Fig. 1).

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Images from the ChestXray14 dataset labelled as showing atelectasis (red boxes indicate wrong label, orange indicate doubtful). Courtesy of Luke Oakden-Rayner (available at: https://lukeoakdenrayner.wordpress.com/2017/12/18/the-chestxray14-dataset-problems/) , with permission
Fig. 2
Fig. 2
Example of a structured report template for pulmonary embolism (left), and a dashboard visualising summary results of all reports created with this template (right). Such information could also be used as labels to the corresponding imaging study
See this image and copyright information in PMC

References

    1. Chartrand G, Cheng PM, Vorontsov E, et al. Deep Learning: a primer for radiologists. Radiographics. 2017;37:2113–2131. doi: 10.1148/rg.2017170077. - DOI - PubMed
    1. Schiappa M (2017) Man vs machine: comparing artificial and biological neural networks. Available via: https://news.sophos.com/en-us/2017/09/21/man-vs-machine-comparing-artifi.... Accessed 12 Aug 2018
    1. Gerstmair A, Daumke P, Simon K, Langer M, Kotter E. Intelligent image retrieval based on radiology reports. Eur Radiol. 2012;22:2750–2758. doi: 10.1007/s00330-012-2608-x. - DOI - PubMed
    1. Chen Po-Hao, Zafar Hanna, Galperin-Aizenberg Maya, Cook Tessa. Integrating Natural Language Processing and Machine Learning Algorithms to Categorize Oncologic Response in Radiology Reports. Journal of Digital Imaging. 2017;31(2):178–184. doi: 10.1007/s10278-017-0027-x. - DOI - PMC - PubMed
    1. Pons Ewoud, Braun Loes M. M., Hunink M. G. Myriam, Kors Jan A. Natural Language Processing in Radiology: A Systematic Review. Radiology. 2016;279(2):329–343. doi: 10.1148/radiol.16142770. - DOI - PubMed

LinkOut - more resources