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Structured Reporting and Artificial Intelligence

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Structured Reporting in Radiology

Part of the book series: Imaging Informatics for Healthcare Professionals ((IIHP))

Abstract

Structured radiology reporting has proved to be not only useful but also necessary in order to achieve completeness, comparability, and quantification and to minimize ambiguity. The introduction of electronic medical record (EMR) holds the promise of advancing clinical research by allowing analysis of data contained in the radiology reports; unfortunately, this is extremely difficult in free-form text, while it is quicker and easier in structured reports. Natural language processing (NLP) techniques automatically identify and extract features, which ML or DL algorithm process, for example, to classify radiology reports. Therefore, free text reports gathered retrospectively can be converted to semantic terminology by NLP. The use of structured reporting templates is also the way in which images to be used for the creation of AI models can be properly annotated with the radiological findings. A further step in the structured reporting is the inclusion of automatically generated quantitative imaging biomarkers in the report. The goal is not to create a fully quantitative report, which would resemble the way in which blood tests are reported, but to combine the findings detected by the radiologist with the associated annotations and quantitative metrics derived with a perfect combination between quantitative data and radiologist impressions.

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Authors and Affiliations

  1. Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy

    Salvatore Claudio Fanni, Michela Gabelloni & Emanuele Neri

  2. Italian Society of Medical and Interventional Radiology, SIRM Foundation, Milan, Italy

    Salvatore Claudio Fanni, Michela Gabelloni & Emanuele Neri

  3. Quantitative Imaging Biomarkers in Medicine (QUIBIM SL), Valencia, Spain

    Angel Alberich-Bayarri

Authors
  1. Salvatore Claudio Fanni
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  2. Michela Gabelloni
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  3. Angel Alberich-Bayarri
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  4. Emanuele Neri
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Corresponding author

Correspondence to Angel Alberich-Bayarri .

Editor information

Editors and Affiliations

  1. Biobank, National Brain Mapping Laboratory, Tehran, Iran

    Mansoor Fatehi

  2. Department of Radiology, University Hospital Cologne, Cologne, Germany

    Daniel Pinto dos Santos

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© 2022 European Society of Medical Imaging Informatics (EuSoMII)

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Fanni, S.C., Gabelloni, M., Alberich-Bayarri, A., Neri, E. (2022). Structured Reporting and Artificial Intelligence. In: Fatehi, M., Pinto dos Santos, D. (eds) Structured Reporting in Radiology. Imaging Informatics for Healthcare Professionals. Springer, Cham. https://doi.org/10.1007/978-3-030-91349-6_8

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  • DOI: https://doi.org/10.1007/978-3-030-91349-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91348-9

  • Online ISBN: 978-3-030-91349-6

  • eBook Packages: MedicineMedicine (R0)

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