AI in Radiology: Where are we today in Multiple Sclerosis Imaging?

Paul Eichinger; Claus Zimmer; Benedikt Wiestler

doi:10.1055/a-1167-8402

RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, Table of Contents

Rofo 2020; 192(09): 847-853
DOI: 10.1055/a-1167-8402

Review

AI in Radiology: Where are we today in Multiple Sclerosis Imaging?

Article in several languages: English | deutsch

Paul Eichinger

¹Department of Radiology, Klinikum rechts der Isar der Technischen Universität München, München, Germany

,

Claus Zimmer

²Department of Neuroradiology, Klinikum rechts der Isar der Technischen Universität München, München, Germany

,

Benedikt Wiestler

²Department of Neuroradiology, Klinikum rechts der Isar der Technischen Universität München, München, Germany

› Author Affiliations

Abstract

Background MR imaging is an essential component in managing patients with Multiple sclerosis (MS). This holds true for the initial diagnosis as well as for assessing the clinical course of MS. In recent years, a growing number of computer tools were developed to analyze imaging data in MS. This review gives an overview of the most important applications with special emphasis on artificial intelligence (AI).

Methods Relevant studies were identified through a literature search in recognized databases, and through parsing the references in studies found this way. Literature published as of November 2019 was included with a special focus on recent studies from 2018 and 2019.

Results There are a number of studies which focus on optimizing lesion visualization and lesion segmentation. Some of these studies accomplished these tasks with high accuracy, enabling a reproducible quantitative analysis of lesion loads. Some studies took a radiomics approach and aimed at predicting clinical endpoints such as the conversion from a clinically isolated syndrome to definite MS. Moreover, recent studies investigated synthetic imaging, i. e. imaging data that is not measured during an MR scan but generated by a computer algorithm to optimize the contrast between MS lesions and brain parenchyma.

Conclusion Computer-based image analysis and AI are hot topics in imaging MS. Some applications are ready for use in clinical routine. A major challenge for the future is to improve prediction of expected disease courses and thereby helping to find optimal treatment decisions on an individual level. With technical improvements, more questions arise about the integration of new tools into the radiological workflow.

Key Points:

Computer algorithms have a growing impact on analyzing MR imaging in MS.
Artificial intelligence is more and more commonly employed in such computer tools.
Applications include lesion segmentation, prediction of clinical parameters and image synthesizing.

Citation Format

Eichinger P, Zimmer C, Wiestler B. AI in Radiology: Where are we today in Multiple Sclerosis Imaging?. Fortschr Röntgenstr 2020; 192: 847 – 853

Key words

multiple sclerosis - artificial intelligence - MR-imaging - radiomics - segmentation

Full Text

References

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