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European Journal of Nuclear Medicine and Molecular Imaging

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Open Access 01-12-2019 | Magnetic Resonance Imaging | Review Article

Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics

Authors: Martina Sollini, Lidija Antunovic, Arturo Chiti, Margarita Kirienko

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 13/2019

Abstract

Purpose

The aim of this systematic review was to analyse literature on artificial intelligence (AI) and radiomics, including all medical imaging modalities, for oncological and non-oncological applications, in order to assess how far the image mining research stands from routine medical application. To do this, we applied a trial phases classification inspired from the drug development process.

Methods

Among the articles we considered for inclusion from PubMed were multimodality AI and radiomics investigations, with a validation analysis aimed at relevant clinical objectives. Quality assessment of selected papers was performed according to the QUADAS-2 criteria. We developed the phases classification criteria for image mining studies.

Results

Overall 34,626 articles were retrieved, 300 were selected applying the inclusion/exclusion criteria, and 171 high-quality papers (QUADAS-2 ≥ 7) were identified and analysed. In 27/171 (16%), 141/171 (82%), and 3/171 (2%) studies the development of an AI-based algorithm, radiomics model, and a combined radiomics/AI approach, respectively, was described. A total of 26/27(96%) and 1/27 (4%) AI studies were classified as phase II and III, respectively. Consequently, 13/141 (9%), 10/141 (7%), 111/141 (79%), and 7/141 (5%) radiomics studies were classified as phase 0, I, II, and III, respectively. All three radiomics/AI studies were categorised as phase II trials.

Conclusions

The results of the studies are promising but still not mature enough for image mining tools to be implemented in the clinical setting and be widely used. The transfer learning from the well-known drug development process, with some specific adaptations to the image mining discipline could represent the most effective way for radiomics and AI algorithms to become the standard of care tools.

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Title: Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics
Authors: Martina Sollini
Lidija Antunovic
Arturo Chiti
Margarita Kirienko
Publication date: 01-12-2019
Publisher: Springer Berlin Heidelberg
Keywords: Magnetic Resonance Imaging
Magnetic Resonance Imaging
Computed Tomography
Computed Tomography
Artificial Intelligence
Positron Emission Tomography
Published in: European Journal of Nuclear Medicine and Molecular Imaging / Issue 13/2019
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI: https://doi.org/10.1007/s00259-019-04372-x

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics

Abstract

Purpose

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusions

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