Systematic Reviews
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Med Imaging. Apr 28, 2022; 3(2): 42-54
Published online Apr 28, 2022. doi: 10.35711/aimi.v3.i2.42
Applications of artificial intelligence in lung ultrasound: Review of deep learning methods for COVID-19 fighting
Laura De Rosa, Serena L'Abbate, Claudia Kusmic, Francesco Faita
Laura De Rosa, Serena L'Abbate, Claudia Kusmic, Francesco Faita, Institute of Clinical Physiology, Consiglio Nazionale delle Ricerche, Pisa 56124, Italy
Serena L'Abbate, Institute of Life Sciences, Scuola Superiore Sant’Anna, Pisa 56124, Italy
Author contributions: Kusmic C and Faita F designed the research study; Faita F and De Rosa L collected and analysed the references mentioned in the review; De Rosa L wrote the initial draft; Kusmic C, Faita F and L’Abbate S revised and edited the manuscript; all authors have read and approve the final manuscript.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Claudia Kusmic, MSc, PhD, Research Scientist, Institute of Clinical Physiology, Consiglio Nazionale delle Ricerche, Via Giuseppe Moruzzi 1, Pisa 56124, Italy. kusmic@ifc.cnr.it
Received: December 19, 2021
Peer-review started: December 19, 2021
First decision: February 10, 2022
Revised: February 22, 2022
Accepted: April 27, 2022
Article in press: April 27, 2022
Published online: April 28, 2022
Abstract
BACKGROUND

The pandemic outbreak of the novel coronavirus disease (COVID-19) has highlighted the need to combine rapid, non-invasive and widely accessible techniques with the least risk of patient’s cross-infection to achieve a successful early detection and surveillance of the disease. In this regard, the lung ultrasound (LUS) technique has been proved invaluable in both the differential diagnosis and the follow-up of COVID-19 patients, and its potential may be destined to evolve. Recently, indeed, LUS has been empowered through the development of automated image processing techniques.

AIM

To provide a systematic review of the application of artificial intelligence (AI) technology in medical LUS analysis of COVID-19 patients using the preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines.

METHODS

A literature search was performed for relevant studies published from March 2020 - outbreak of the pandemic - to 30 September 2021. Seventeen articles were included in the result synthesis of this paper.

RESULTS

As part of the review, we presented the main characteristics related to AI techniques, in particular deep learning (DL), adopted in the selected articles. A survey was carried out on the type of architectures used, availability of the source code, network weights and open access datasets, use of data augmentation, use of the transfer learning strategy, type of input data and training/test datasets, and explainability.

CONCLUSION

Finally, this review highlighted the existing challenges, including the lack of large datasets of reliable COVID-19-based LUS images to test the effectiveness of DL methods and the ethical/regulatory issues associated with the adoption of automated systems in real clinical scenarios.

Keywords: Lung ultrasound, Deep learning, Neural network, COVID-19 pneumonia, Medical imaging

Core Tip: Challenging coronavirus disease 2019 (COVID-19) pandemic through the identification of effective diagnostic and prognostic tools is of outstanding importance to tackle the healthcare system burdening and improve clinical outcomes. Application of deep learning (DL) in medical lung ultrasound may offer the advantage of combining non-invasiveness and wide accessibility of ultrasound imaging techniques with higher diagnostic performance and classification accuracy. This paper overviews the current applications of DL models in medical lung ultrasound imaging in COVID-19 patients, and highlight the existing challenges associated with the effective clinical application of automated systems in the medical imaging field.