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
Processing time: 129 Days and 22.6 Hours
ARTICLE HIGHLIGHTS
Research background

The current coronavirus disease 2019 (COVID-19) pandemic crisis has highlighted the need for biomedical imaging techniques in rapid clinical diagnostic evaluation of patients. Furthermore, imaging techniques are currently important in the follow-up of subjects with COVID-19. The lung ultrasound technique has become increasingly popular and is considered a good option for real-time point-of-care testing, although it has specificity limits comparable to those of chest computed tomography.

Research motivation

The application of artificial intelligence, and of deep learning in particular, in medical pulmonary ultrasound can offer an improvement in diagnostic performance and classification accuracy to a non-invasive and low-cost technique, thus implementing its diagnostic and prognostic importance to COVID-10 pandemic.

Research objectives

This review presents the state of the art of the use of artificial intelligence and deep learning techniques applied to lung ultrasound in COVID-19 patients.

Research methods

We performed a literature search, according to preferred reporting items of systematic reviews and meta-analysis guidelines, for relevant studies published from March 2020 - to 30 September 2021 on the use of deep learning tools applied to lung ultrasound imaging in COVID-19 patients. Only English-language publications were selected.

Research results

We surveyed 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.

Research conclusions

Application of deep learning systems to lung ultrasound images for the diagnosis/prognosis of COVID-19 disease has the potential to provide significant support to the medical community. However, there are critical issues related to the availability of high-quality databases with large sample size and free access to datasets.

Research perspectives

Close collaboration between the communities of computer scientists/engineers and medical professionals could facilitate the outcome of adequate guidelines for the use of deep learning in ultrasound lung imaging.