TY - JOUR
T1 - Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19)
T2 - A detailed review with direction for future research
AU - Soomro, Toufique A.
AU - Zheng, Lihong
AU - Afifi, Ahmed J.
AU - Ali, Ahmed
AU - Yin, Ming
AU - Gao, Junbin
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Nature B.V.
Includes bibliographical references
PY - 2022/2
Y1 - 2022/2
N2 - Since early 2020, the whole world has been facing the deadly and highly contagious disease named coronavirus disease (COVID-19) and the World Health Organization declared the pandemic on 11 March 2020. Over 23 million positive cases of COVID-19 have been reported till late August 2020. Medical images such as chest X-rays and Computed Tomography scans are becoming one of the main leading clinical diagnosis tools in fighting against COVID-19, underpinned by Artificial Intelligence based techniques, resulting in rapid decision-making in saving lives. This article provides an extensive review of AI-based methods to assist medical practitioners with comprehensive knowledge of the efficient AI-based methods for efficient COVID-19 diagnosis. Nearly all the reported methods so far along with their pros and cons as well as recommendations for improvements are discussed, including image acquisition, segmentation, classification, and follow-up diagnosis phases developed between 2019 and 2020. AI and machine learning technologies have boosted the accuracy of Covid-19 diagnosis, and most of the widely used deep learning methods have been implemented and worked well with a small amount of data for COVID-19 diagnosis. This review presents a detailed mythological analysis for the evaluation of AI-based methods used in the process of detecting COVID-19 from medical images. However, due to the quick outbreak of Covid-19, there are not many ground-truth datasets available for the communities. It is necessary to combine clinical experts’ observations and information from images to have a reliable and efficient COVID-19 diagnosis. This paper suggests that future research may focus on multi-modality based models as well as how to select the best model architecture where AI can introduce more intelligence to medical systems to capture the characteristics of diseases by learning from multi-modality data to obtain reliable results for COVID-19 diagnosis for timely treatment.
AB - Since early 2020, the whole world has been facing the deadly and highly contagious disease named coronavirus disease (COVID-19) and the World Health Organization declared the pandemic on 11 March 2020. Over 23 million positive cases of COVID-19 have been reported till late August 2020. Medical images such as chest X-rays and Computed Tomography scans are becoming one of the main leading clinical diagnosis tools in fighting against COVID-19, underpinned by Artificial Intelligence based techniques, resulting in rapid decision-making in saving lives. This article provides an extensive review of AI-based methods to assist medical practitioners with comprehensive knowledge of the efficient AI-based methods for efficient COVID-19 diagnosis. Nearly all the reported methods so far along with their pros and cons as well as recommendations for improvements are discussed, including image acquisition, segmentation, classification, and follow-up diagnosis phases developed between 2019 and 2020. AI and machine learning technologies have boosted the accuracy of Covid-19 diagnosis, and most of the widely used deep learning methods have been implemented and worked well with a small amount of data for COVID-19 diagnosis. This review presents a detailed mythological analysis for the evaluation of AI-based methods used in the process of detecting COVID-19 from medical images. However, due to the quick outbreak of Covid-19, there are not many ground-truth datasets available for the communities. It is necessary to combine clinical experts’ observations and information from images to have a reliable and efficient COVID-19 diagnosis. This paper suggests that future research may focus on multi-modality based models as well as how to select the best model architecture where AI can introduce more intelligence to medical systems to capture the characteristics of diseases by learning from multi-modality data to obtain reliable results for COVID-19 diagnosis for timely treatment.
KW - Artificial intelligence(AI)
KW - Classification
KW - Coronavirus (COVID-19)
KW - Deep learning
KW - Medical imaging
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85104728081&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85104728081&partnerID=8YFLogxK
U2 - 10.1007/s10462-021-09985-z
DO - 10.1007/s10462-021-09985-z
M3 - Article
C2 - 33875900
AN - SCOPUS:85104728081
SN - 0269-2821
VL - 55
SP - 1409
EP - 1439
JO - Artificial Intelligence Review
JF - Artificial Intelligence Review
IS - 2
ER -