Citation: Mulrenan, C.; Rhode, K.;
Fischer, B.M. A Literature Review on
the Use of Artificial Intelligence for
the Diagnosis of COVID-19 on CT
and Chest X-ray. Diagnostics 2022, 12,
869. https://doi.org/10.3390/
diagnostics12040869
Academic Editor: Henk
A. Marquering
Received: 17 February 2022
Accepted: 29 March 2022
Published: 31 March 2022
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Review
A Literature Review on the Use of Artificial Intelligence for the
Diagnosis of COVID-19 on CT and Chest X-ray
Ciara Mulrenan
1
, Kawal Rhode
1
and Barbara Malene Fischer
1,2,3,
*
1
School of Biomedical Engineering & Imaging Sciences, Kings College London, London WC2R 2LS, UK;
ciara.mulrenan@kcl.ac.uk (C.M.); kawal.rhode@kcl.ac.uk (K.R.)
2
Rigshospitalet, Department of Clinical Physiology and Nuclear Medicine, Blegdamsvej 9,
2100 Copenhagen, Denmark
3
Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark
* Correspondence: malene.fischer@kcl.ac.uk
Abstract:
A COVID-19 diagnosis is primarily determined by RT-PCR or rapid lateral-flow testing,
although chest imaging has been shown to detect manifestations of the virus. This article reviews the
role of imaging (CT and X-ray), in the diagnosis of COVID-19, focusing on the published studies that
have applied artificial intelligence with the purpose of detecting COVID-19 or reaching a differential
diagnosis between various respiratory infections. In this study, ArXiv, MedRxiv, PubMed, and Google
Scholar were searched for studies using the criteria terms ‘deep learning’, ‘artificial intelligence’,
‘medical imaging’, ‘COVID-19’ and ‘SARS-CoV-2’. The identified studies were assessed using a
modified version of the Transparent Reporting of a Multivariable Prediction Model for Individual
Prognosis or Diagnosis (TRIPOD). Twenty studies fulfilled the inclusion criteria for this review. Out
of those selected, 11 papers evaluated the use of artificial intelligence (AI) for chest X-ray and
12 for
CT. The size of datasets ranged from 239 to 19,250 images, with sensitivities, specificities and AUCs
ranging from 0.789–1.00, 0.843–1.00 and 0.850–1.00. While AI demonstrates excellent diagnostic
potential, broader application of this method is hindered by the lack of relevant comparators in
studies, sufficiently sized datasets, and independent testing.
Keywords: artificial intelligence; deep learning; medical imaging; SARS-CoV-2
1. Introduction
Coronaviruses are a group of RNA viruses that give rise to respiratory-tract and
intestinal infections [
1
]. Gaining high pathogenic status during the severe acute respiratory
syndrome (SARS-CoV) outbreak in 2002–2003, a new coronavirus emerged in Wuhan,
Hubei province, China in December 2019 [
2
]. The virus was named ‘COVID-19’ or ‘SARS-
CoV-2” and as a result of its rapid spread, was declared a pandemic by the World Health
Organization (WHO) in March 2020 [
3
]. As of 3 January 2022, there have been a total
of 291,721,552 cases worldwide, which is increasing at a steady rate each day [
4
]. The
most commonly used diagnostic test is the nasopharyngeal swab for reverse-transcriptase
polymerase chain reaction (RT-PCR). However, RT-PCR has lower than optimal sensitivity
rates. At day 1, RT-PCR has a false-negative rate of 100%; by day 4 it lowers to a rate of
67% and reaches 38% by the time of symptom onset [
5
]. More recently, as mass testing
has emerged, rapid lateral-flow tests have been used to detect COVID-19. The sensitivity
of these tests is dependent on the skill of the individual performing the test: laboratory
scientists perform with a sensitivity of 79%; in self-trained members of the public, this level
is 58% [6].
It is pivotal that a diagnostic test demonstrates a high sensitivity rate, particularly
for COVID-19, so that the infected individual is directed to self-isolate, thereby reducing
transmission [
7
]. At present, the RT-PCR testing method is the only approved method to
detect the COVID-19 disease [
8
]. It has been reported that medical imaging can be used to
Diagnostics 2022, 12, 869. https://doi.org/10.3390/diagnostics12040869 https://www.mdpi.com/journal/diagnostics
