Use of Conventional Chest Imaging and Artificial Intelligence in COVID-19 Infection. A Review of the Literature

Abelaira, María Dolores Corbacho; Abelaira, Fernando Corbacho; Ruano-Raviña, Alberto; Fernández-Villar, Alberto

doi:10.1016/j.opresp.2020.100078

Cited by 10 publications

(7 citation statements)

References 45 publications

(38 reference statements)

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“…The AI model learns by itself to discriminate COVID CT scans from non-COVID CT scans after reviewing a series of pictures. Several studies [ 24 , 25 ] demonstrate significant success in the use of AI and deep learning (DL) algorithms for effective illness identification from chest CT images.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised clustering of SARS-CoV-2 using deep convolutional autoencoder

Sherif

Ahmed

2022

J. Eng. Appl. Sci.

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SARS-CoV-2’s population structure might have a substantial impact on public health management and diagnostics if it can be identified. It is critical to rapidly monitor and characterize their lineages circulating globally for a more accurate diagnosis, improved care, and faster treatment. For a clearer picture of the SARS-CoV-2 population structure, clustering the sequencing data is essential. Here, deep clustering techniques were used to automatically group 29,017 different strains of SARS-CoV-2 into clusters. We aim to identify the main clusters of SARS-CoV-2 population structure based on convolutional autoencoder (CAE) trained with numerical feature vectors mapped from coronavirus Spike peptide sequences. Our clustering findings revealed that there are six large SARS-CoV-2 population clusters (C1, C2, C3, C4, C5, C6). These clusters contained 43 unique lineages in which the 29,017 publicly accessible strains were dispersed. In all the resulting six clusters, the genetic distances within the same cluster (intra-cluster distances) are less than the distances between inter-clusters (P-value 0.0019, Wilcoxon rank-sum test). This indicates substantial evidence of a connection between the cluster’s lineages. Furthermore, comparisons of the K-means and hierarchical clustering methods have been examined against the proposed deep learning clustering method. The intra-cluster genetic distances of the proposed method were smaller than those of K-means alone and hierarchical clustering methods. We used T-distributed stochastic-neighbor embedding (t-SNE) to show the outcomes of the deep learning clustering. The strains were isolated correctly between clusters in the t-SNE plot. Our results showed that the (C5) cluster exclusively includes Gamma lineage (P.1) only, suggesting that strains of P.1 in C5 are more diversified than those in the other clusters. Our study indicates that the genetic similarity between strains in the same cluster enables a better understanding of the major features of the unknown population lineages when compared to some of the more prevalent viral isolates. This information helps researchers figure out how the virus changed over time and spread to people all over the world.

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Section: Introductionmentioning

confidence: 99%

Unsupervised clustering of SARS-CoV-2 using deep convolutional autoencoder

Sherif

Ahmed

2022

J. Eng. Appl. Sci.

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“… 2,8 During the pandemic, an approximation of 20% of chest radiographs were reported by general radiologists as normal while they were found to be abnormal after a second peer reading. 11 Nevertheless, only 65% of positive finding on chest radiographs were picked up by expert radiologists as Covid-19 pneumonic changes usually appear as subtle findings in the majority of patients. 12 This can be explained by the limited number of experienced thoracic radiologists and the sudden increase in work overload during the pandemic.…”

Section: Introductionmentioning

confidence: 99%

“… 12 This can be explained by the limited number of experienced thoracic radiologists and the sudden increase in work overload during the pandemic. 11 …”

Section: Introductionmentioning

confidence: 99%

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The role of artificial intelligence in plain chest radiographs interpretation during the Covid-19 pandemic

AlNuaimi

AlKetbi

2022

BJR|Open

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Artificial intelligence (AI) plays a crucial role in the future development of all healthcare sectors ranging from clinical assistance of physicians by providing accurate diagnosis, prognosis and treatment to the development of vaccinations and aiding in the combat against the Covid-19 global pandemic. AI has an important role in diagnostic radiology where the algorithms can be trained by large datasets to accurately provide a timely diagnosis of the radiological images given. This has led to the development of several AI algorithms that can be used in regions of scarcity of radiologists during the current pandemic by simply denoting the presence or absence of Covid-19 pneumonia in PCR positive patients on plain chest radiographs as well as in helping to levitate the over burdened radiology departments by accelerating the time for report delivery. Plain chest radiography is the most common radiological study in the emergency department setting and is readily available, fast and a cheap method that can be used in triaging patients as well as being portable in the medical wards and can be used as the initial radiological examination in Covid-19 positive patients to detect pneumonic changes. Numerous studies have been done comparing several AI algorithms to that of experienced thoracic radiologists in plain chest radiograph reports measuring accuracy of each in Covid-19 patients. The majority of studies have reported performance equal or higher to that of the well-experienced thoracic radiologist in predicting the presence or absence of Covid-19 pneumonic changes in the provided chest radiographs.

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“…Due to the recent rapid development of deep learning (LeCun et al 2015 ; Goodfellow et al 2016 ), the Artificial Intelligence (Bullock et al 2020 ; Mei et al 2020 ) and Robotics (Yang et al 2020 ) become widely used in COVID-19 related research (Arora et al 2020 ; Rasheed et al 2020 ; Tseng et al 2020 ). Artificial Intelligence methods have been applied to various topics related to the ongoing pandemic, such as virus genome analysis (Saqib Nawaz et al 2021 ), detecting pneumonia in COVID-19 patients (Harmon et al 2020 ; Farhat et al 2020 ; Corbacho Abelaira et al 2021 ), predicting the numbers of infected people (Ahmad et al 2020 ; Rahimi et al 2021 ), classification of medical images of COVID-19 patients (Albahri et al 2020 ), or sorting out which information on the pandemic is reliable (Rashmid and Wang 2020 ). Various detailed reviews on deep learning techniques that are currently being applied for COVID-19 diagnostics may be found in Ozsahin et al ( 2020 ), Roberts et al ( 2020 ), Chiroma et al ( 2020 ), Syeda et al ( 2020 ), or Islam et al ( 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Differential evolution and particle swarm optimization against COVID-19

Piotrowski

Piotrowska

2021

Artif Intell Rev

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COVID-19 disease, which highly affected global life in 2020, led to a rapid scientific response. Versatile optimization methods found their application in scientific studies related to COVID-19 pandemic. Differential Evolution (DE) and Particle Swarm Optimization (PSO) are two metaheuristics that for over two decades have been widely researched and used in various fields of science. In this paper a survey of DE and PSO applications for problems related with COVID-19 pandemic that were rapidly published in 2020 is presented from two different points of view: 1. practitioners seeking the appropriate method to solve particular problem, 2. experts in metaheuristics that are interested in methodological details, inter comparisons between different methods, and the ways for improvement. The effectiveness and popularity of DE and PSO is analyzed in the context of other metaheuristics used against COVID-19. It is found that in COVID-19 related studies: 1. DE and PSO are most frequently used for calibration of epidemiological models and image-based classification of patients or symptoms, but applications are versatile, even interconnecting the pandemic and humanities; 2. reporting on DE or PSO methodological details is often scarce, and the choices made are not necessarily appropriate for the particular algorithm or problem; 3. mainly the basic variants of DE and PSO that were proposed in the late XX century are applied, and research performed in recent two decades is rather ignored; 4. the number of citations and the availability of codes in various programming languages seems to be the main factors for choosing metaheuristics that are finally used.

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Use of Conventional Chest Imaging and Artificial Intelligence in COVID-19 Infection. A Review of the Literature

Cited by 10 publications

References 45 publications

Unsupervised clustering of SARS-CoV-2 using deep convolutional autoencoder

Unsupervised clustering of SARS-CoV-2 using deep convolutional autoencoder

The role of artificial intelligence in plain chest radiographs interpretation during the Covid-19 pandemic

Differential evolution and particle swarm optimization against COVID-19

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