Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging

López-Cabrera, José Daniel; Orozco-Morales, Rubén; Portal-Díaz, Jorge Armando; Lovelle-Enríquez, Orlando; Pérez-Díaz, Marlen

doi:10.1007/s12553-021-00520-2

Cited by 74 publications

(58 citation statements)

References 74 publications

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“…However, the power of generalization is low when the machine learning model was trained on sets of heterogeneous images. The absence of an adequate evaluation protocol also limited the artificial intelligence assistance in CXR and its utility in clinical settings [13]. Nonetheless, CXR is useful to monitor disease progression in unstable patients.…”

Section: Introductionmentioning

confidence: 99%

A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia

Kao

Lin

2021

Diagnostics

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Introduction: Coronavirus disease 2019 (COVID-19) led to a global pandemic. Although reverse transcription polymerase chain reaction (RT-PCR) of viral nucleic acid is the gold standard for COVID-19 diagnosis, its sensitivity was found to not be high enough in many reports. As radiomics-based diagnosis research has recently emerged, we aimed to use computerized tomography (CT)-based radiomics models to differentiate COVID-19 pneumonia from other viral pneumonia infections. Materials and methods: This study was performed according to the preferred reporting items for systematic review and meta-analysis diagnostic test accuracy studies (PRISMA-DTA) guidelines. The Pubmed, Cochrane, and Embase databases were searched. The pooled sensitivity and pooled specificity were calculated. A summary receiver operating characteristic (sROC) curve was constructed. The study quality was evaluated based on the radiomics quality score. Results: A total of 10,300 patients were involved in this meta-analysis. The radiomics quality score ranged from 13 to 16 (maximum score: 36). The pooled sensitivity was 0.885 (95% CI: 0.818-0.929), and the pooled specificity was 0.811 (95% CI: 0.667-0.902). The pooled AUC was 906. Conclusion: Our meta-analysis showed that CT-based radiomics feature models can successfully differentiate COVID-19 from other viral pneumonias.

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Section: Introductionmentioning

confidence: 99%

A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia

Kao

Lin

2021

Diagnostics

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“…The traditional methods take 15 minutes for analysis of the chest CT scan. Nowadays, machine learning or deep learning techniques are used for automated analysis of CT scans and chest X-rays ( Lopez-Cabrera et al, 2021 ). These techniques will help to speed up the analysis process ( Ng et al, 2020 ; Fang et al, 2020 ; Weinstock & RJea Echenique, 2020 ).…”

Section: Diagnosis Of Covid-19mentioning

confidence: 99%

Overview of current state of research on the application of artificial intelligence techniques for COVID-19

Kumar

Singh

Kaur

et al. 2021

PeerJ Computer Science

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Background Until now, there are still a limited number of resources available to predict and diagnose COVID-19 disease. The design of novel drug-drug interaction for COVID-19 patients is an open area of research. Also, the development of the COVID-19 rapid testing kits is still a challenging task. Methodology This review focuses on two prime challenges caused by urgent needs to effectively address the challenges of the COVID-19 pandemic, i.e., the development of COVID-19 classification tools and drug discovery models for COVID-19 infected patients with the help of artificial intelligence (AI) based techniques such as machine learning and deep learning models. Results In this paper, various AI-based techniques are studied and evaluated by the means of applying these techniques for the prediction and diagnosis of COVID-19 disease. This study provides recommendations for future research and facilitates knowledge collection and formation on the application of the AI techniques for dealing with the COVID-19 epidemic and its consequences. Conclusions The AI techniques can be an effective tool to tackle the epidemic caused by COVID-19. These may be utilized in four main fields such as prediction, diagnosis, drug design, and analyzing social implications for COVID-19 infected patients.

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“…Moreover, this technique costs less than a CT scan. However, detecting Covid-19 from these images is often quite a complex task, as it has similarities with other types of viral pneumonia [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…The financial costs of diagnostic laboratory kits are an important problem in fighting the disease especially for developing and underdeveloped countries [ 17 ]. Clinicians and field experts can use deep learning-based expert systems for early detection of Covid-19 [ 14 ]. The recent popularity of deep learning architectures that offer high performance has given the opportunity to use models based on these architectures effectively.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images

Akyol

Şen

2021

Interdiscip Sci Comput Life Sci

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Coronavirus disease, which comes up in China at the end of 2019 and showed different symptoms in people infected, affected millions of people. Computer-aided expert systems are needed due to the inadequacy of the reverse transcription-polymerase chain reaction kit, which is widely used in the diagnosis of this disease. Undoubtedly, expert systems that provide effective solutions to many problems will be very useful in the detection of Covid-19 disease, especially when unskilled personnel and financial deficiencies in underdeveloped countries are taken into consideration. In the literature, there are numerous machine learning approaches built with different classifiers in the detection of this disease. This paper proposes an approach based on deep learning which detects Covid-19 and no-finding cases using chest X-ray images. Here, the classification performance of the Bi-LSTM network on the deep features was compared with the Deep Neural Network within the frame of the fivefold cross-validation technique. Accuracy, sensitivity, specificity and precision metrics were used to evaluate the classification performance of the trained models. Bi-LSTM network presented better performance compare to DNN with 97.6% value of high accuracy despite the few numbers of Covid-19 images in the dataset. In addition, it is understood that concatenated deep features more meaningful than deep features obtained with pre-trained networks by one by, as well. Consequently, it is thought that the proposed study based on the Bi-LSTM network and concatenated deep features will be noteworthy in the design of highly sensitive automated Covid-19 monitoring systems.

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Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging

Cited by 74 publications

References 74 publications

A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia

A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia

Overview of current state of research on the application of artificial intelligence techniques for COVID-19

Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images

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