Application of artificial intelligence in COVID-19 medical area: a systematic review

Chang, Zhoulin; Zhan, Zhiqing; Zhao, Zifan; You, Zhixuan; Liu, Yang; Yan, Zhihong; Fu, Yong; Liang, Wenhua; Zhao, Lei

doi:10.21037/jtd-21-747

Cited by 27 publications

(15 citation statements)

References 78 publications

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“…Currently, the diagnosis of COVID-19 mainly relies on epidemiological history, clinical symptoms, laboratory results, chest imaging findings, nucleic acid testing, or homologous comparison of gene sequencing [ 164 ]. COVID-19 is highly contagious and has an incubation period.…”

Section: Application Analysis and Discussionmentioning

confidence: 99%

A Survey on Deep Learning in COVID-19 Diagnosis

Han

Wang

et al. 2022

J. Imaging

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According to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide. The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The rapid and accurate diagnosis of COVID-19 directly affects the spread of the virus and the degree of harm. Currently, the classification of chest X-ray or CT images based on artificial intelligence is an important method for COVID-19 diagnosis. It can assist doctors in making judgments and reduce the misdiagnosis rate. The convolutional neural network (CNN) is very popular in computer vision applications, such as applied to biological image segmentation, traffic sign recognition, face recognition, and other fields. It is one of the most widely used machine learning methods. This paper mainly introduces the latest deep learning methods and techniques for diagnosing COVID-19 using chest X-ray or CT images based on the convolutional neural network. It reviews the technology of CNN at various stages, such as rectified linear units, batch normalization, data augmentation, dropout, and so on. Several well-performing network architectures are explained in detail, such as AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. We analyzed and discussed the existing CNN automatic COVID-19 diagnosis systems from sensitivity, accuracy, precision, specificity, and F1 score. The systems use chest X-ray or CT images as datasets. Overall, CNN has essential value in COVID-19 diagnosis. All of them have good performance in the existing experiments. If expanding the datasets, adding GPU acceleration and data preprocessing techniques, and expanding the types of medical images, the performance of CNN will be further improved. This paper wishes to make contributions to future research.

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Section: Application Analysis and Discussionmentioning

confidence: 99%

A Survey on Deep Learning in COVID-19 Diagnosis

Han

Wang

et al. 2022

J. Imaging

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“…As a comprehensive frontier subject, artificial intelligence is widely used in economics, the military, medicine, and daily life. With the rapid development of AI technology in the medical field, its powerful computing and deep learning capabilities have attracted the attention of people in the medical field [ 37 ]. Digestive endoscopy, as an important field of AI image recognition applications, has also received more and more attention.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Accuracy of Wireless Capsule Endoscopy in Polyp Recognition Using Deep Learning: A Meta-Analysis

Han

Wang

et al. 2022

International Journal of Clinical Practice

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Aim. As the completed studies have small sample sizes and different algorithms, a meta-analysis was conducted to assess the accuracy of WCE in identifying polyps using deep learning. Method. Two independent reviewers searched PubMed, Embase, the Web of Science, and the Cochrane Library for potentially eligible studies published up to December 8, 2021, which were analysed on a per-image basis. STATA RevMan and Meta-DiSc were used to conduct this meta-analysis. A random effects model was used, and a subgroup and regression analysis was performed to explore sources of heterogeneity. Results. Eight studies published between 2017 and 2021 included 819 patients, and 18,414 frames were eventually included in the meta-analysis. The summary estimates for the WCE in identifying polyps by deep learning were sensitivity 0.97 (95% confidence interval (CI), 0.95–0.98); specificity 0.97 (95% CI, 0.94–0.98); positive likelihood ratio 27.19 (95% CI, 15.32–50.42); negative likelihood ratio 0.03 (95% CI 0.02–0.05); diagnostic odds ratio 873.69 (95% CI, 387.34–1970.74); and the area under the sROC curve 0.99. Conclusion. WCE uses deep learning to identify polyps with high accuracy, but multicentre prospective randomized controlled studies are needed in the future.

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“…Due to the great involvement of the scientific community, many studies have been published dealing with this issue from different perspectives. Many works on the use of machine learning methods to diagnose COVID-19 have been covered in review articles such as [11][12][13][14][15].…”

Section: Related Workmentioning

confidence: 99%

“…The reviews [13][14][15] discuss the possibility of using machine learning in various aspects related to COVID-19, such as epidemiology, spreading, diagnosis, treatment, or psychological health impact. The examples of approaches presented in these works are focused on diagnostics based on image and clinical data (e.g., blood test results).…”

Section: Related Workmentioning

confidence: 99%

Screening Support System Based on Patient Survey Data—Case Study on Classification of Initial, Locally Collected COVID-19 Data

et al. 2021

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New diseases constantly endanger the lives of populations, and, nowadays, they can spread easily and constitute a global threat. The COVID-19 pandemic has shown that the fight against a new disease may be difficult, especially at the initial stage of the epidemic, when medical knowledge is not complete and the symptoms are ambiguous. The use of machine learning tools can help to filter out those sick patients who do not need to be tested for spreading the pathogen, especially in the event of an overwhelming increase in disease transmission. This work presents a screening support system that can precisely identify patients who do not carry the disease. The decision of the system is made on the basis of patient survey data that are easy to collect. A case study on a data set of symptomatic COVID-19 patients shows that the system can be effective in the initial phase of the epidemic. The case study presents an analysis of two classifiers that were tuned to achieve an assumed acceptable threshold of negative predictive values during classification. Additionally, an explanation of the obtained classification models is presented. The explanation enables the users to understand the basis of the decision made by the model. The obtained classification models provide the basis for the DECODE service, which could serve as support in screening patients with COVID-19 disease at the initial stage of the pandemic. Moreover, the data set constituting the basis for the analyses performed is made available to the research community. This data set, consisting of more than 3000 examples, is based on questionnaires collected at a hospital in Poland.

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Application of artificial intelligence in COVID-19 medical area: a systematic review

Cited by 27 publications

References 78 publications

A Survey on Deep Learning in COVID-19 Diagnosis

A Survey on Deep Learning in COVID-19 Diagnosis

Diagnostic Accuracy of Wireless Capsule Endoscopy in Polyp Recognition Using Deep Learning: A Meta-Analysis

Screening Support System Based on Patient Survey Data—Case Study on Classification of Initial, Locally Collected COVID-19 Data

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