Artificial Intelligence Approaches on X-ray-oriented Images Process for Early Detection of COVID-19.

IF 1.1 Q4 ENGINEERING, BIOMEDICAL

Journal of Medical Signals & Sensors Pub Date : 2022-07-26 eCollection Date: 2022-07-01 DOI:10.4103/jmss.jmss_111_21

Sorayya Rezayi, Marjan Ghazisaeedi, Sharareh Rostam Niakan Kalhori, Soheila Saeedi

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引用次数: 2

Abstract

Background: COVID-19 is a global public health problem that is crucially important to be diagnosed in the early stages. This study aimed to investigate the use of artificial intelligence (AI) to process X-ray-oriented images to diagnose COVID-19 disease.

Methods: A systematic search was conducted in Medline (through PubMed), Scopus, ISI Web of Science, Cochrane Library, and IEEE Xplore Digital Library to identify relevant studies published until 21 September 2020.

Results: We identified 208 papers after duplicate removal and filtered them into 60 citations based on inclusion and exclusion criteria. Direct results sufficiently indicated a noticeable increase in the number of published papers in July-2020. The most widely used datasets were, respectively, GitHub repository, hospital-oriented datasets, and Kaggle repository. The Keras library, Tensorflow, and Python had been also widely employed in articles. X-ray images were applied more in the selected articles. The most considerable value of accuracy, sensitivity, specificity, and Area under the ROC Curve was reported for ResNet18 in reviewed techniques; all the mentioned indicators for this mentioned network were equal to one (100%).

Conclusion: This review revealed that the application of AI can accelerate the process of diagnosing COVID-19, and these methods are effective for the identification of COVID-19 cases exploiting Chest X-ray images.

Abstract Image

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基于人工智能的新型冠状病毒早期检测x线图像处理方法

背景:COVID-19是一个全球性公共卫生问题，早期诊断至关重要。本研究旨在探讨利用人工智能(AI)处理x射线定向图像来诊断COVID-19疾病。方法:系统检索Medline(通过PubMed)、Scopus、ISI Web of Science、Cochrane Library和IEEE Xplore数字图书馆，确定截至2020年9月21日发表的相关研究。结果:在重复删除后，我们确定了208篇论文，并根据纳入和排除标准将其筛选为60篇引用。直接结果充分表明，2020年7月发表的论文数量显著增加。使用最广泛的数据集分别是GitHub存储库、面向医院的数据集和Kaggle存储库。Keras库、Tensorflow和Python也在文章中被广泛使用。x线影像在入选文章中应用较多。在回顾的技术中，ResNet18的准确性、灵敏度、特异性和ROC曲线下面积的价值最为可观;该网络的上述指标均等于1(100%)。结论:人工智能的应用可以加快新冠肺炎的诊断过程，这些方法对于利用胸部x线图像识别新冠肺炎病例是有效的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Medical Signals & Sensors ENGINEERING, BIOMEDICAL-

CiteScore

2.30

自引率

0.00%

发文量

审稿时长

33 weeks

期刊介绍： JMSS is an interdisciplinary journal that incorporates all aspects of the biomedical engineering including bioelectrics, bioinformatics, medical physics, health technology assessment, etc. Subject areas covered by the journal include: - Bioelectric: Bioinstruments Biosensors Modeling Biomedical signal processing Medical image analysis and processing Medical imaging devices Control of biological systems Neuromuscular systems Cognitive sciences Telemedicine Robotic Medical ultrasonography Bioelectromagnetics Electrophysiology Cell tracking - Bioinformatics and medical informatics: Analysis of biological data Data mining Stochastic modeling Computational genomics Artificial intelligence & fuzzy Applications Medical softwares Bioalgorithms Electronic health - Biophysics and medical physics: Computed tomography Radiation therapy Laser therapy - Education in biomedical engineering - Health technology assessment - Standard in biomedical engineering.