Artificial Intelligence Approaches on X‑ray‑oriented Images Process for Early Detection of COVID‑۱۹

  • سال انتشار: 1401
  • محل انتشار: مجله سیگنالها و سنسورهای پزشکی، دوره: 12، شماره: 3
  • کد COI اختصاصی: JR_JMSI-12-3_006
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 265
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نویسندگان

Sorayya Rezayi

Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran

Marjan Ghazisaeedi

Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran

Sharareh Rostam Niakan Kalhori

Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran

Soheila Saeedi

Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran- Clinical Research Development Unit of Farshchian Heart Center, Hamadan University of Medical Sciences, Hamadan, Iran

چکیده

Background: COVID-۱۹ 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-۱۹ 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 ۲۱ September ۲۰۲۰. Results: We identified ۲۰۸ papers after duplicate removal and filtered them into ۶۰ citations based on inclusion and exclusion criteria. Direct results sufficiently indicated a noticeable increase in the number of published papers in July-۲۰۲۰. 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 ResNet۱۸ in reviewed techniques; all the mentioned indicators for this mentioned network were equal to one (۱۰۰%). Conclusion: This review revealed that the application of AI can accelerate the process of diagnosing COVID-۱۹, and these methods are effective for the identification of COVID-۱۹ cases exploiting Chest X-ray images.

کلیدواژه ها

۲۰۱۹‑nCoV disease, artificial intelligence, computed tomography, deep learning, image processing, X‑ray images

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