Diagnosing COVID-19 using artificial intelligence: a comprehensive review

Autor: Varada Vivek Khanna, Krishnaraj Chadaga, Niranjana Sampathila, Srikanth Prabhu, Rajagopala Chadaga, Shashikiran Umakanth
Rok vydání: 2022
Předmět:
Zdroj: Network Modeling Analysis in Health Informatics and Bioinformatics. 11
ISSN: 2192-6670
2192-6662
Popis: In early March 2020, the World Health Organization (WHO) proclaimed the novel COVID-19 as a global pandemic. The coronavirus went on to be a life-threatening infection and is still wreaking havoc all around the globe. Though vaccines have been rolled out, a section of the population (the elderly and people with comorbidities) still succumb to this deadly illness. Hence, it is imperative to diagnose this infection early to prevent a potential severe prognosis. This contagious disease is usually diagnosed using a conventional technique called the Reverse Transcription Polymerase Chain Reaction (RT-PCR). However, this procedure leads to a number of wrong and false-negative results. Moreover, it might also not diagnose the newer variants of this mutating virus. Artificial Intelligence has been one of the most widely discussed topics in recent years. It is widely used to tackle various issues across multiple domains in the modern world. In this extensive review, the applications of Artificial Intelligence in the detection of coronavirus using modalities such as CT-Scans, X-rays, Cough sounds, MRIs, ultrasound and clinical markers are explored in depth. This review also provides data enthusiasts and the broader health community with a complete assessment of the current state-of-the-art approaches in diagnosing COVID-19. The key issues and future directions are also provided for upcoming researchers.
Databáze: OpenAIRE
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