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Role of deep learning in early detection of COVID-19: Scoping review

BACKGROUND: Since the onset of the COVID-19 pandemic, the world witnessed disruption on an unprecedented scale affecting our daily lives including but not limited to healthcare, business, education, and transportation. Deep Learning (DL) is a branch of Artificial intelligence (AI) applications, the...

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Detalles Bibliográficos
Autores principales: Alzubaidi, Mahmood, Zubaydi, Haider Dhia, Bin-Salem, Ali Abdulqader, Abd-Alrazaq, Alaa A, Ahmed, Arfan, Househ, Mowafa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Authors. Published by Elsevier B.V. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321699/
https://www.ncbi.nlm.nih.gov/pubmed/34345877
http://dx.doi.org/10.1016/j.cmpbup.2021.100025
Descripción
Sumario:BACKGROUND: Since the onset of the COVID-19 pandemic, the world witnessed disruption on an unprecedented scale affecting our daily lives including but not limited to healthcare, business, education, and transportation. Deep Learning (DL) is a branch of Artificial intelligence (AI) applications, the recent growth of DL includes features that could be helpful in fighting the COVID-19 pandemic. Utilizing such features could support public health efforts. OBJECTIVE: Investigate the literature available in the use of DL technology to support dealing with the COVID-19 crisis. We summarize the literature that uses DL features to analyze datasets for the purpose of a quick COVID-19 detection. METHODS: This review follows PRISMA Extension for Scoping Reviews (PRISMA-ScR). We have scanned the most two commonly used databases (IEEE, ACM). Search terms were identified based on the target intervention (DL) and the target population (COVID-19). Two authors independently handled study selection and one author assigned for data extraction. A narrative approach is used to synthesize the extracted data. RESULTS: We retrieved 53 studies and after passing through PRISMA excluding criteria, only 17 studies are considered in this review. All studies used deep learning for detection of COVID-19 cases in early stage based on different diagnostic modalities. Convolutional Neural Network (CNN) and Transfer Learning (TL) were the most commonly used techniques. CONCLUSION: The included studies showed that DL techniques has significant impact on early detection of COVID-19 with high accuracy rate. However, most of the proposed methods are still in development and not tested in a clinical setting. Further investigation and collaboration are required from the research community and healthcare professionals in order to develop and standardize guidelines for use of DL in the healthcare domain.