Alzubaidi Mahmood, Zubaydi Haider Dhia, Bin-Salem Ali Abdulqader, Abd-Alrazaq Alaa A, Ahmed Arfan, Househ Mowafa
College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
National Advanced IPv6 Centre, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia.
Comput Methods Programs Biomed Update. 2021;1:100025. doi: 10.1016/j.cmpbup.2021.100025. Epub 2021 Jul 30.
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.
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.
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.
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.
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.
自新冠疫情爆发以来，世界经历了前所未有的破坏，影响了我们的日常生活，包括但不限于医疗保健、商业、教育和交通。深度学习（DL）是人工智能（AI）应用的一个分支，DL最近的发展所具备的特性可能有助于抗击新冠疫情。利用这些特性可以支持公共卫生工作。
调查利用DL技术支持应对新冠危机的现有文献。我们总结了使用DL特性来分析数据集以实现快速新冠检测目的的文献。
本综述遵循系统综述扩展版的首选报告项目（PRISMA-ScR）。我们检索了两个最常用的数据库（IEEE、ACM）。基于目标干预（DL）和目标人群（新冠）确定搜索词。两位作者独立进行研究筛选，一位作者负责数据提取。采用叙述性方法对提取的数据进行综合。
我们检索到53项研究，经过PRISMA排除标准筛选后，本综述仅纳入17项研究。所有研究均基于不同的诊断方式，使用深度学习来早期检测新冠病例。卷积神经网络（CNN）和迁移学习（TL）是最常用的技术。
纳入的研究表明，DL技术对新冠的早期检测具有显著影响，准确率很高。然而，大多数提出的方法仍在开发中，尚未在临床环境中进行测试。研究界和医疗专业人员需要进一步开展调查与合作，以便制定和规范DL在医疗领域的使用指南。
