Department of Chemistry, Faculty of Mathematics and Natural Sciences, Padjadjaran University, Bandung 40173, Indonesia.
Department of Biomedical Engineering, Near East University, Mersin 99138, Turkey.
Sensors (Basel). 2022 Dec 30;23(1):426. doi: 10.3390/s23010426.
Despite the fact that COVID-19 is no longer a global pandemic due to development and integration of different technologies for the diagnosis and treatment of the disease, technological advancement in the field of molecular biology, electronics, computer science, artificial intelligence, Internet of Things, nanotechnology, etc. has led to the development of molecular approaches and computer aided diagnosis for the detection of COVID-19. This study provides a holistic approach on COVID-19 detection based on (1) molecular diagnosis which includes RT-PCR, antigen-antibody, and CRISPR-based biosensors and (2) computer aided detection based on AI-driven models which include deep learning and transfer learning approach. The review also provide comparison between these two emerging technologies and open research issues for the development of smart-IoMT-enabled platforms for the detection of COVID-19.
尽管由于诊断和治疗疾病的不同技术的发展和整合，COVID-19 不再是一种全球性的大流行病，但分子生物学、电子、计算机科学、人工智能、物联网、纳米技术等领域的技术进步，已经导致了针对 COVID-19 的分子方法和计算机辅助诊断的发展。本研究基于 (1) 分子诊断，包括 RT-PCR、抗原-抗体和基于 CRISPR 的生物传感器，以及 (2) 基于人工智能驱动模型的计算机辅助检测，包括深度学习和迁移学习方法，提供了一种全面的 COVID-19 检测方法。该综述还比较了这两种新兴技术，并为开发用于 COVID-19 检测的智能-IoMT 启用平台提出了开放的研究问题。
