The Evolving Role of Transmission Electron Microscopy in Emerging Viral Disease Diagnostics: From Classical Ultrastructural Identification to Artificial Intelligence-Assisted Imaging

Emerging and re-emerging viral diseases continue to pose major challenges to both human and veterinary health worldwide. Rapid identification of viral pathogens and detailed understanding of virus-host interactions are essential for disease surveillance, outbreak control and therapeutic development. Although molecular diagnostic techniques such as PCR and next-generation sequencing have become routine laboratory tools, transmission electron microscopy (TEM) remains a unique platform capable of direct visualization of viral particles and virus-induced ultrastructural alterations. This review summarizes the current role of TEM in the detection, identification and characterization of emerging viral pathogens. Particular emphasis is placed on the ultrastructural features of major viral families, including coronaviruses, poxviruses, orthomyxoviruses and arboviruses. Advances in cryo-electron microscopy, electron tomography, immunogold labeling and high-resolution three-dimensional imaging are discussed as transformative technologies that have expanded the diagnostic and research capabilities of electron microscopy. Recent developments in artificial intelligence-assisted image analysis and automated viral recognition systems are also reviewed. Furthermore, the advantages and limitations of TEM are compared with molecular and immunological diagnostic approaches. Despite challenges related to cost, expertise and infrastructure, TEM continues to provide irreplaceable information regarding viral morphogenesis, intracellular replication compartments and host-pathogen interactions. Future integration of advanced imaging technologies with computational tools is expected to strengthen the role of electron microscopy in infectious disease diagnostics and translational virology

کلیدواژه ها:

Transmission Electron Microscopy ، Emerging Viruses ، Viral Diagnostics ، Ultrastructure ، Artificial Intelligence