Presented in 13th Emirates Pathology, Digital Pathology & Cancer Conference Holiday Inn Dubai, UAE & Virtual
Speaker Name: Dr. Nina Tamazyan
Research interest: Thyroid cytology, serous fluid cytology, cell-blocks
Global Journal of Pathology & Laboratory Medicine
Unified Citation Journals, Pathology 2024, ISSN 2754-0952
Keywords: Digital cytology, artificial intelligence, fine needle aspiration, rapid-on-site evaluation, cytopathology
Biography: Doctor Nina Tamazyan has graduated from Yerevan State Medical Universiry and has completed her residency program in Clinical Laboratory Diagnostics. She works in Clinical Scientific Department as cytopathologist in Pavlov First Medical University. She has published more than 5abstracts in different journals. In 2018 she successfully passed the exam, organized by International Academy of cytology and received IAC cytopahtology diploma.
Introduction:
Cytopathology is a subspecialty of pathology that examines cell specimens. It is a minimally invasive, simple, inexpensive diagnostic method, which has a considerable success for diagnostics of multiple cancer types and detection of cancer at early stages. Cell sampling is essential for making correct diagnostics and therapy decisions. The success of cytological diagnosis depends on the principles of laboratory management. Key concepts in Laboratory management includes regulation and accreditation, staffing, workload limits, proficiency testing, continuing education, reporting systems, quality assurance, statistics and data.
In spite of many advantages, cytology has its limitations. The main limitations are the difficulties to obtain diagnostic material, shortage of work force and intra- and interobserver variability. To improve the accuracy of cytological diagnosis there are several solutions.US-guided and percutaneous computerised tomography (CT)-guided fine needle biopsies and PET-CT guided FNA are generally a reliable method for obtaining cell samples. Rapid on-site evaluation (ROSE) of fine needle aspiration (FNA) improves the quality of FNA specimens and decreases health care costs by reducing rate of non-diagnostic specimens.
The advancements in the fields of technology and networking have changed the world including the fields of medicine. Digitalisation and artificial intelligence (AI) technologies are transforming cytopathology practice as well. They allow to solve many problems, such as the understaffing, picture archiving and communication among specialists. AI applications are successfully used for gynecological screening for many years. Recently several Artificial Intelligence based models were developed for Non-Gynecological Cancer Cytopathology. There is new model worked up in Russia. Platform is promising, even if at the moment it is used only in histopathology. The use of digital image algorithms in cytopathology enables not only automation and computer-assisted diagnosis, but also offers the potential to standardize diagnoses, improve accuracy, and better explore pathologic diseases.
References:
[2] Bryn Nelson PhD, William Faquin MD, PhD (june 2023) Cancer Cytopathology,
[3] William W, Ware A, Basaza-Ejiri AH, Obungoloch J. (2018) Comput Methods Programs Biomed. 164: 15-22.
[4] Nishant Thakur, Mohammad Rizwan Alam, Jamshid Abdul-Ghafar, and Yosep Chong, Cancer (Basel), (2022jul) 14:3529
Tags:
Cancer, Immunodeficiency States, Immunophenotyping, Molecular Diagnostics & Proteomics, Evolutionary Medicine, Functional Identification & Biomarkers, Hematopoietic & other Malignancies, Anatomical Pathology, Clinical Pathology, Dermato Pathology, Forensic Pathology, Hemato Pathology, Histopathology, Molecular Pathology, Surgical Pathology, Histopathology, Chemical Pathology, Hematopathology, Histopathology, Cytopathology, Forensic Pathology, Dermatopathology, Clinical Biochemistry, Infection Control, Cytokines, Enzymology, Endocrinology, Cellular Lineage, Virology, Rheology, Toxicology, Neuropathology, Diagnostic Pathology, Mesothelial Proliferations, Transfusion medicine, Clinical microbiology, Cytogenetics, Molecular Genetics Pathology, Immunopathology, Veterinary Pathology, Anatomical Pathology, General Pathology
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