Clinical Diagnosis, Prognosis and Data Analysis of Medical Parasites and Arthropods

Dear Colleagues,

Parasitic diseases have a considerable socio-economic impact on society. Globally, around 3.5 billion people are affected by intestinal parasitic infections, and more than 200,000 related deaths are reported annually. Many arthropods play a critical role in human health, acting as vectors and intermediate hosts of human pathogens and displaying the potential to cause outbreaks in overcrowded areas.

In medical parasitology, diagnoses are mainly based on traditional diagnostic methods. Microscopy, rapid diagnostic tests (RDTs), and PCR are the most commonly used diagnostic methods.

To overcome the limitations of traditional diagnostic methods in parasite and arthropod identification, advanced diagnostic approaches, such as artificial intelligence technology, have emerged. Artificial intelligence (AI) algorithms have been highly developed, and deep learning algorithms have also emerged, becoming an important component of clinical microbiology informatics. AI in general and computer vision specifically are emerging tools that clinical microbiologists need to study, develop, and implement in order to improve clinical microbiology.

This Special Issue aims to invite authors to present their experiences in the diagnosis, prognosis, and data analysis of medical parasites and arthropods.

Prof. Dr. Tamer Sanlidag
Dr. Dilber Uzun Ozsahin
Dr. Ilker Ozsahin
Guest Editors

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