A deep learning-based model for detecting Leishmania amastigotes in microscopic slides: a new approach to telemedicine.

Journal: BMC infectious diseases
Published Date: Jun 1, 2024

Abstract

BACKGROUND: Leishmaniasis, an illness caused by protozoa, accounts for a substantial number of human fatalities globally, thereby emerging as one of the most fatal parasitic diseases. The conventional methods employed for detecting the Leishmania parasite through microscopy are not only time-consuming but also susceptible to errors. Therefore, the main objective of this study is to develop a model based on deep learning, a subfield of artificial intelligence, that could facilitate automated diagnosis of leishmaniasis.

Authors

  • Alireza Sadeghi
    Intelligent Mobile Robot Lab (IMRL), Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran.
  • Mahdieh Sadeghi
    Student Research Committee, Mazandaran University of Medical Sciences, Sari, Iran.
  • Mahdi Fakhar
    Molecular and Cell Biology Research Center, Dept. of Parasitology, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran.
  • Zakaria Zakariaei
    Toxicology and Forensic Medicine Division, Mazandaran Registry Center for Opioids Poisoning, Antimicrobial Resistance Research Center, Imam Khomeini Hospital, Mazandaran University of Medical Sciences, Sari, Iran.
  • Mohammadreza Sadeghi
    Student Research Committee, Sari Branch, Islamic Azad University, Sari, Iran.
  • Reza Bastani
    Iranian National Registry Center for Lophomoniasis and Toxoplasmosis, Imam Khomeini Hospital, Mazandaran University of Medical Sciences, P. O box, Sari, 48166-33131, Iran.

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