Jove
Visualize
Contact Us

Related Concept Videos

Leishmaniasis01:30

Leishmaniasis

Leishmaniasis is a protozoal disease caused by species of the genus Leishmania and transmitted through the bite of infected female sandflies. The parasite exists in two principal morphological forms during its life cycle. A sandfly acquires intracellular amastigotes from an infected reservoir host, such as a dog. Within the sandfly, these forms differentiate into motile, flagellated promastigotes. During a subsequent blood meal, promastigotes are injected into the human host, where they...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

New technologies for identification and surveillance of Chagas disease vectors.

Revista da Sociedade Brasileira de Medicina Tropical·2026
Same author

Identifying disease vector images in the Americas in the age of artificial intelligence.

Revista da Sociedade Brasileira de Medicina Tropical·2025
Same author

Phlebotomine sand flies associated with a Boa constrictor snake in a chicken coop: species identification, trypanosomatid infection, and control.

Acta tropica·2025
Same author

Persistence of <i>L. V. braziliensis</i> in the Nasal Mucosa of Treated Patients.

Biomedicines·2025
Same author

Automated identification of spotted-fever tick vectors using convolutional neural networks.

Medical and veterinary entomology·2025
Same author

Phytophagous, blood-suckers or predators? Automated identification of Chagas disease vectors and similar bugs using convolutional neural network algorithms.

Acta tropica·2025
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 16, 2026

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

Automated Identification of Cutaneous Leishmaniasis Lesions Using Deep-Learning-Based Artificial Intelligence.

José Fabrício de Carvalho Leal1,2, Daniel Holanda Barroso3, Natália Santos Trindade2

  • 1Graduate Program in Tropical Medicine, Center for Tropical Medicine, Faculty of Medicine, University of Brasília-UnB, Brasília 70904-970, Brazil.

Biomedicines
|January 26, 2024
PubMed
Summary

Deep learning (DL) using AlexNet accurately identifies cutaneous leishmaniasis (CL) skin lesions. This automated tool shows potential for mobile applications to aid healthcare professionals in diagnosing CL.

Keywords:
AlexNetdermatologydiagnosisleishmaniasismachine learningpictures

More Related Videos

Cutaneous Leishmaniasis in the Dorsal Skin of Hamsters: a Useful Model for the Screening of Antileishmanial Drugs
11:36

Cutaneous Leishmaniasis in the Dorsal Skin of Hamsters: a Useful Model for the Screening of Antileishmanial Drugs

Published on: April 21, 2012

22.0K
Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

16.8K

Related Experiment Videos

Last Updated: Jun 16, 2026

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K
Cutaneous Leishmaniasis in the Dorsal Skin of Hamsters: a Useful Model for the Screening of Antileishmanial Drugs
11:36

Cutaneous Leishmaniasis in the Dorsal Skin of Hamsters: a Useful Model for the Screening of Antileishmanial Drugs

Published on: April 21, 2012

22.0K
Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

16.8K

Area of Science:

  • Dermatology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Cutaneous leishmaniasis (CL) diagnosis is challenging due to varied lesion presentations, often mimicking other skin conditions.
  • Accurate differentiation is crucial for timely and effective treatment.

Purpose of the Study:

  • To evaluate the efficacy of AlexNet, a deep learning algorithm, in identifying images of CL skin lesions.
  • To assess the potential of automated diagnosis for assisting healthcare services.

Main Methods:

  • A dataset of 2458 CL lesion images from Midwest Brazil patients was compiled.
  • The AlexNet model was trained and tested on this dataset, differentiating CL from 26 other dermatoses.
  • The image database was split into training (80%), validation (10%), and testing (10%) sets.

Main Results:

  • AlexNet achieved an average accuracy of 95.04% (95% CI: 93.81-96.04) in identifying CL lesions.
  • The algorithm demonstrated excellent performance in distinguishing CL from various other skin conditions.
  • Three simulations confirmed the model's robust identification capabilities.

Conclusions:

  • Automated identification of CL using AlexNet shows significant potential to support clinical diagnosis.
  • This technology could facilitate the development of mobile diagnostic tools for CL in healthcare settings.
  • Further development could improve accessibility and efficiency of CL diagnosis.