ArticleBiomedicines2023
Automated Identification of Cutaneous Leishmaniasis Lesions Using Deep-Learning-Based Artificial Intelligence.
Article in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 15 citations in OpenAlex.
- Special Issue "Leishmaniasis: Pathophysiology, Diagnostics and Current and Emerging Therapeutic Approaches".Biomedicines · 2026Article
- Feasibility study of an AI-powered mobile app to support cutaneous leishmaniasis diagnosis in the Brazilian Amazon.PLoS neglected tropical diseases · 2026Article
- Pathogenic Significance of Trypanosomatids: Progress in Drug Resistance, Control Strategies, and Artificial Intelligence.Interdisciplinary perspectives on infectious diseases · 2026Review
- Toxoplasmosis: Epidemiological Significance, Economic Burden, and Emerging Directions in Drug Discovery and Artificial Intelligence.International journal of microbiology · 2026Review
- A lightweight deep learning framework for reliable microscopy-based diagnosis of cutaneous leishmaniasis.PloS one · 2026Article
- Molecular diagnostics for cutaneous leishmaniasis: progress towards fulfilling the WHO target product profile.Parasitology · 2025Review
- Automated identification of spotted-fever tick vectors using convolutional neural networks.Medical and veterinary entomology · 2025Article
- Advances and Challenges in the Diagnosis of Leishmaniasis.Molecular diagnosis & therapy · 2025Review
- Identifying disease vector images in the Americas in the age of artificial intelligence.Revista da Sociedade Brasileira de Medicina Tropical · 2025Review
- Unlocking Responsive and Unresponsive Signatures: A Transfer Learning Approach for Automated Classification in Cutaneous Leishmaniasis Lesions.Transboundary and emerging diseases · 2025Article
- Exploring Bioinformatics Solutions for Improved Leishmaniasis Diagnostic Tools: A Review.Molecules (Basel, Switzerland) · 2024Review
- Artificial Intelligence in Cutaneous Leishmaniasis Diagnosis: Current Developments and Future Perspectives.Diagnostics (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The polymorphism of cutaneous leishmaniasis (CL) complicates diagnosis in health care services because lesions may be confused with other dermatoses such as sporotrichosis, paracocidiocomycosis, and venous insufficiency. Automated identification of skin diseases based on deep learning (DL) has been applied to assist diagnosis. In this study, we evaluated the performance of AlexNet, a DL algorithm, to identify pictures of CL lesions in patients from Midwest Brazil. We used a set of 2458 pictures (up to 10 of each lesion) obtained from patients treated between 2015 and 2022 in the Leishmaniasis Clinic at the University Hospital of Brasilia. We divided the picture database into training (80%), internal validation (10%), and testing sets (10%), and trained and tested AlexNet to identify pictures of CL lesions. We performed three simulations and trained AlexNet to differentiate CL from 26 other dermatoses (e.g., chromomycosis, ecthyma, venous insufficiency). We obtained an average accuracy of 95.04% (Confidence Interval 95%: 93.81-96.04), indicating an excellent performance of AlexNet in identifying pictures of CL lesions. We conclude that automated CL identification using AlexNet has the potential to assist clinicians in diagnosing skin lesions. These results contribute to the development of a mobile application to assist in the diagnosis of CL in health care services.
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Registered trials
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