Lynn, Theo
ORCID: 0000-0001-9284-7580, Gomes Vilar de Andrade, Hilson, da Silva Rocha, Elisson
ORCID: 0000-0002-7742-2995, de Carvalho Monteiro, Kayo H., Matos de Morais, Cleber, Moura dos Santos, Danielle Christine, Cassimiro Nascimento, Dimas, Dourado, Raphael A. and Takako Endo, Patricia
ORCID: 0000-0002-9163-5583
(2025)
On the usage of artificial intelligence in leprosy care: A systematic literature review.
PLoS Computational Biology, 21
(6).
ISSN 1553-7358
Abstract
Leprosy, or Hansen’s disease, is a Neglected Tropical Disease (NTD) caused by Mycobacterium leprae that mainly affects the skin and peripheral nerves, causing neuropathy to varying degrees. It can result in physical disabilities and functional loss and is particularly prevalent amongst the most vulnerable populations in tropical and subtropical regions worldwide. The persistent stigma and social exclusion associated with leprosy complicate eradication efforts exacerbate the wider challenges faced by NTDs in sourcing the necessary resources and attention for control and elimination. The introduction of Multidrug Therapy (MDT) significantly lowers the global disease burden. Despite this breakthrough in the treatment of leprosy, over 200,000 new leprosy cases are reported annually across more than 120 countries, emphasizing the need for ongoing detection and management efforts. Artificial Intelligence (AI) has the potential to transform leprosy care by accelerating early detection, improving accurate diagnosis, and enabling
predictive modeling to improve the quality for those affected. The potential of AI to provide information to assist healthcare professionals in interventions that reduce the risk of disability, and consequently stigma, particularly in endemic regions, presents a promising path to reducing the incidence of leprosy and improving integration social status of
patients. This systematic literature review (SLR) examines the state of the art in research on the use of AI for leprosy care. From an initial 657 works from six scientific databases (ACM Digital Library, IEEE Xplore, PubMed, Scopus, Science Direct and Springer), only 30 relevant works were identified, after analysis of three independent reviewers. We have excluded works due duplication, couldn’t be retrieved and quality assessment. Results show that current research is focused primarily on the identification of symptoms using image based classification using three main techniques, neural networks, convolutional neural networks, and support vector machines; a small number of studies focus on other thematic areas of leprosy care. A comprehensive systematic approach
to research on the application of AI to leprosy care can make a meaningful contribution to a leprosy-free world and help deliver on the promise of the Sustainable Development Goals (SDG)
Metadata
| Item Type: | Article (Published) |
|---|---|
| Refereed: | Yes |
| Uncontrolled Keywords: | Artificial intelligence and leprosy care |
| Subjects: | Computer Science > Artificial intelligence Computer Science > Computer engineering |
| DCU Faculties and Centres: | DCU Faculties and Schools > DCU Business School |
| Publisher: | Public Library of Science |
| Official URL: | https://journals.plos.org/ploscompbiol/article?id=... |
| Copyright Information: | Authors |
| ID Code: | 31414 |
| Deposited On: | 15 Aug 2025 10:47 by Gordon Kennedy . Last Modified 15 Aug 2025 10:47 |
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