02594nas a2200289   4500000000100000008004100001260001200042653002100054653002300075653001200098653001400110653004500124653001800169653002100187100001800208700001300226700001800239700001300257700001400270700001200284700001500296245008900311856006700400490000700467520181600474022001402290       2023                            d  bMDPI AG10aGeneral Medicine10aHansen’s disease10aLeprosy10adiagnosis10aArtificial Intelligence in Public Health10aDeep learning10amachine learning1 aFernandes JRN1 aTeles AS1 aFernandes TRS1 aLima LDB1 aBalhara S1 aGupta N1 aTeixeira S00aArtificial Intelligence on Diagnostic Aid of Leprosy: A Systematic Literature Review  uhttps://www.mdpi.com/2077-0383/13/1/180/pdf?version=17037592620 v133 a<p>Leprosy is a neglected tropical disease that can cause physical injury and mental disability. Diagnosis is primarily clinical, but can be inconclusive due to the absence of initial symptoms and similarity to other dermatological diseases. Artificial intelligence (AI) techniques have been used in dermatology, assisting clinical procedures and diagnostics. In particular, AI-supported solutions have been proposed in the literature to aid in the diagnosis of leprosy, and this Systematic Literature Review (SLR) aims to characterize the state of the art. This SLR followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework and was conducted in the following databases: ACM Digital Library, IEEE Digital Library, ISI Web of Science, Scopus, and PubMed. Potentially relevant research articles were retrieved. The researchers applied criteria to select the studies, assess their quality, and perform the data extraction process. Moreover, 1659 studies were retrieved, of which 21 were included in the review after selection. Most of the studies used images of skin lesions, classical machine learning algorithms, and multi-class classification tasks to develop models to diagnose dermatological diseases. Most of the reviewed articles did not target leprosy as the study’s primary objective but rather the classification of different skin diseases (among them, leprosy). Although AI-supported leprosy diagnosis is constantly evolving, research in this area is still in its early stage, then studies are required to make AI solutions mature enough to be transformed into clinical practice. Expanding research efforts on leprosy diagnosis, coupled with the advocacy of open science in leveraging AI for diagnostic support, can yield robust and influential outcomes.</p>
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