Cutaneous Leishmaniasis (CL) is a major global health problem requiring appropriate diagnosis methods. Its diagnosis is challenging, particularly in resource-limited settings. The integration of Artificial Intelligence (AI) into medical diagnostics has shown promising results in various fields, including dermatology. In this systematic review, we aim to highlight the value of using AI for CL diagnosis and the AI-based algorithms that are employed in this process, and to identify gaps that need to be addressed. Our work highlights that only a limited number of studies are related to using AI algorithms for CL diagnosis. Among these studies, seven gaps were identified for future research. Addressing these considerations will pave the way for the development of robust AI systems and encourage more research in CL detection by AI. This could contribute to improving CL diagnosis and, ultimately, healthcare outcomes in CL-endemic regions.

In Morocco, cutaneous leishmaniasis (CL) caused by Leishmania (L.) tropica is an important health problem. Despite the high incidence of CL in the country, the genomic heterogeneity of these parasites is still incompletely understood. In this study, we sequenced the genomes of 14 Moroccan isolates of L. tropica collected from confirmed cases of CL to investigate their genomic heterogeneity. Comparative genomics analyses were conducted by applying the recently established Genome Instability Pipeline (GIP), which allowed us to conduct phylogenomic and principal components analyses (PCA), and to assess genomic variations at the levels of the karyotype, gene copy number, single nucleotide polymorphisms (SNPs) and small insertions/deletions (INDELs) variants. Read-depth analyses revealed a mostly disomic karyotype, with the exception of the stable tetrasomy of chromosome 31. In contrast, we identified important gene copy number variations across all isolates, which affect known virulence genes and thus were probably selected in the field. SNP-based cluster analysis of the 14 isolates revealed a core group of 12 strains that formed a tight cluster and shared 45.1 % (87 751) of SNPs, as well as two strains (M3015, Ltr_16) that clustered separately from each other and the core group, suggesting the circulation of genetically highly diverse strains in Morocco. Phylogenetic analysis, which compared our 14 L. tropica isolates against 40 published genomes of L. tropica from a diverse array of locations, confirmed the genetic difference of our Moroccan isolates from all other isolates examined. In conclusion, our results indicate potential regional variations in SNP profiles that may differentiate Moroccan L. tropica from other L. tropica strains circulating in endemic countries in the Middle East. Our report paves the way for future research with a larger number of strains that will allow correlation of diverse phenotypes (resistance to treatments, virulence) and origins (geography, host species, year of isolation) to defined genomic signals such as gene copy number variations or SNP profiles that may represent interesting biomarker candidates

Leishmania tropica causes cutaneous leishmaniasis (CL) from North Africa and Ethiopia to India, and is reported to be transmitted from human to human through sand fly bites. While this species is characterized by a high genomic diversity in all the area of endemicity, there is very little information on diversity at a microepidemiological scale. Here, we concentrated on an epidemic Moroccan focus of CL and studied transmission patterns by comparative genomics of parasites in human patients. We used a culture-independent method of genome sequencing, applied directly on dermal scrapings. We identified 7 groups of nearly identical genotypes, as well as parasites with mixed ancestry. Our results reveal a microfocal transmission among humans, underlain by (pseudo)clonal and sexual reproductive modes. This study demonstrates the power of direct genome sequencing for evolutionary genetics at a microepidemiological scale.

Cutaneous leishmaniasis (CL) caused by Leishmania protozoa and transmitted through infected sandfly bites, poses a significant public health burden in Morocco. Our research aims to retrospectively assess spatio-temporal patterns of CL in the most densely populated region of the country, Casablanca-Settat, during a period of 14 years (2009–2022). We investigate epidemiological trends, seasonal fluctuations and climatic factors influence on CL prevalence and predict cases in the region’s most important CL focus. Using Geographic Information Systems (GIS) and statistical methods, we analyzed CL data obtained from the Moroccan Ministry of Health and Social Protection with demographic information and climate-related parameters. Four machine learning prediction models for CL cases in the El Brouj focus were compared and their performance was evaluated. Our results revealed a steady increase in CL cases until 2019, followed by a significant surge, with Leishmania tropica identified as the causative agent for all documented cases. The incidence rates varied among demographic groups, with higher rates observed in females and children aged 5-14. Seasonal analysis indicated a notable increase in cases during spring. Spatial distribution analysis identified El Brouj as the sole endemic CL focus_and the highest hotspot in the area, underscoring the necessity of targeted interventions in this location. Furthermore, Spearman correlation analysis revealed a significant association between CL cases and low temperature and precipitation, emphasizing climate variables role in disease incidence. Prediction modeling for future CL cases in El Brouj focus was assessed with six different models and Linear Regression was the most suitable. This retrospective study provides crucial insights into CL epidemiology in the Casablanca-Settat region, highlighting the need for implementing a coordinated approach tailored to the region that could help curb the spread of CL in Settat province, given the predictive model’s outlook on future CL trends in this area.

Leishmania tropica is causing cutaneous leishmaniasis (CL) from North Africa to India and in Ethiopia and is reported to be transmitted from humans to humans through sand fly bites. While this species is characterized by a high genetic diversity all over the area of endemicity, there is very little information on diversity at micro-epidemiological scale. Here, we zoomed on an epidemic Moroccan focus of CL - restricted in space and time - and studied transmission patterns by comparative genomics of parasites in human patients. We used a culture-independent method of genome sequencing based on Leishmania genome capture (SureSelect-sequencing, SuSL-seq), applied directly on dermal scrapings. We first compared the genome of paired samples: i.e. parasites in host tissues analyzed by SuSL-seq and derived isolates shortly maintained in culture and analyzed by whole genome sequencing (WGS). Despite the low passage number of isolates, significant differences were observed between karyotypes of 4/7 paired samples, highlighting the clinical and epidemiological relevance of direct genome sequencing. Secondly, we identified 7 groups of nearly identical genotypes, characteristic of clonal propagation as well as parasites with mixed ancestry, a signature of genetic exchange. Our results reveal a micro-focal transmission among humans, underlain by clonal and sexual reproductive modes. This study demonstrates the power of direct genome sequencing for evolutionary genetics at a micro-epidemiological scale.