Background According to the World Health Organization, achieving targets for control of leprosy by 2030 will require disease elimination and interruption of transmission at the national or regional level. India and Brazil have reported the highest leprosy burden in the last few decades, revealing the need for strategies and tools to help health professionals correctly manage and control the disease. Objective The main objective of this study was to develop a cross-platform app for leprosy screening based on artificial intelligence (AI) with the goal of increasing accessibility of an accurate method of classifying leprosy treatment for health professionals, especially for communities further away from major diagnostic centers. Toward this end, we analyzed the quality of leprosy data in Brazil on the National Notifiable Diseases Information System (SINAN). Methods Leprosy data were extracted from the SINAN database, carefully cleaned, and used to build AI decision models based on the random forest algorithm to predict operational classification in paucibacillary or multibacillary leprosy. We used Python programming language to extract and clean the data, and R programming language to train and test the AI model via cross-validation. To allow broad access, we deployed the final random forest classification model in a web app via shinyApp using data available from the Brazilian Institute of Geography and Statistics and the Department of Informatics of the Unified Health System. Results We mapped the dispersion of leprosy incidence in Brazil from 2014 to 2018, and found a particularly high number of cases in central Brazil in 2014 that further increased in 2018 in the state of Mato Grosso. For some municipalities, up to 80% of cases showed some data discrepancy. Of a total of 21,047 discrepancies detected, the most common was “operational classification does not match the clinical form.” After data processing, we identified a total of 77,628 cases with missing data. The sensitivity and specificity of the AI model applied for the operational classification of leprosy was 93.97% and 87.09%, respectively. Conclusions The proposed app was able to recognize patterns in leprosy cases registered in the SINAN database and to classify new patients with paucibacillary or multibacillary leprosy, thereby reducing the probability of incorrect assignment by health centers. The collection and notification of data on leprosy in Brazil seem to lack specific validation to increase the quality of the data for implementations via AI. The AI models implemented in this work had satisfactory accuracy across Brazilian states and could be a complementary diagnosis tool, especially in remote areas with few specialist physicians.

Background Early detection of Mycobacterium leprae is a key strategy for disrupting the transmission chain of leprosy and preventing the potential onset of physical disabilities. Clinical diagnosis is essential, but some of the presented symptoms may go unnoticed, even by specialists. In areas of greater endemicity, serological and molecular tests have been performed and analyzed separately for the follow-up of household contacts, who are at high risk of developing the disease. The accuracy of these tests is still debated, and it is necessary to make them more reliable, especially for the identification of cases of leprosy between contacts. We proposed an integrated analysis of molecular and serological methods using artificial intelligence by the random forest (RF) algorithm to better diagnose and predict new cases of leprosy. Methods The study was developed in Governador Valadares, Brazil, a hyperendemic region for leprosy. A longitudinal study was performed, including new cases diagnosed in 2011 and their respective household contacts, who were followed in 2011, 2012, and 2016. All contacts were diligently evaluated by clinicians from Reference Center for Endemic Diseases (CREDEN-PES) before being classified as asymptomatic. Samples of slit skin smears (SSS) from the earlobe of the patients and household contacts were collected for quantitative polymerase chain reaction (qPCR) of 16S rRNA, and peripheral blood samples were collected for ELISA assays to detect LID-1 and ND-O-LID. Results The statistical analysis of the tests revealed sensitivity for anti-LID-1 (63.2%), anti-ND-O-LID (57.9%), qPCR SSS (36.8%), and smear microscopy (30.2%). However, the use of RF allowed for an expressive increase in sensitivity in the diagnosis of multibacillary leprosy (90.5%) and especially paucibacillary leprosy (70.6%). It is important to report that the specificity was 92.5%. Conclusion The proposed model using RF allows for the diagnosis of leprosy with high sensitivity and specificity and the early identification of new cases among household contacts.

Background Immunological biomarkers have often been used as a complementary approach to support clinical diagnosis in several infectious diseases. The lack of commercially available laboratory tests for conclusive early diagnosis of leprosy has motivated the search for novel methods for accurate diagnosis. In the present study, we describe an integrated analysis of a cytokine release assay using a machine learning approach to create a decision tree algorithm. This algorithm was used to classify leprosy clinical forms and monitor household contacts. Methods A model of Mycobacterium leprae (M. leprae) antigen-specific in vitro assay with subsequent cytokine measurements by ELISA was employed to measure the levels of TNF, IFN-γ, IL-4, and IL-10 in culture supernatants of peripheral blood mononuclear cells from leprosy patients, healthy controls as well as household contacts. Receiver Operating Characteristic (ROC) curve analysis was carried out to define each cytokine's global accuracy and performance indices to identify clinical subgroups. Results Data demonstrated that TNF [Control Culture (CC): AUC=0.72; antigen-stimulated culture (Ml): AUC=0.80] and IL-10 (CC: AUC=0.77; Ml: AUC=0.71) were the most accurate biomarkers to classify subgroups of household contacts and leprosy patients, respectively. Decision tree classifier algorithms were for TNF analysis categorized subgroups of household contacts according to the operational classification with moderate accuracy (CC:79%, 48/61, and Ml:84%, 51/61). Additionally, IL-10 analysis categorized leprosy patients' subgroups with moderate accuracy (CC:73%, 22/30 and Ml:70%, 21/30). Conclusions Together, our findings demonstrated that a cytokine-release assay is a promising method to complement clinical diagnosis, ultimately contributing to effective control of the disease.

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Background Schistosomiasis is a chronic disease that affects over 200 million people worldwide. A pivotal role of IL-10 is down-regulating Th1 and Th2 responses to schistosome antigens, which can favor the parasite establishment. The SmATPDases degrade ATP and ADP in AMP and adenosine, a molecule with anti-inflammatory properties. We evaluated the expression of SmATPDases 1 and 2 enzymes in S. mansoni eggs obtained from infected individuals as a possible parasite-related factor that could influence the host immune response and the clinical outcome of the disease. Methods Fecal samples were collected from 40 infected individuals to detect coding regions of the enzymes by the qPCR. The production of cytokines was measured in supernatants of PBMC cultures. The analysis was performed by the global median determination for each cytokine and set up high producers (HP) of cytokines. Results Six individuals expressed SmATPDase 1 in their fecal samples, 6 expressed SmATPDase 2, and 6 expressed both enzymes. The group who expressed only SmATPDase 1 showed a high frequency of IFN-γ, TNF, IL-4 HP, and a low frequency of IL-6 HP. The group who expressed only SmATPDase 2 showed a high frequency of IFN-γ, IL-6, and IL-4 HP and a low frequency of IL-10 HP. The group who expressed both enzymes showed a high frequency of IL-10 HP and low frequencies of IFN-γ, IL-6, IL-2, IL-4, and IL-13 HP. In the group that had SmATPDase 2 expression was observed higher indices the ratio between IFN-γ/IL-10 than individuals that showed expression both enzymes. The positive correlation between infection intensity and IL-10 levels remained only in the positive SmATPDase group. Overall, the analysis revealed that 62.5% of the cytokines presented reduced frequency in the group of individuals expressing both enzymes, the IL-10 is the only cytokine induced by the expression of both enzymes and the expression profile of SmATPDases is relevant data for grouping individuals. Conclusions The expression of both enzymes in the parasite's eggs seems to be a new undescribed factor that negatively modulates the host immune response by inducing high IL-10 production, which, in turn, can contribute to the survival of the parasite.