Leprosy is a chronic infectious disease that continues to be present in more than 120 countries. In 2023, over 20,000 new cases were reported in Brazil, making it the second most endemic country in the world. The development of new information systems that utilize available clinical records is essential to support decision-making during the treatment of this highly stigmatizing disease. In this study, we present preliminary results demonstrating the feasibility of using YOLO for the automatic recognition of non-textual data from neurological assessments of patients undergoing treatment. Our approach achieved an accuracy of 97.5% in recognizing sensory assessment records used in the Brazilian healthcare system.
Leprosy, caused by Mycobacterium leprae, remains a global challenge, requiring strategies to achieve disease elimination by 2030. In Brazil, the Simplified Neurological Assessment (from Portuguese Avaliação Neurológica Simplificada, ANS) is mandatory for suspected cases; however, the form is still manually fulfilled, which limits the use of data. This study evaluates computer vision models (YOLOv8x, YOLO11x, Faster R-CNN) for detecting hand and foot sensitivity regions from ANS forms. All models were evaluated based on precision, recall, mean average precision (mAP) and confusion matrix. YOLO variants achieved over 94% precision and 84% recall across all classes. Automating ANS data extraction can facilitate the creation of structured datasets, enhancing disease monitoring and enabling the train of predictive models.
This study addresses the challenge of improving Hansen’s disease understating and monitoring in Brazil’s public healthcare system (SUS) through the development of a digital record system rooted in Soft Systems Methodology (SSM). The proposed solution, the hansen.app, integrates participatory cycles with real stakeholders to adapt and digitize the Simplified Neurological Assessment (from Portuguese ANS) form, ensuring data fidelity and enhanced usability for healthcare professionals and patients. Hence, data modeling and the prototype aim to reduce the complexity of Hansen’s disease care and monitoring by delivering more essential data to improve the knowledge capacity of the health-care system.
Doenças como malária, arbovírus, tuberculose e hanseníase, conhecidas como doenças tropicais negligenciadas, representam uma ameaça à saúde das populações de baixa renda, impactando negativamente a qualidade de vida dos indivíduos afetados. Este artigo apresenta o Health Guardian, um aplicativo colaborativo que utiliza inteligência artificial para auxiliar os profissionais de saúde no processo de tomada de decisão referente ao tratamento de doenças tropicais negligenciadas, visando melhorar a eficiência do atendimento e a qualidade de vida dos pacientes.
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).
A Estratégia Global da Organização Mundial de Saúde (OMS) para Hanseníase 2021–2030 define a ampliação das atividades de prevenção como um dos seus pilares estratégicos. Estima-se que cerca de 25 milhões de pessoas podem se beneficiar de intervenções profiláticas, a partir da administração de uma dose única de rifampicina para os contatos próximos. Analisar os dados referentes aos exames em contatos próximos, pode ser útil para auxiliar nesta estratégia preventiva, além de fornecer subsídios para o processo de tomada de decisão por parte das autoridades públicas de saúde. Este trabalho tem como objetivo apresentar um painel interativo de monitoramento dos casos de hanseníase no Brasil, com foco na taxa de contatos examinados pela Unidade Federativa (UF). O painel utiliza a base de dados do Sistema de Informação de Agravos de Notificação (SINAN), de 2001 a 2023, contendo 923.920 registros…
A Estratégia Global da Organização Mundial de Saúde (OMS) para Hanseníase 2021–2030 define a prevenção de novas incapacidades como um dos seus pilares estratégicos. Estima-se que cerca de quatro milhões de pessoas vivam com incapacidades físicas devido à hanseníase. Analisar os dados referentes ao Grau de Incapacidade Física (GIF) decorrente da hanseníase pode auxiliar na melhoria do tratamento fornecido aos pacientes, bem como fornecer subsídios para auxiliar no processo de tomada de decisão por parte das autoridades públicas de saúde.
Leprosy is a neglected tropical disease (NTD) caused by Mycobacterium leprae. It predominantly occurs in areas with poor socio-economic conditions and affects the skin and peripheral nerves. Without proper treatment, leprosy can lead to severe physical deformities, making it a highly stigmatizing disease. This study evaluated four machine learning models that predict the progression of the grade of physical disability related to leprosy based on clinical and socio-demographic data sourced from a Brazilian database. The database contained notifications of leprosy cases spanning from 2001 to 2023. The objective was to predict the likelihood of an increase in the disability grade caused by the disease. After preprocessing the data, a total of 199,924 records and 12 clinical and socio-demographic variables were selected for analysis. The study evaluated the performance of four machine learning models: Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost) and Gradient Boosting (GB). The RF model had the highest performance achieving a recall of 82.59% (±0.02), followed by GB with 77.27%, AdaBoost with 73.11% and DT with 72.09%. These results suggest that the models are proficient at identifying instances of the positive class, which in this context means predicting the progression of leprosy-related disability, thereby reducing the number of false negatives. This ability is crucial in predicting the progression of the disease and potentially improving patient outcomes. This study’s findings indicate that machine learning can be a valuable tool in managing leprosy, particularly in predicting the worsening of physical disabilities. By leveraging clinical and socio-demographic data, healthcare providers can better identify patients at higher risk and tailor their treatment strategies accordingly.
Leprosy is a neglected tropical disease (NTD). Brazil has long been recognized as an endemic country for leprosy; it is the second highest leprosy burden country in the world. In 2023, Brazil reported 22,773 new cases of leprosy, which represents 92% of the new cases in the Americas, 13% of 174,087 new cases worldwide. Leprosy can lead to severe physical deformities, making it a highly stigmatizing disease.
This study evaluates four machine learning models – Decision Tree, Random Forest, Adaptive Boosting (AdaBoost) and Gradient Boosting (GB) – to predict the progression of the grade of physical disability. We utilized a real Brazilian dataset extracted from SINAN, the Brazilian national notifiable disease information system. The dataset contained 12 attributes (including the target class) and 923,920 records of leprosy cases from 2001 to 2023. In the dataset, 157,062 patients showed no evolution or reduction in the grade of impairment function (GIF) while 12,957 exhibited an increase in the GIF from diagnosis to cure. We found that 29,905 cases demonstrated a decrease in GIF; these records were excluded from the dataset for model training. After preprocessing steps, a total of 199,924 records and 12 clinical and sociodemographic variables were selected for training and testing models. Models were evaluated using recall, also referred to as sensitivity, as the primary evaluation metric. Recall quantifies the proportion of true positive cases identified by the model out of all actual positive cases….