COVID-19: an opportunity of systematic integration for Chagas disease. Example of a community-based approach within the Bolivian population in Barcelona.

BACKGROUND: As a Neglected Tropical Disease associated with Latin America, Chagas Disease (CD) is little known in non-endemic territories of the Americas, Europe and Western Pacific, making its control challenging, with limited detection rates, healthcare access and consequent epidemiological silence. This is reinforced by its biomedical characteristics-it is usually asymptomatic-and the fact that it mostly affects people with low social and financial resources. Because CD is mainly a chronic infection, which principally causes a cardiomyopathy and can also cause a prothrombotic status, it increases the risk of contracting severe COVID-19. METHODS: In order to get an accurate picture of CD and COVID-19 overlapping and co-infection, this operational research draws on community-based experience and participative-action-research components. It was conducted during the Bolivian elections in Barcelona on a representative sample of that community. RESULTS: The results show that 55% of the people interviewed had already undergone a previous T. cruzi infection screening-among which 81% were diagnosed in Catalonia and 19% in Bolivia. The prevalence of T. cruzi infection was 18.3% (with 3.3% of discordant results), the SARS-CoV-2 22.3% and the coinfection rate, 6%. The benefits of an integrated approach for COVID-19 and CD were shown, since it only took an average of 25% of additional time per patient and undoubtedly empowered the patients about the co-infection, its detection and care. Finally, the rapid diagnostic test used for COVID-19 showed a sensitivity of 89.5%. CONCLUSIONS: This research addresses CD and its co-infection, through an innovative way, an opportunity of systematic integration, during the COVID-19 pandemic.

iMAGING: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope.

Introduction: Malaria is one of the most prevalent infectious diseases in sub-Saharan Africa, with 247 million cases reported worldwide in 2021 according to the World Health Organization. Optical microscopy remains the gold standard technique for malaria diagnosis, however, it requires expertise, is time-consuming and difficult to reproduce. Therefore, new diagnostic techniques based on digital image analysis using artificial intelligence tools can improve diagnosis and help automate it. Methods: In this study, a dataset of 2571 labeled thick blood smear images were created. YOLOv5x, Faster R-CNN, SSD, and RetinaNet object detection neural networks were trained on the same dataset to evaluate their performance in Plasmodium parasite detection. Attention modules were applied and compared with YOLOv5x results. To automate the entire diagnostic process, a prototype of 3D-printed pieces was designed for the robotization of conventional optical microscopy, capable of auto-focusing the sample and tracking the entire slide. Results: Comparative analysis yielded a performance for YOLOv5x on a test set of 92.10% precision, 93.50% recall, 92.79% F-score, and 94.40% mAP0.5 for leukocyte, early and mature Plasmodium trophozoites overall detection. F-score values of each category were 99.0% for leukocytes, 88.6% for early trophozoites and 87.3% for mature trophozoites detection. Attention modules performance show non-significant statistical differences when compared to YOLOv5x original trained model. The predictive models were integrated into a smartphone-computer application for the purpose of image-based diagnostics in the laboratory. The system can perform a fully automated diagnosis by the auto-focus and X-Y movements of the robotized microscope, the CNN models trained for digital image analysis, and the smartphone device. The new prototype would determine whether a Giemsa-stained thick blood smear sample is positive/negative for Plasmodium infection and its parasite levels. The whole system was integrated into the iMAGING smartphone application. Conclusion: The coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of Plasmodium parasites in digital images with a smartphone software and AI algorithms confers the prototype the optimal features to join the global effort against malaria, neglected tropical diseases and other infectious diseases.

Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review.

Malaria is an infectious disease caused by parasites of the genus Plasmodium spp. It is transmitted to humans by the bite of an infected female Anopheles mosquito. It is the most common disease in resource-poor settings, with 241 million malaria cases reported in 2020 according to the World Health Organization. Optical microscopy examination of blood smears is the gold standard technique for malaria diagnosis; however, it is a time-consuming method and a well-trained microscopist is needed to perform the microbiological diagnosis. New techniques based on digital imaging analysis by deep learning and artificial intelligence methods are a challenging alternative tool for the diagnosis of infectious diseases. In particular, systems based on Convolutional Neural Networks for image detection of the malaria parasites emulate the microscopy visualization of an expert. Microscope automation provides a fast and low-cost diagnosis, requiring less supervision. Smartphones are a suitable option for microscopic diagnosis, allowing image capture and software identification of parasites. In addition, image analysis techniques could be a fast and optimal solution for the diagnosis of malaria, tuberculosis, or Neglected Tropical Diseases in endemic areas with low resources. The implementation of automated diagnosis by using smartphone applications and new digital imaging technologies in low-income areas is a challenge to achieve. Moreover, automating the movement of the microscope slide and image autofocusing of the samples by hardware implementation would systemize the procedure. These new diagnostic tools would join the global effort to fight against pandemic malaria and other infectious and poverty-related diseases.