The malaria system microApp: A new, mobile device-based tool for malaria diagnosis

Background: Malaria is a public health problem that affects remote areas worldwide. Climate change has contributed to the problem by allowing for the survival of Anopheles in previously uninhabited areas. As such, several groups have made developing news systems for the automated diagnosis of malaria a priority. Objective: The objective of this study was to develop a new, automated, mobile device-based diagnostic system for malaria. The system uses Giemsa-stained peripheral blood samples combined with light microscopy to identify the Plasmodium falciparum species in the ring stage of development. Methods: The system uses image processing and artificial intelligence techniques as well as a known face detection algorithm to identify Plasmodium parasites. The algorithm is based on integral image and haar-like features concepts, and makes use of weak classifiers with adaptive boosting learning. The search scope of the learning algorithm is reduced in the preprocessing step by removing the background around blood cells. Results: As a proof of concept experiment, the tool was used on 555 malaria-positive and 777 malaria-negative previously-made slides. The accuracy of the system was, on average, 91%, meaning that for every 100 parasite-infected samples, 91 were identified correctly. Conclusions: Accessibility barriers of low-resource countries can be addressed with low-cost diagnostic tools. Our system, developed for mobile devices (mobile phones and tablets), addresses this by enabling access to health centers in remote communities, and importantly, not depending on extensive malaria expertise or expensive diagnostic detection equipment.Peer ReviewedPostprint (published version

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

Artificial intelligence; Malaria diagnosis; Robotized microscopeInteligencia artificial; Diagnóstico de malaria; Microscopio robotizadoIntel·ligència artificial; Diagnòstic de malària; Microscopi robotitzatIntroduction: 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.The project is funded by the Microbiology Department of Vall d’Hebron University Hospital, the Cooperation Centre of the Universitat Politècnica de Catalunya (CCD-UPC), and the Probitas Foundation

Community-based actions in consulates: a new paradigm for opportunities for systematic integration in Chagas disease detection

Chagas disease; Health community-based strategy; In-situ screeningMalaltia de Chagas; Estratègia de salut comunitària; Cribratge in situEnfermedad de Chagas; Estrategia de salud comunitaria; Cribrado in situResearch has shown that multidimensional approaches to Chagas disease (CD), integrating its biomedical and psycho-socio-cultural components, are successful in enhancing early access to diagnosis, treatment and sustainable follow-up. For the first time, a consulate was selected for a community-based CD detection campaign. Two different strategies were designed, implemented and compared between 2021 and 2022 at the Consulate General of Bolivia and a reference health facility in Barcelona open to all Bolivians in Catalonia. Strategy 1 consisted in CD awareness-raising activities before referring those interested to the reference facility for infectious disease screening. Strategy 2 offered additional in-situ serological CD screening. Most of the 307 participants were Bolivian women residents in Barcelona. In strategy 1, 73 people (35.8% of those who were offered the test) were screened and 19.2% of them were diagnosed with CD. Additionally, 53,4% completed their vaccination schedules and 28.8% were treated for other parasitic infections (strongyloidiasis, giardiasis, eosinophilia, syphilis). In strategy 2, 103 people were screened in-situ (100% of those who were offered the test) and 13.5% received a CD diagnosis. 21,4% completed their vaccination schedule at the reference health facility and 2,9% were referred for iron deficiency anemia, strongyloidiasis or chronic hepatitis C. The fact that the screening took place in an official workplace of representatives of their own country, together with the presence of community-based participants fueled trust and increased CD understanding. Each of the strategies assessed had different benefits. Opportunities for systematic integration for CD based on community action in consulates may enhance early access to diagnosis, care and disease prevention

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

Deep learning; Malaria diagnosis; Microscopic examinationAprenentatge profund; Diagnòstic de malària; Examen microscòpicAprendizaje profundo; Diagnóstico de malaria; Examen microscópicoMalaria 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.The project is funded by the Microbiology Department of Vall d’Hebron Universitary Hospital, the Cooperation Centre of the Universitat Politècnica de Catalunya (CCD-UPC) and the Probitas Foundation

Epidemiology of imported malaria among children and young adults in Barcelona (1990-2008)

<p>Abstract</p> <p>Background</p> <p>Increasing international travel and migration is producing changes in trends in infectious diseases, especially in children from many European cities. The objective of this study was to describe the epidemiology and determine the trends of imported malaria in patients under 20 years old in the city of Barcelona, Spain, during an 18-year period.</p> <p>Methods</p> <p>The study included malaria cases that were laboratory confirmed and reported to the malaria register at the Public Health Agency of Barcelona from 1990 to 2008, residing in Barcelona and less than 20 years old. Patients were classified as natives (born in Spain) or immigrants. Differences in the distribution of demographic, clinical characteristics, and incidence per 100,000 person-year evolution were analysed. Natives and immigrants were compared by logistic regression by calculating the <it>odds ratio </it>(OR) with a 95% confidence interval (CI) and Chi-square for a linear trend (p < 0.05).</p> <p>Results</p> <p>Of the total 174 cases, 143 (82.1%) were immigrants, 100 (57.5%) were female, 121 (69.5%) <it>Plasmodium falciparum</it>, and 108 (62.1%) were visiting friends and relatives (VFR) as the reason for travel. Among the immigrants, 99 (67.8%) were from Equatorial Guinea. Immigrant cases more frequently travelled to Africa than natives (p = 0.02). The factors associated with imported malaria among immigrant residents was travelling for VFR (OR: 6.2 CI 1.9-20.2) and age 15-19 (OR: 3.7 CI 1-13.3). The incidence increased from 1990 to 1999 (p < 0.001) and decreased from 2000 to 2008 (p = 0.01), although the global linear trend was not statistically significant (p = 0.41). The fatality rate was 0.5%.</p> <p>Conclusions</p> <p>The majority of cases of malaria in population less than 20 years in Barcelona were immigrants, travelling to Africa for VFR and <it>Plasmodium falciparum </it>was most frequently detected. The trend analysis of the entire study period did not show a statistically significant decline. It is recommended to be aware of malaria, especially among children of immigrants who travel to their parent's home country for VFR. Better access to pre travel advice should be provided.</p

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

Chagas disease; COVID-19; BoliviaEnfermedad de Chagas; COVID-19; BoliviaMalaltia de Chagas; COVID-19; BolíviaBackground 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.This intervention was partially funded by the NGO Fundación Mundo Sano-España, financing part of the promotional material of the community intervention. The design of the study and the collection, the data analysis and its interpretation has not been funded

Unexpected Loa loa Finding in an Asymptomatic Patient From The Gambia: A Case Report

Gambia; Epidemiology; LoiasisGambia; Epidemiología; LoiasisGàmbia; Epidemiologia; LoiasiA 17-year-old asymptomatic male from The Gambia presented for a routine health examination after migration to Spain. Laboratory diagnosis confirmed the presence of Loa loa microfilariae. This unusual finding emphasizes the importance of screening in newly arrived migrants and the need of an extended anamnesis including migratory route and previous travels

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

Evaluation of Two Different Strategies for Schistosomiasis Screening in High-Risk Groups in a Non-Endemic Setting

Diagnosis; Non-endemic; SchistosomiasisDiagnóstico; No endémico; EsquistosomiasisDiagnòstic; No endèmic; EsquistosomiasiA consensus on the recommended screening algorithms for schistosomiasis in asymptomatic high-risk subjects in non-endemic areas is lacking. The objective of this study was to evaluate the real-life performance of direct microscopy and ELISA serology for schistosomiasis screening in a high-risk population in a non-endemic setting. A retrospective cohort study was conducted in two out-patient Tropical Medicine units in Barcelona (Spain) from 2014 to 2017. Asymptomatic adults arriving from the Sub-Saharan region were included. Schistosomiasis screening was conducted according to clinical practice following a different strategy in each setting: (A) feces and urine direct examination plus S. mansoni serology if non-explained eosinophilia was present and (B) S. mansoni serology plus uroparasitological examination as the second step in case of a positive serology. Demographic, clinical and laboratory features were collected. Schistosomiasis cases, clinical management and a 24 month follow-up were recorded for each group. Four-hundred forty individuals were included. The patients were mainly from West African countries. Fifty schistosomiasis cases were detected (11.5% group A vs. 4 % group B, p = 0.733). When both microscopic and serological techniques were performed, discordant results were recorded in 18.4% (16/88). Schistosomiasis cases were younger (p < 0.001) and presented eosinophilia and elevated IgE (p < 0.001) more frequently. Schistosomiasis is a frequent diagnosis among high-risk populations. Serology achieves a similar performance to direct diagnosis for the screening of schistosomiasis in a high-risk population

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

IntroductionMalaria 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.MethodsIn 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.ResultsComparative 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.ConclusionThe 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