Management of paediatric soil-transmitted helminthiasis in a non-endemic area: experience in a reference international health unit.

Soil-transmitted helminth (STH) infections inflict disability worldwide, especially in the poorest communities. Current therapeutic options against STHs show limited efficacy, particularly against Trichuris trichiura. The empirical management of patients coming from high-prevalence areas has been suggested for non-endemic areas. This study aimed to describe the management of STH infections in a non-endemic setting using an individualised approach. We performed a retrospective, descriptive study of all patients up to 16 years of age with STH infections attended at an international health unit in a non-endemic area (2014-2018), including all T. trichiura, Necator americanus, Ancylostoma duodenale, and Ascaris lumbricoides infections diagnosed using a formol-ether concentration technique and direct visualisation. Patients were treated according to current international guidelines. Sixty-one stool samples from 48 patients testing positive for STHs were collected, with 96% (46/48) reporting a previous long-term stay in endemic areas. Cure rates with 3-day benzimidazole regimens were 72% for T. trichiura, 40% for hookworms, and 83% for A. lumbricoides. The results were not influenced by any reinfection risk due to the study being performed in a non-endemic area. Patients coming from STH-endemic areas should be evaluated with appropriate diagnostic tools and followed up until cure control results. Cure rates in our cohort were moderate to low, similar to those published in studies in endemic areas. The efficacy of current treatment options is insufficient to recommend a specific empirical approach in high-income countries' healthcare systems.

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.

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

A 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.