The gold standard diagnosis for malaria is the microscopic visualization of blood smears to identify Plasmodium parasites, although it is an expert-dependent technique and could trigger diagnostic errors. Artificial intelligence (AI) tools based on digital image analysis were postulated as a suitable supportive alternative for automated malaria diagnosis. A diagnostic evaluation of the iMAGING AI-based system was conducted in the reference laboratory of the International Health Unit Drassanes-Vall d’Hebron in Barcelona, Spain. iMAGING is an automated device for the diagnosis of malaria by using artificial intelligence image analysis tools and a robotized microscope. A total of 54 Giemsa-stained thick blood smear samples from travelers and migrants coming from endemic areas were employed and analyzed to determine the presence/absence of Plasmodium parasites. AI diagnostic results were compared with expert light microscopy gold standard method results. The AI system shows 81.25% sensitivity and 92.11% specificity when compared with the conventional light microscopy gold standard method. Overall, 48/54 (88.89%) samples were correctly identified [13/16 (81.25%) as positives and 35/38 (92.11%) as negatives]. The mean time of the AI system to determine a positive malaria diagnosis was 3 min and 48 s, with an average of 7.38 FoV analyzed per sample. Statistical analyses showed the Kappa Index = 0.721, demonstrating a satisfactory correlation between the gold standard diagnostic method and iMAGING results. The AI system demonstrated reliable results for malaria diagnosis in a reference laboratory in Barcelona. Validation in malaria-endemic regions will be the next step to evaluate its potential in resource-poor settings.

In a clinical context, conventional optical microscopy is commonly used for the visualization of biological samples for diagnosis. However, the availability of molecular techniques and rapid diagnostic tests are reducing the use of conventional microscopy, and consequently the number of experienced professionals starts to decrease. Moreover, the continuous visualization during long periods of time through an optical microscope could affect the final diagnosis results due to induced human errors and fatigue. Therefore, microscopy automation is a challenge to be achieved and address this problem. The aim of the study is to develop a low-cost automated system for the visualization of microbiological/parasitological samples by using a conventional optical microscope, and specially designed for its implementation in resource-poor settings laboratories. A 3D-prototype to automate the majority of conventional optical microscopes was designed. Pieces were built with 3D-printing technology and polylactic acid biodegradable material with Tinkercad/Ultimaker Cura 5.1 slicing softwares. The system’s components were divided into three subgroups: microscope stage pieces, storage/autofocus-pieces, and smartphone pieces. The prototype is based on servo motors, controlled by Arduino open-source electronic platform, to emulate the X-Y and auto-focus (Z) movements of the microscope. An average time of 27.00 ± 2.58 seconds is required to auto-focus a single FoV. Auto-focus evaluation demonstrates a mean average maximum Laplacian value of 11.83 with tested images. The whole automation process is controlled by a smartphone device, which is responsible for acquiring images for further diagnosis via convolutional neural networks. The prototype is specially designed for resource-poor settings, where microscopy diagnosis is still a routine process. The coalescence between convolutional neural network predictive models and the automation of the movements of a conventional optical microscope confer the system a wide range of image-based diagnosis applications. The accessibility of the system could help improve diagnostics and provide new tools to laboratories worldwide.

This study describes a high prevalence of malaria in 2894 asymptomatic migrants from sub-Saharan Africa over an 8-year period using qPCR as a screening technique. The use of nucleic acid amplification tests constitutes a good alternative for malaria diagnosis in this moving population.

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.

Coproparasitological stool analysis based on microscopic examination is the reference diagnostic technique routinely performed in clinical microbiology laboratories. Automated image analysis systems could offer a suitable solution, reducing technologist workload and improving screening efficiency. The Human Fecal Ova & Parasite (O&P) Detection Solution (Techcyte) is an artificial intelligence (AI) software employing imaging-based algorithms to provide screening presumptive diagnostic results. This study aimed to validate the Wet Mount Iodine Solution software for detecting and diagnosing protozoan and helminthic infections in stool specimens. A total of 178 clinical stool specimens were retrospectively analyzed between July and October 2024 for comparative evaluation of the AI-based system. Positive specimens with confirmed mono ( n = 116) and mixed ( n = 31) protozoa and helminth infections, as well as negative specimens ( n = 31), were included. Conventional microscopy results were compared with AI presumptive detection and identification outcomes. In addition, operator-reviewed AI results were evaluated against the reference diagnosis. The system achieved 96.62% positive percent agreement (PPA) and 93.33% negative percent agreement (NPA) for overall detection considering operator-reviewed interpretation. The kappa coefficient was 0.865, indicating strong agreement with conventional microscopy. The AI system detected additional parasites in 74/178 (41.57%) specimens, providing a diagnostic improvement compared to traditional microscopy. AI-assisted microscopy enhances intestinal parasite diagnosis, providing reliable results when combined with operator review. Despite limitations such as specimen low volume analysis and artifact detection, the system shows strong potential for improving diagnostic workflows in clinical microbiology laboratories. IMPORTANCE: Microscopic examination of stool specimens remains the reference technique for diagnosing intestinal parasitic infections although it is labor-intensive and operator-dependent methodology. The implementation of artificial intelligence-assisted microscopy offers a promising approach to streamline diagnostic workflows, improve consistency, and enhance traditional methods. Our evaluation study provides valuable clinical evaluation data of the Human Fecal Ova & Parasite (O&P) Detection Wet Mount Iodine Solution (Techcyte), demonstrating high sensitivity, specificity, and agreement with conventional microscopy. Importantly, AI-assisted review yielded diagnostic gains in a substantial proportion of specimens, underscoring its value as a screening and complementary tool for routine parasitological diagnostics. These findings highlight the potential of AI-powered image analysis to improve the accuracy and efficiency of coproparasitological testing, thereby supporting clinical decision-making and optimizing resource utilization in microbiology laboratories.